Determination of Pb and Mg accumulation in some of the landscape plants in shrub forms

Evaluation of succulent plants Echeveria elegans as a biomonitor of heavy metals and radionuclides

This work evaluates the use of Echeveria elegans as a biomonitor of metals and radionuclides, using semi-urban soils as a study area. The study area is exposed to various trace elements of concern for various social groups in nearby localities. The quantification of metals and radionuclides was performed by X-ray fluorescence spectrometry and gamma spectrometry, respectively. Cumulative frequency distribution curves, descriptive statistics, and multivariate analysis were used to estimate the local geochemical baseline and identify geochemical and anthropogenic patterns of metals and radionuclides from topsoil and E. elegans. The evaluation of contaminants and the contribution of possible exposure routes (topsoil and atmospheric deposition) was performed with the enrichment factor (EF) and the relative concentration factor (CFR). The results suggest that the plant does not present significant physical stress due to the environmental conditions to which it was exposed. Likewise, it can bioaccumulate heavy metals from natural and anthropogenic sources. The quantification of radionuclides in the plant is below the detection limits, indicating a low bioavailability and transfer factor. The CFR and EF results showed that the plant accumulates metals from the topsoil and atmospheric deposition. The bioaccumulation mechanism would be related to the functioning of Crassulaceae Acid Metabolism (CAM). In topsoil, the organic acids of the plant would modify the solubility of the metals present in an insoluble form in the soil, acting as ligands and, subsequently, following the transport route of these metabolites. In atmospheric deposition, the metals deposited in the leaves would be incorporated into the plant through the opening of the stomata because of the capture of CO2 (at night, day, or during environmental stress) by the CAM. Overall, the evidence showed that the succulent can be used as a biomonitor of heavy metals. However, additional studies are required to determine its usefulness as a radionuclide biomonitor.

Assessment of metals (Ni, Ba) deposition in plant types and their organs at Mersin City, Türkiye

The increase in heavy metal concentrations in the air, especially after the Industrial Revolution, is notable for the scientific world because of the adverse effects that threaten environmental and human health. Among the trace elements, nickel (Ni) is carcinogenic, and all barium (Ba) compounds are toxic. Trace elements are critical for human and environmental health. Their threat further increases, especially in the urban areas and surroundings with a high population. In urban areas, the trace element contamination in the airborne can be reduced using plants. However, which plant and plant organs absorb trace elements could not be determined. In the present study, Ni and Ba concentrations in the branch, wood, and leaf samples of 14 species collected from the city center of Mersin province were determined. As a result, broad-leaved species' Ni and Ba concentrations in their leaf sample were generally higher than other species. Almost all species had the lowest Ni and Ba concentrations in their wood samples. Among these 14 species, it was found that Ni concentration was very high, especially in non-washed leaves of Platanus orientalis, Photinia serrulata, and Citrus reticulate, and Ba concentration was very high in Citrus reticulata, Chamaecyparis lawsoniana, Laurus nobilis, and Acer hyrcanum. Using broad-leaved species in urban areas where pollution is at high levels will significantly contribute to reducing Ni and Ba pollution. It is recommended that these points be considered in future urban landscaping projects.

Effects of oral exposure to brake wear particulate matter on the springtail Orthonychiurus folsomi

Most of the heavy metals in urban environments derives from road traffic, particularly from tyres and brake wear (non-exhaust emission sources). These pollutants contaminate the soil, where several organisms have a primary ecosystem role (e.g., springtails, ants, earthworms). Springtails (Collembola) are soil-dwelling animals regulating soil fertility, flow of energy through above- and below-ground food webs, and they contribute to soil microbial community dispersion and biodiversity maintenance. In this study we investigated the ecotoxicological effects of oral exposure to particles emitted from brake pads and cast-iron brake discs in the euedaphic collembola species Orthonychiurus folsomi under laboratory conditions. Our results showed that chronic exposure to brake wear particles can have sub-lethal effects both at low and high concentrations and it can cause histological alterations. Here, SEM-EDX was applied to observe the particulate and we found its chemical markers in the gut and faeces of collembola, while histological analysis detected alterations of the digestive and reproductive systems and of the abdominal fat body at high concentrations.

Accumulation of antimony and lead in leaves and needles of trees: The role of traffic emissions

Antimony (Sb) is a toxic metalloid, which has been increasingly used in the brake lining of vehicles, and increased concentrations have been found in soils near abundant traffic. However, since very few investigations of Sb accumulation in urban vegetation have been undertaken there exists a knowledge gap. We studied the concentrations of Sb in leaves and needles of trees in the Gothenburg City area, Sweden. In addition, lead (Pb), also associated with traffic, was investigated. Sb and Pb concentrations of Quercus palustris leaves at seven sites with contrasting traffic intensity varied substantially, correlated with the traffic-related PAH (polycyclic aromatic hydrocarbon) air pollution at the sites and increased during the growing season. Sb but not Pb concentrations were significantly higher in needles of Picea abies and Pinus sylvestris near major roads compared to sites at larger distances. In Pinus nigra needles at two urban streets both Sb and Pb were higher compared to an urban nature park environment, emphasising the role of traffic emissions for these elements. A continued accumulation of Sb and Pb in three years old needles of Pinus nigra, two years old needles of Pinus sylvestris and eleven years old needles of Picea abies was observed. Our data suggest a pronounced link between traffic pollution and Sb accumulation in leaves and needles, where the particles carrying Sb seem not to be transported very far from the source. We also conclude that there exists a strong potential for Sb and Pb bioaccumulation over time in leaves and needles. Implications of these findings are that increased concentrations of toxic Sb and Pb are likely to prevail in environments with high traffic intensity and that Sb can enter the ecological food chain by accumulation in leaves and needles, which is important for the biogeochemical cycling.

