Heavy Metal Stress Levels Research Articles

Enrichment Characteristics, Source Apportionment, and Risk Assessment of Heavy Metals in the Industrial and Mining Area of Northern Guangdong Province

In order to understand the heavy metal pollution of the industrial and mining area in northern Guangdong Province, topsoil samples (0-20 cm) from 209 sites in study area were collected, and the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were analyzed. The source and distribution characteristics of Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn in soil samples were analyzed using the geographic information system (GIS) and principal component analysis (PCA), and the geo-accumulation index and potential ecological hazard index were used to evaluate their pollution status and ecological risk. The results showed that:① Except Ni, all seven heavy metal elements exceeded the national soil background value but were below the pollution risk screening value. The soil environment as a whole was relatively clean. ② The spatial distribution of heavy metals in soil differed. The contents of As and Pb showed northwest-southeast zonal distribution. The contents of Cd, Cu, Hg, and Zn generally decreased from the middle to the surrounding, which was consistent with the industrial and mining enterprise locations. However, the spatial distribution of Cr and Ni had no direct relationship with the location of pollution sources such as industrial enterprises. ③ The eight heavy metals could be identified as three principal components (PCs). PC1 (Cd, Cu, Pb, and Zn) was mainly affected by human activities such as lead-zinc deposit dressing, traffic emissions, and agricultural production. However, PC2 (Cr and Ni) was a natural source, mainly affected by soil parent material. In addition, PC3 (As and Hg) was mainly affected by industrial activities such as non-ferrous metal smelting and thermal power generation. ④ According to the geo-accumulation index method, the risk degree of the eight heavy metals was:Cd>As>Zn>Hg>Pb>Cu>Cr=Ni. The contents of Cr and Ni in soil were at a no-risk level; the contents of As, Cu, Hg, Pb, and Zn were at a low-risk level; and the content of Cd was at a high-risk level. Most potential ecological hazards for single elements in the study area were at a mild risk level. In contrast, a small proportion of the surface soils in areas of intense industrial activity were subject to substantial levels of heavy metal stress and require further attention.

Study on Rice Phenological Differences under Heavy Metal Stress by Using Multi-source Remote Sensing Time-Series Images

Heavy metal pollution is surveyed rapidly and accurately, which is of great significance for soil pollution control. heavy metal pollution on farmland results in phenological changes, and we can use remote sensing technology to observe these changes. In the paper, Normalized Difference Vegetation Index (NDVI) time-series is built based on multi-source remote sensing images. Savitzky-Golay filter (S-G filter) was applied for denoising and reconstructing the time series. NDVI time series were used to calculate phenological indicators. Five phenological indicators were selected, which include seasonal amplitude, growth rate, seasonal length, seasonal integral and base level, to study on effect of heavy metal stress on rice phenology. The results showed that differences in phenological indexes at different levels of heavy metal stress as well as the phenological indicators under the stressed condition were generally lower than those under mild and moderate stress. From the findings, it can be concluded that the superiority of remote sensing phenological information in the monitoring of heavy metal stress in rice, and a new method for distinguishing heavy metal stress in rice will be provided.

Relationships between microbial activity, enzyme activities and metal(loid) form in Ni[sbnd]Cu tailings area

Here we combined microcalorimetry, enzyme activity measurements, and characterization of metal form in order to evaluate the effect of metal(loid)s on the activity of microbial community inhabiting tailings area with high toxic metal(loid)s concentration. Chromium (Cr), nickel (Ni), copper (Cu) and manganese (Mn) were the main pollutants. The exchangeable fractions (bioavailability) of Cu, Ni and Mn were higher in the tailings sample (Site Z), indicating a higher environmental risk. The total heat Qtotal (17,726.87 J/g), peak power Ppeak (541.42 μW/g) and growth rate constant k (0.11 h−1) of Site Z were higher than that of the polluted soil around tailings (Site Y). Such observation may be explained by physiological changes within the microbial community in response to high levels of heavy metal stress, thereby increasing respiration and improving microbial activity. In contrast, enzyme activities and enzyme activities index (GmeA) of Site Z were lower than the Site Y, which is strongly influenced by changes on physical-chemical properties (TN and TOC) and the presence of Cr, Mn, and Ni. Correlation coefficient and principal component analysis (PCA) indicate that GmeA is significantly correlated (p < 0.05 or p < 0.01) with environmental factors (EC, TOC and TN), Mn and Ni concentration, Ni bioavailability, and peak time (Tpeak). Therefore, GmeA represents a potential biological indicator for reporting the pollution degree in tailings area. Our results provide a theoretical basis for the prevention and control of pollution in non-ferrous metal(loid) tailings area.