The change in biocomfort zones in the area of Muğla province in near future due to the global climate change scenarios

It is inevitable that the global climate change, which has important effects on the climate throughout the world, would have significant effects on the biocomfort zones. Hence, how global climate change will change the biocomfort zones should be determined and the data to be obtained should be used in urban planning projects. In the current study, SSPs 245 and SSPs 585 scenarios were taken as a basis, and the potential effects of global climate change on the biocomfort zones in Muğla province, Türkiye were investigated. Within the scope of the present study, the current status of biocomfort zones in Muğla and their possible conditions in years 2040, 2060, 2080, and 2100 were compared using DI and ETv methods. At the end of the study, it was estimated that, according DI method, 14.13% of Muğla province are in cold zone, 31.96% in cool zone, and 53.71% in comfortable zone. According to the SSPs 585 scenario, together with the increase in temperature, the cold and cool zones will totally disappear in year 2100, comfortable zones will decrease to 31.22%, and approx. 68.78% of the province will be in hot zone. According to the calculations made using ETv method, Muğla province currently consists of moderately cold zones by 2%, quite cold zones by 13.16%, slightly cold zones by 57.06%, and mild zones by 27.79%. Based on the SSPs 585 scenario for the year 2100, it is projected that Muğla will consist of slightly cool zones by 1.41%, mild zones by 14.42%, and comfortable zones by 68.06%, besides warm zones by 16.11% which are not present at this moment. This finding suggests that especially the cooling costs will increase and the air-conditioning systems to be used will negatively affect the global climate change through the energy consumption and the gases used.

Convolutional graph neural networks-based research on estimating heavy metal concentrations in a soil-rice system

Estimating heavy metal concentrations in soil-rice systems is of great significance to identify the factors controlling heavy metal transfer in soil-crop ecosystems. Recent research utilizes the advantage of convolutional calculations to extract and learn complicated information from 17 environmental covariates in rice and achieve promising results. However, as the complexity and interconnectivity in soil-crop ecosystem, just relying on convolutional calculations and a deep network structure is far from enough. The data processed by traditional deep learning technologies even with convolutional calculations are limited to Euclidean space; these architectures do not have the ability to extract information from the relationships in graph structures, which may contain rich information. Thus, in this paper, we try to integrate graph information into convolutional calculations for heavy metal prediction and propose a model named ConvGNN-HM. ConvGNN-HM combines the advantages of graph learning and convolutional calculations to predict heavy metal concentrations in a soil-rice system with analysis of 17 environmental factors. For comparison, we conduct an experiment to compare ConvGNN-HM with techniques with convolutional neural networks, multilayer perceptron, back-propagation neural networks, support vector machine, random forest, Bayesian ridge regression, and multiple linear regression. The experimental results illustrate that ConvGNN-HM got the best prediction values; the R² values of ConvGNN-HM for cadmium (Cd), plumbum (Pb), chromium (Cr), arsenic (As), and hydrargyrum (Hg) in rice were 0.84, 0.75, 0.79, 0.49, and 0.83, respectively, and the MAE values were also acceptable. We further conduct sensitivity analysis to demonstrate the stability and robustness of ConvGNN-HM. This study demonstrates the usefulness of combining graph learning and convolutional calculations in the prediction of heavy metal concentrations and provides a new perspective to build multidimensional and multi-scale complex ecosystem models.

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM₁₀ and PM_2.5). However, the effect of the lockdown is most prominent on PM_2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM_2.5 and PM₁₀ concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM_2.5 and PM₁₀ during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

The wild plant Gnaphalium lavandulifolium as a sentinel for biomonitoring the effects of environmental heavy metals in the metropolitan area of México Valley

Biomonitoring is a valuable tool for assessing the presence and effects of air pollutants such as heavy metals (HM); due to their toxicity and stability, these compounds can affect human health and the balance of ecosystems. To assess its potential as a sentinel organism of HM pollution, the wild plant Gnaphalium lavandulifolium was exposed to four sites in the metropolitan area of México Valley (MAMV): Altzomoni (ALT) Coyoacán (COY), Ecatepec (ECA), and Tlalnepantla (TLA) during 2, 4, and 8 weeks, between October and November 2019. Control plants remained under controlled conditions. The chemical analysis determined twelve HM (Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn) in the leaves. Macroscopic damage to the leaves, later determined in semi-thin sections under light microscopy, lead to a finer analysis. Transmission electron microscope (TEM) showed major structural changes: chromatin condensation, protoplast shrinkage, cytoplasm vacuolization, cell wall thinning, decreased number and size of starch grains, and plastoglobules in chloroplasts. All these characteristics of stress-induced programed cell death (sPCD) were related to the significant increase of toxic HM in the leaves of the exposed plants compared to the control (p < 0.05). Immunohistochemistry revealed a significant amount of proteases with caspase 3-like activity in ECA and TLA samples during long exposure times. Ultrastructural changes and sPCD features detected confirmed the usefulness of G. lavandulifolium as a good biomonitor of HM contamination. They supported the possibility of considering subcellular changes as markers of abiotic stress conditions in plants.

A field study on the composition, structure, and function of endophytic bacterial community of Robinia pseudoacacia at a composite heavy metals tailing

Robinia pseudoacacia (R. pseudoacacia) is a well reported plant species for heavy metal phytoremediation, and it was capable to improve Cd uptake efficiency after inoculated with plant growth promoting endophytes. However, the knowledge on R. pseudoacacia associated endophytes in field condition and the relationship between these microbial communities and heavy metal uptake capacities are still scarce. In this study, the characteristics of heavy metal bioaccumulation and translocation in R. pseudoacacia, and the structure and function of its endophytic bacterial communities were revealed. The results showed that heavy metal pollution made microbes more sensitive to the environment as the diversity (Shannon) of endophyte community decreased but the abundance (Chao) increased. Redundancy analysis (RDA) also showed that heavy metals were the key factor affecting the composition of endophyte. In the co-occurrence network, 27 keystone taxa mainly from Actinobacteria, Proteobacteria and Firmicutes occupied the dominant niches, among which 16 OTUs mainly from lactobacillus, bacteroides, staphylococcus, methylorubrum and bifidobacterium were positively related to bioaccumulation and translocation of Cd, Cu, Pb and Zn. Besides, heavy metal stress enhanced the functional adaptability of endophytic bacteria community. Related predicted genes were enriched in immune response, physiological metabolism pathway and stress-resistant enzyme synthesis. This study showed that heavy metal stress enhanced the structural and functional adaptability of endophyte community and keystone taxa played significant role in improving the efficiency of phytoremediation.

Bioremediation of hazardous pollutants from agricultural soils: A sustainable approach for waste management towards urban sustainability

Soil contamination is perhaps the most hazardous issue all over the world; these emerging pollutants ought to be treated to confirm the safety of our living environment. Fast industrialization and anthropogenic exercises have resulted in different ecological contamination and caused serious dangerous health effects to humans and animals. Agro wastes are exceptionally directed because of their high biodegradability. Effluents from the agro-industry are a possibly high environmental risk that requires suitable, low-cost, and extensive treatment. Soil treatment using a bioremediation method is considered an eco-accommodating and reasonable strategy for removing toxic pollutants from agricultural fields. The present review was led to survey bioremediation treatability of agro soil by microbes, decide functional consequences for microbial performance and assess potential systems to diminish over potentials. The presence of hazardous pollutants in agricultural soil and sources, and toxic health effects on humans has been addressed in this review. The present review emphasizes an outline of bioremediation for the effective removal of toxic contaminants in the agro field. In addition, factors influencing recent advancements in the bioremediation process have been discussed. The review further highlights the roles and mechanisms of micro-organisms in the bioremediation of agricultural fields.

Estimation of heavy metal concentrations (Cd and Pb) in plant leaves using optimal spectral indicators and artificial neural networks

The necessity of continuously monitoring the agricultural products in terms of their health has enforced the development of rapid, low-cost, and non-destructive monitoring solutions. Heavy metal contamination of the plants is known as a source of health threats that are made by their proximities with pollutant soil, water, and air. In this paper, a method was proposed to measure lead (Pb) and cadmium (Cd) contamination of plant leaves through field spectrometry as a low-cost solution for continuous monitoring. The study area was Mahneshan county of Zanjan province in Iran with rich heavy metal mines that have more potential for plant contamination. At first, we collected different plant samples throughout the study area and measured the Pb and Cd concentrations using ICP-AES, in which we observed that the concentrations of Pb and Cd are in the range of 1.4 ~ 282.6 and 0.3 ~ 66.7 μgg-1, respectively, and then we tried to find the optimum estimator model through a multi-objective version of genetic algorithm (GA) optimization that finds simultaneously the structure of an artificial neural network and its input features. The features extracted from the raw spectrums have been collimated to be compatible with the Sentinel-2 multispectral bands for the possibility of further developments. The results demonstrate the efficiency of the optimum estimator model in estimation of the leaves' Pb and Cd contamination, irrespective of the plant type, which has reached the R2 of 0.99 and 0.85 for Pb and Cd, respectively. Additionally, the results suggested that the 783-, 842-, and 865-nm spectral bands, which are similar to the 7, 8, and 8a sentinel-2 spectral bands, are more efficient for this purpose.

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

Kolkata has a reputation for being one of the world's most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to "very poor" (301 ≤ AQI ≤ 400) and "poor" (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM_2.5 and PM₁₀ that cause the AQI of Kolkata to deteriorate to "very poor" and "poor." For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM₁₀ and PM_2.5. Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.