Integration of transcriptome and proteome reveals molecular mechanisms underlying stress responses of the cutworm, Spodoptera litura, exposed to different levels of lead (Pb)

Heavy metals are major environmental pollutants that affect organisms across different trophic levels. Herbivorous insects play an important role in the bioaccumulation, and eventually, biomagnification of these metals. Although effects of heavy metal stress on insects have been well-studied, the molecular mechanisms underlying their effects remain poorly understood. Here, we used the RNA-Seq profiling and isobaric tags for relative and absolute quantitation (iTRAQ) approaches to unravel these mechanisms in the polyphagous pest Spodoptera litura exposed to lead (Pb) at two different concentrations (12.5 and 100 mg Pb/kg; PbL and PbH, respectively). Altogether, 1392 and 1630 differentially expressed genes (DEGs) and 58, 114 differentially expressed proteins (DEPs) were identified in larvae exposed to PbL and PbH, respectively. After exposed to PbL, the main up-regulated genes clusters and proteins in S. litura larvae were associated with their metabolic processes, including carbohydrate, protein, and lipid metabolism, but the levels of cytochrome P450 associated with the pathway of xenobiotic biodegradation and metabolism were found to be decreased. In contrast, the main up-regulated genes clusters and proteins in larvae exposed to PbH were enriched in the metabolism of xenobiotic by cytochrome P450, drug metabolism-cytochrome P450, and other drug metabolism enzymes, while the down-regulated genes and proteins were found to be closely related to the lipid (lipase) and protein (serine protease, trypsin) metabolism and growth processes (cuticular protein). These findings indicate that S. litura larvae exposed to PbL could enhance food digestion and absorption to prioritize for growth rather than detoxification, whereas S. litura larvae exposed to PbH reduced food digestion and absorption and channelized the limited energy for detoxification rather than growth. These contrasting results explain the dose-dependent effects of heavy metal stress on insect life-history traits, wherein low levels of heavy metal stress induce stimulation, while high levels of heavy metal stress cause inhibition at the transcriptome and proteome levels.

A Brief Survey of Assessment Models to Predict Stress Level of Heavy Metals in Soil

Soil is a major and important natural resource, which not only support human life but also furnish commodities for ecological and economic growth. Ecological risk, such as degradation of soil pose a serious threat to the ecosystem. The high-stress level of heavy metals like chromium, copper, cadmium, etc., produce ecological risks, which include: decrease in the fertility of the soil; reduction in crop yield and degradation of metabolism of living beings, and hence ecological health. The ecological risk associated demands the assessment of heavy metal stress levels in soils. As the rate of stress level of heavy metals is exponentially increasing in recent times, it is apparent to assess or predict heavy metal contamination in soil. The assessment will help the concerned authorities to take corrective as well as preventive measures to enhance the ecological, and hence economic growth. This study reviews the efficient assessment models to predict contamination of soil due to heavy metal.

Potential use of grapevine cv Askari for heavy metal phytoremediation purposes at greenhouse scale.