Convolutional neural network-based applied research on the enrichment of heavy metals in the soil-rice system in China

The enrichment of heavy metals in the soil-rice system is affected by various factors, which hampers the prediction of heavy metal concentrations. In this research, a prediction model (CNN-HM) of heavy metal concentrations in rice was constructed based on convolutional neural network (CNN) technology and 17 environmental factors. For comparison, other machine learning models, such as multiple linear regression, Bayesian ridge regression, support vector machine, and backpropagation neural networks, were applied. Furthermore, the LH-OAT method was used to evaluate the sensitivity of CNN-HM to each environmental factor. The results showed that the R² values of CNN-HM for Cd, Pb, Cr, As, and Hg were 0.818, 0.709, 0.688, 0.462, and 0.816, respectively, and both the MAE and RMAE values were acceptable. The sensitivity analysis showed that the concentrations of Cd and Pb, mechanical composition, soil pH, and altitude were the main sensitive features for CNN-HM. Compared with CNN-HM based on all input features, the performance of the quick prediction model that was based on the sensitive features did not degrade significantly, thereby indicating that CNN-HM has stronger stability and robustness. The quick prediction model has extensive application value for timely prediction of the enrichment of heavy metals in emergencies. This study demonstrated the effectiveness and practicability of CNNs in predicting heavy metal enrichment in the soil-rice system and provided a new perspective and solution for heavy metal prediction.

GIS-based multicriteria decision analysis for settlement areas: a case study in Canik

In addition to global population growth due to migration from rural areas to urban areas, population density is constantly increasing in certain regions, thereby necessitating the introduction of new settlements in these regions. However, in the selection of settlement areas, no sufficient preliminary examinations have been conducted; consequently, various natural disasters may cause significant life and property losses. Herein, the most suitable settlement areas were determined using GIS (geographic information systems) in Canik District, where the population is continuously increasing. Therefore, this study aimed to incorporate a new perspective into studies on this subject. Within the scope of the study, landslide and flood risks, which are among the most important natural disasters in the region, were primarily evaluated, and high-risk areas were determined. Elevation, slope, aspect, curvature, lithology, topographic humidity index (TWI), and proximity to river parameters were used to produce flood susceptibility maps. A digital elevation model (DEM) of the study area was produced using contours on the 1/25,000 scaled topographic map. The elevation, slope, aspect, curvature, and TWI parameters were produced from the DEM using the relevant analysis routines of ArcGIS software. The raster map of each parameter was divided into 5 subclasses using the natural breaks classification method. In the reclassified raster maps, the most flood-sensitive or flood-prone subclasses were assigned a value of 5, and the least sensitive subclasses were assigned a value of 1. Then, the reclassified maps of the 7 parameters were collected using the "map algebra" function of ArcGIS 10.5 software, and the flood susceptibility index (FSI) map of the study area was obtained. The flood susceptibility map of the study area was obtained by dividing the FSI into 5 subclasses (very low, low, moderate, high, and very high) according to the natural breaks classification method. Thereafter, suitable and unsuitable areas in terms of biocomfort, which affects people's health, peace, comfort, and psychology and is significant in terms of energy efficiency, were determined. At the last stage of the study, the most suitable settlement areas that were suitable in terms of both biocomfort and low levels of landslide and flood risks were determined. The calculated proportion of such areas to the total study area was only 2.1%. Therefore, because these areas were insufficient for the establishment of new settlements, areas that had low landslide and flood risks but were unsuitable for biocomfort were secondarily determined; the ratio of these areas was calculated as 56.8%. The remaining areas were inconvenient for the establishment of settlements due to the risk of landslides and floods; the ratio of these areas was calculated as 41.1%. This study is exemplary in that the priority for the selection of settlement areas was specified, and this method can be applied for selecting new settlements for each region considering different criteria. Due to the risk of landslides or flooding in the study area, the areas unsuitable for establishing a settlement covered approximately 41.1% of the total study area. The areas that had low flood and landslide risks but were suitable for biocomfort constituted only 2.1% of the study area. In approximately 56.8% of the study area, the risk of landslides or floods was low, and these areas were unsuitable in terms of biocomfort. Therefore, these areas were secondarily preferred as settlement areas. The most suitable areas for settlements constituted only 0.19% of the total study area, and these areas will not be able to meet the increasing demand for settlement area. Therefore, it is recommended to select areas that do not have the risk of landslides and floods but are unsuitable for biocomfort. This study reveals that grading should be performed in the selection of settlement areas. When choosing a settlement area in any region, possible natural disasters in the region should be identified first, and these disasters should be ordered in terms of their threat potential. Moreover, biocomfort areas suitable for settlements should be considered. In the next stages of settlement area selection, the criteria that affect the peace and comfort of people, such as distance to pollution sources, distance to noise sources, and proximity to natural areas, should also be evaluated. Thus, a priority order should be created for the selection of settlement areas using various other criteria.

Photosynthetic bacteria with iron oxide nanoparticles as catalyst for cooking oil removal and valuable products recovery with heavy metal co-contamination

Waste cooking oil discharge causes environmental pollution in receiving waters, particularly when associated with heavy metals that can lead to formation of hazardous organometallic compounds. This study combined iron oxide nanomaterial and the anoxygenic photosynthetic bacterium Rhodopseudomonas faecalis PA2 for removal of cooking oil in the presence of heavy metals. R. faecalis PA2, with known capability to generate beneficial substances from several wastes, was capable of cooking oil removal with production of valuable products. Oil removal, biomass, protein, and carotenoid production were 82.38%, 1.48 g/L, 1,600.19 mg/L, and 1,046.33 mg/L, respectively, under optimal conditions (cooking oil as carbon source and 30% inoculum density). Iron (Fe) stimulates growth of R. faecalis; in this study, Fe₃O₄ nanoparticles were synthesized and used as a catalyst to facilitate interaction and high reactivity between Fe and R. faecalis PA2. Size measurement by transmission electron microscopy (17.44 nm), X-ray diffraction peaks, and magnetic susceptibility confirmed that the synthesized nanoparticles were magnetite Fe₃O₄. Biomass, protein, and carotenoid production of the Fe₃O₄ supplemented experiment increased by 61.56%, 70.78%, and 57.2%, respectively, when compared with the control. When different concentrations of heavy metals (Pb, Ni, Co, and Zn) were supplemented in the media containing cooking oil, Fe₃O₄ addition increased heavy metal tolerance, improved bacterial growth, and enhanced valuable products when compared with the non-supplemented group. This study reports the positive impact of nanoparticle application as a catalyst for valorization of cooking oil waste with heavy metal co-contamination by the photosynthetic bacterium R. faecalis PA2.

Heavy metal accumulation characteristics and physiological response of Sabina chinensis and Platycladus orientalis to atmospheric pollution

Heavy metal (Cu, Mn, Zn, Pb, and Cd) concentrations were measured in the leaves of Sabina chinensis and Platycladus orientalis collected from urban, suburban, and rural sites in Tianjin, China. Photosynthetic pigment contents, reactive oxygen species content, malondialdehyde (MDA) content and antioxidant enzyme activity were investigated, providing physiological response parameters. Our comparison of the sites revealed that urbanization significantly influenced the heavy metal concentrations in both plant leaves. At the rural site, both plant leaves exhibited the lowest heavy metal accumulation. The highest Cu, Mn, and Zn concentrations were found in S. chinensis leaves from the urban site; the highest Pb and Cd concentrations were found in P. orientalis leaves from the urban site. These results indicate that the urban site contained larger heavy metal concentrations in the plant leaves that may reflect the anthropogenic emission gradient. It is also found that S. chinensis may be used to monitor airborne heavy metal pollution because it is highly quick response to heavy metals, while P. orientalis may be used for mitigation due to its high resistance. The results of this study can contribute to the development of monitoring and environmental management plans by providing information on sensitive and resistant tree species for city greening in North China.

Cadmium stress triggers significant metabolic reprogramming in Enterococcus faecium CX 2-6

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A cadmium resistant strain of Enterococcus faecium CX 2–6 is sequenced.

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Differential expression analysis found 47% of CX 2–6 genes are significantly affected by Cd treatment.

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Differentially expressed genes (DEGs) form physically linked gene clusters in the CX 2–6 genome.

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A prophage is unique to CX 2–6 and is strongly activated by high Cd concentration.

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A majority of DEGs responding to Cd treatment are present in the core genome.

Heavy metal pollutions in the soils are increasingly threatening the global crop and food production. Using plant associated bacteria to remediate heavy metal contamination is a promising approach. We have isolated a cadmium (Cd) resistant Enterococcus faecium strain CX 2–6 from a heavy metal contaminated farmland. We have shown that: (i) CX 2–6 can tolerate cadmium (Cd) with a slower growth rate; (ii) The CX 2–6 complete genome is fully assembled using PacBio long reads; (iii) Differential expression analysis found 47% of CX 2–6 genes are significantly affected by Cd treatment and form three gene groups with distinct expression profiles; (iv) Differentially expressed genes (DEGs) form physically linked gene clusters in the CX 2–6 genome, and one of the gene clusters corresponds to a prophage that is unique to CX 2–6 and is strongly activated when Cd concentration is higher; (v) A majority of DEGs responding to Cd treatment are present in the core genome; and (vi) 55 noncoding RNA genes are identified and 49 of them are DEGs responding to cadmium stress. Our pan-genome analysis and comparative RNA-seq data analysis has significantly improved our understanding of the metabolic reprogramming of E. faecium CX 2–6 under Cd stress.

Effects of acid rain on plant growth: A meta-analysis

Anthropogenic driven acid gases emission has caused acid rain in many regions globally. Although efforts have been made to assess the effects of acid rain on terrestrial ecosystems, a systematic assessment of growth-related traits across plant aboveground and belowground is lacking. Hence, we performed a phylogenetically controlled meta-analysis of 755 observations from 69 independent studies to quantify the effects of acid rain on six growth-related traits of plant. We estimated the inhibitory effects of acid rain on plant growth in general and found that aboveground and belowground plant parts responded differently. The acidity of acid rain and acid rain interval had direct modulation effects on plant growth. We also found that there were interactions between acid rain pH and other acid rain characteristics (i.e., acid rain interval, mole ratio of S:N, and acid rain rate) and experimental characteristics (i.e., initial soil pH and plant exposure part), indicating that there were pH-dependent interaction patterns. Thus, an effective approach to evaluate and predict the effects of acid rain on plant growth is to fully consider the direct effects of acid rain pH and the interactions between acid rain pH and other factors.

Periodical and regional change of particulate matter and CO2 concentration in Misurata

Today, environmental pollution, air pollution in particular, is among the most important problems in the world. Air pollution, which has become a global issue, is estimated to cause the death of around 1 in 8 people worldwide. Due to the significance of air pollution, all components of air pollution are of great importance for human health, and thus the studies on air pollution are highly important, especially in areas with high population density. In this study, it was aimed to determine the regional and periodic change of CO₂ and particulate matter pollution in the city of Misurata, one of the important cities of Libya. The study was conducted in the city centre of Misurata, and the measurements were made once in every three days between November and February at 7 different locations selected in the city centre. As a result of the measurements made, the changes in the pollution parameters were evaluated on the basis of zones and months. As a result of the study, the cleanest areas were found to be the industrial area (B3) located in the east of the city and the zone (B4), where the Al-Swehli farm was located, while the dirtiest areas were found to be the main street (B1 and B2) in the city centre and the city centre, in general. In terms of months, the lowest CO₂ values were obtained in January, and the highest value was in February, whereas the lowest particulate matter values were obtained in January and the highest values in December.

The effects of base station as an electromagnetic radiation source on flower and cone yield and germination percentage in Pinus brutia Ten

Electromagnetic radiation is a substantial pollution factor that most of the living things found almost everywhere are constantly exposed to with current technology. The number of studies conducted on the effects of this exposed radiation on the living things constantly is limited; and almost all of the studies conducted are aimed at measuring the effects of short-term exposure. In addition to this, most of the studies conducted on plants focus on herbaceous plant species. In this study, the effects of distance to base station on flower and cone yield and germination percentage were investigated in Pinus brutia individuals, one of the critical forest tree species. The study results revealed that being close to the base station significantly reduced the number of flowers and cones in P. brutia individuals, and that the values obtained in individuals at a distance of 800 m from the base station were 11 times more than the number of flowers and 7 times more than the number of cones compared to the individuals at a distance of 100 m. In the seeds subject to the study, there is a three-times difference in terms of the germination percentage among the individuals located at the furthest and closest distance to the base station. These results show that P. brutia individuals are considerably affected by the base station.

Distillery wastewater detoxification and management through phytoremediation employing Ricinus communis L

This study aimed to assess the phytoremediation potential of Ricinus communis L. for heavy metals remediation via rhizospheric bacterial activities for distillery wastewater detoxification and management. Results revealed that distillery wastewater contained high levels of metals and other physico-chemical pollution parameters that could cause environmental pollution and aquatic toxicity. The identified bacterium produced several plant growth-promoting compounds including siderophores, ligninolytic enzymes, and indole acetic acid that resulted in nutrient enhancement and improved mineralization of metals in the plants during stress conditions. The bioconcentration factor (BCF) of all the metals examined were > 1, which showed that these metals are accumulating in the root, shoot, and leaves of Ricinus communis L. Most of the metals are stablised in the roots but Pb, Cd and Zn were translocated more to the shoorts (TC>1). The ability of Ricinus communis L. to grow in metals-containing distillery wastewater and reduce heavy metals and organic contaminants suggests that it can be used to provide an effective treatment of distillery wastewater. The use of Ricinus communis L. is an eco-friendly tool for the reduction of organometallic contamination and protecting agricultural land.

The usability of Cupressus arizonica annual rings in monitoring the changes in heavy metal concentration in air

Air pollution, which has been increasing in recent years, has reached significant dimensions and has become one of the most important agenda topics of present day. Among air pollution components, heavy metals are of particular importance, since they are not easily decomposed, they tend to bioaccumulate, and some of them have toxic or carcinogenic effects even at low concentrations. Therefore, it is an extremely important subject to monitor the changes in heavy metal concentrations found in air. The most preferred method in determining the changes in heavy metal concentrations in the atmosphere is the use of biomonitors. From past to present, trees have been good biomonitors in determining the increase in heavy metal concentrations in the atmosphere. Particularly, with the help of the annual growth rings of trees, vital information can be obtained on the changes in heavy metal concentrations in air. In this study, after the annual rings were determined on the log taken from the main body of the cypress (Cupressus arizonica) tree cut from the Kisla park located in Kastamonu province, the concentrations of Bi, Cd, and Ni in the outer bark, inner bark, and wood were compared in the inward-facing and road-facing parts of these sections. Also, the changes in heavy metal concentrations in the annual rings were evaluated on a yearly basis. As a result of the study, it was found that the element concentrations in the outer bark of the road-facing part were generally at a higher level, and that the changes in the elements on a yearly basis generally followed a fluctuating course, but there had been a general increase in the Cd and Ni concentrations in recent years.

Using Cedrus atlantica's annual rings as a biomonitor in observing the changes of Ni and Co concentrations in the atmosphere

Among the environmental pollution elements, one of the most important problems of the world today, air pollution and heavy metal pollution that have gradually increased after the industrial activities, are of significant importance. Heavy metals are not easily dissolved in nature. Moreover, they tend to bioaccumulate in living organisms, and some of them have a toxic or carcinogenic effect even at low concentrations. For this reason, it is crucial to monitor the heavy metal concentration in the air and monitor the changes. One of the efficient methods in observing the change of heavy metal concentrations in the air is the use of annual rings of trees as a biomonitor. The present study aimed to determine the annual change of Ni and Co concentrations, which are among the heavy metals related to traffic volume at most, in annual rings of Cedrus atlantica harvested in Kastamonu in late 2019. Within the scope of this study, the heavy metal concentrations in the inner and outer bark were compared to the heavy metal concentration in wood and the change of concentration in terms of direction was also analyzed. As a result of the present study, the highest concentrations in both heavy metals were found in the outer bark, whereas the transmission of metals within the wood was limited. It was found that Ni concentration significantly changed directionally in woods, and this change was related to the traffic volume. Thus, it was concluded that the annual rings of Cedrus atlantica were very suitable biomonitors to monitor Ni concentration change.

Assessment of heavy metal contamination in herbal medicinal products consumed in the Iranian market

Herbal medicines have been reported to contain many contaminants that are potential harmful to health. These include heavy metals, such as lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg). Therefore, this study aimed to measure the levels of Pb, Cd, As, and Hg in several orally administered herbal products available in the Iranian market. Fifteen products labeled (A-O) of different brands from three different production batches (n = 45) were purchased from traditional herbal medicine factories in Iran. Each sample was digested with nitric acid by the wet digestion method, and the resultant solutions were used to determine the concentrations of Pb, Cd, As, and Hg. These measurements were performed using flame atomic absorption spectrometry, graphite furnace atomic absorption, or cold vapor atomic absorption. The lead, cadmium, arsenic, and mercury contents in the investigated samples did not show significant levels that may be associated with toxicity. All four metals were present at concentrations below the limits recommended by the WHO (World Health Organization), American Herbal Products Association (AHPA), and Canadian standard, but in several herbal products, the concentrations of these trace elements exceeded the Agency for Toxic Substances and Disease Registry (ATSDR). The concentrations of Pb, Cd, As, and Hg in commercially available herbal remedies were well below the acceptable intake recommended by global recommendations. Our findings revealed that at present, the amount of heavy metals in medicinal herbs processed at the level of supply by pharmacies licensed is favorable.

Assessment of metals in PM10 filters and Araucaria heterophylla needles in two areas of Quito, Ecuador

The reliability of Araucaria heterophylla needles as a biomonitor was evaluated by analyzing the concentration of metals in PM₁₀ filters and in Araucaria heterophylla needles. The sampling campaign was carried out at two sites in the city of Quito, Ecuador, in 2017–2019. Concentrations of Cr, Cu, K, Mn, Pb, Zn, Ca, Fe, Al and Mg were determined in PM₁₀ filters and in Araucaria heterophylla needles using an Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES). The annual mean concentrations of PM₁₀ ranged between 24.9 and 26.3 μg m⁻³, exceeding the limit established by the World Health Organization (20 μg m⁻³). Statistical analyses, performed for the PM₁₀ filters, showed that dust resuspension and anthropogenic activities were important sources for PM₁₀ emissions in the city. Metals related to natural emissions (Ca, Mg, K, Al and Fe) dominated in both types of samples, while the minor metals were those related to anthropogenic emissions (Zn, Cu, Cr and Pb). The former were positively associated with the needle samples, while the latter were associated with PM₁₀ filters. This work not only improved scientific knowledge on the concentrations of PM₁₀ and metals in the Andean city of Quito, but also greatly contributed to the progress of research on the use of Araucaria heterophylla needles as a biomonitor.

The effect of air pollution on immunological, antioxidative and hematological parameters, and body condition of Eurasian tree sparrows

Air pollution constitutes potential threats to wildlife and human health; therefore, it must be monitored accurately. However, little attention has been given to understanding the toxicological effects induced by air pollution and the suitability of bird species as bioindicators. The Eurasian tree sparrow (Passer montanus), a human commensal species, was used as a study model to examine toxic metal accumulation, retention of particulate matter (PM), immunological and antioxidant capacities, and hematological parameters in birds inhabiting those areas with relatively higher (Shijiazhuang city) or lower (Chengde city) levels of PM_2.5 and PM₁₀ in China. Our results showed that Shijiazhuang birds had significantly more particle retention in the lungs and toxic metal (including aluminum, arsenic, cadmium, iron, manganese, and lead) accumulation in the feathers relative to Chengde birds. They also had lower superoxide dismutase, albumin, immunoglobulin M concentrations in the lung lavage fluid, and total antioxidant capacity (T-AOC) in the lungs and hearts. Furthermore, although they had higher proportions of microcytes, hypochromia, and polychromatic erythrocytes in the peripheral blood (a symptom of anemia), both populations exhibited comparable body conditions, white cell counts, heterophil and lymphocyte ratios, and plasma T-AOC and corticosterone levels. Therefore, our results not only confirmed that Shijiazhuang birds experienced a greater burden from environmental PM and toxic metals but also identified a suite of adverse effects of environmental pollution on immunological, antioxidative, and hematological parameters in multiple tissues. These findings contribute to our understanding of the physiological health consequences induced by PM exposure in wild animals. They suggest that free-living birds inhabiting urban areas could be used as bioindicators for evaluating the adverse effects induced by environmental pollution.

Application of artificial neural networks to predict the heavy metal contamination in the Bartin River

In this study, copper (Cu), iron (Fe), zinc (Zn), manganese (Mn), nickel (Ni), and lead (Pb) analyses were performed, and the results were modelled by artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Samples were taken from 3 stations selected on the Bartin River for 1 year between December 2012 and December 2013. Radial basis neural network (RBANN), multilayer perceptron (MLP) neural networks models, and adaptive neuro-fuzzy inference system (ANFIS) were applied to the data in order to predict the heavy metal concentrations. As a result of the study, the RMSE and MAE values of all the heavy metal models were found to have very low error values during the test phase, and it was found that the models created using MLP had R² values higher than 0.77 during the test phase; the test phase R² values of the models using RBN method were found to be ranging between 0.773 and 0.989, and the test phase R² value of the ANFIS model was higher than 0.80. If sorted from the best model to the worst by taking the MAE and RMSE values into consideration based on the test evaluation results, according to the heavy metal types, where all of the MLP, RBN, and ANFIS models were generally approximate to each other, RBN was successful for Cu, Zn, and Mn, while MLP model was successful for Ni and ANFIS model for Fe and Pb. According to the results, it can be inferred that the heavy metal contents can be estimated approximately with artificial intelligence models and relatively easy-to-measure parameters; it will be possible to detect heavy metals which are harmful to the viability of the rivers, both quickly and economically.

The possibility of using Scots pine needles as biomonitor in determination of heavy metal accumulation

One of the biggest problems of modern world is the air pollution which causes the death of millions of people every year. The heavy metals included in the component of air pollution occupy an important position in human health since they could remain intact in nature for a long time and build bioaccumulation, and also some of them are toxic or carcinogenic even at some low concentrations. Even the heavy metals functioning as micronutrient element could create toxic effect for human beings at the high concentrations. For this reason, the determination of the heavy metal concentration has an important role in terms of the detection of risky regions and risky level. One of the primary sources of heavy metals is industrial plants where the heavy metal ores are processed. Those regions might have risks at high level in terms of particular heavy metals. Consequently, it is significant to find out in which level they influence the area around these plants and to what extent the heavy metal pollution is effective. In this study, Ba, Zn, Cd, K, and Na concentrations are determined by analyzing the samples which are taken from 1- and 2-year-old needles of Scots pines (Pinus sylvestris L.) growing in 1 km, 3 km, 10 km and 25 km distances around a processing and mining of magnesite ore in Russia. In the end of the study, it is concluded that generally, the concentrations of heavy metals subject to the study are increasing depending upon the distance, and this increase is quite apparent in some elements, and finally in many points, the concentrations determined in 2-year-old needles have higher levels than 1-year-old needles.

Ca, Cu, and Li in washed and unwashed specimens of needles, bark, and branches of the blue spruce (Picea pungens) in the city of Ankara

The use of certain types of plants to determine the accumulation of HMs (heavy metals) has yielded quite consistent results in the research fields. Many researches have focused on particular types of HMs due to their common presence in the air (Pb, Cd, Ni, Co, Cr to name a few). However, it is equally as important to shed light on other types of HMs and the scale of their existence in our atmosphere, hence this paper. Blue spruce (Picea pungens) tree organs were used in an experiment to calculate the recent concentration of HMs. The research concentrates on Ca, Cu, and Li elements in the washed and unwashed needles, branches, and barks, and these organs were evaluated depending on the organ age. The study results showed that the concentration of the elements subjected to the research changed depending on the organ, washing status and organ age, and that the lowest concentrations of Ca and Cu elements were obtained in the barks in general. In relation to the organ age, it was found that there was an increase in the concentration of Ca with age, and that the concentration of Li element was inversely proportional to age.

Research on recognition and protection of ecological security patterns based on circuit theory: a case study of Jinan City

Accelerated urbanization and population growth have resulted in the loss of ecological land and biodiversity, accompanied by the degradation of ecosystem services. Identifying and improving existing ecological security patterns are of great significance for maintaining the sustainable development of cities. In this study, Jinan, the capital of China's Shandong Province, was used as a case study area. Based on three ecosystem services, namely, soil conservation, water conservation and carbon fixation, ecological sources were determined. Furthermore, a resistance surface was constructed based on biodiversity. On these bases, the circuit theory concept of random walks was applied to simulate ecosystem processes in a heterogeneous landscape and identify ecological corridors, pinch points and barriers. A total of 25 ecological sources, 48 ecological corridors and 19 pinch points were identified, and restoration areas were delimited to three levels. These elements together constituted the ecological security patterns. Specifically, the ecological sources were mainly distributed in southern Jinan and were covered mostly with forest land. The ecological corridors were located mainly in the eastern and southwestern plains below the southern mountainous areas and were covered mostly with cropland. Furthermore, the eastern corridors were much longer than the southwestern corridors. Pinch points were distributed mostly along rivers or around large-scale construction land. Barriers were distributed mainly in Zhangqiu District and northern Licheng District. Based on these findings, hierarchical restoration areas were delimited. Differentiated development contradictions in restoration areas were discussed, and corresponding ecological protection measures were proposed. An ecological security optimization pattern of "one center, two wings, and two belts" was finally proposed to provide planning strategies for decision-makers.