Grapevine varieties possess desirable characteristics for phytoremediation purposes. We investigated the potential of grapevine cv Askari in phytoremediation of heavy metal (HM) pollutions. In total 80 grapevine seedlings were exposed to four levels of HM stress (mild, low, moderate, and severe) in greenhouse condition during two growing years (2018 and 2019). The HM concentrations (Zn, Cu, Cd, Cr, and Pb) were subsequently determined in the soils, roots, and grapevine aboveground parts (AGPs), and then phytoextraction and phytostabilization potential assessment indices, i.e., biological absorption coefficient (BAC), bioconcentration factor (BCF), and translocation factor (TF), were calculated. Results led to ranking of the cumulative concentration order of the HMs, i.e.: soils (3476.6) > AGPs (1418.8) > roots (562.2) mg/kg-DW. The mean concentrations ranking order of studied HMs were in soil: Cu (1184.8) > Pb (865.5) > Cd (803.2) > Cr (623.0) > Zn (277.9) mg/kg-DW; roots, Cu (242.0) > Cd (239.5) > Zn (188.8) > Pb (63.5) > Cr (17.2) mg/kg-DW; and AGP environments, Cu (910.2) > Cd (322.9) > Zn (160.3) > Pb (152.9) > Cr (25.3) mg/kg-DW. Principal component analysis results demonstrated the same distribution pattern for the studied HMs between soil, root, and AGP environments, and the highest correlation coefficients were found for Cu, Zn, and Cd. Based on the obtained results (Cu-BAC (> 1), Zn-BCF (> 1), Zn-TF (< 1), Cu-AGP mean concentrations (> 1000 mg/kg-DW), and Cd-AGP mean concentrations (> 100 mg/kg-DW)), it can be concluded that grapevine cv Askari possesses potentials for phytoremediation purposes of Cu, Zn, and Cd. These results were acquired in a greenhouse environment and under controlled conditions; we suggest that the phytoremediation potential of this grapevine variety be assessed in a contaminated vineyard environment as well.

Spatio-temporal Index Based on Time Series of Leaf Area Index for Identifying Heavy Metal Stress in Rice under Complex Stressors.

Crops under various types of stresses, such as stress caused by heavy metals, drought and pest/disease exhibit similar changes in physiological-biochemical parameters (e.g., leaf area index [LAI] and chlorophyll). Thus, differentiating between heavy metal stress and nonheavy metal stress presents a great challenge. However, different stressors in crops do cause variations in spatiotemporal characteristics. This study aims to develop a spatiotemporal index based on LAI time series to identify heavy metal stress under complex stressors on a regional scale. The experimental area is located in Zhuzhou City, Hunan Province. The situ measured data and Sentinel-2A images from 2017 and 2018 were collected. First, a series of LAI in rice growth stages was simulated based on the WOrld FOod STudies (WOFOST) model incorporated with Sentinel 2 images. Second, the local Moran’s I and dynamic time warping (DTW) of LAI were calculated. Third, a stress index based on spatial and temporal features (SIST) was established to assess heavy metal stress levels according to the spatial autocorrelation and temporal dissimilarity of LAI. Results revealed the following: (1) The DTW of LAI is a good indicator for distinguishing stress levels. Specifically, rice subjected to high stress levels exhibits high DTW values. (2) Rice under heavy metal stress is well correlated with high-high SIST clusters. (3) Rice plants subjected to high pollution are observed in the northwest of the study regions and rice under low heavy metal stress is found in the south. The results suggest that SIST based on a sensitive indicator of rice biochemical impairment can be used to accurately detect regional heavy metal stress in rice. Combining spatial-temporal features and spectral information appears to be a highly promising method for discriminating heavy metal stress from complex stressors.

Extracting heavy metal stress indicators from remote sensing imagery using WOFOST model and wavelet packet decomposition algorithm

Heavy metal pollution of crops seriously endangers food security and indirectly threatens human health. Direct measures in the fields and laboratories through on-site sample collection, testing, and analysis are time-consuming and labor intensive, thereby prohibiting their applications in large-scale monitoring. Remote sensing techniques provide an alternative means through examining above-ground vegetation status, e.g. leaf area index (LAI). Heavy metals, however, are typically accumulated in the root of crops, which may also be affected by a large number of external environmental factors besides heavy metals. The objective of this paper, therefore, is to identify heavy metal stress indicators of crops through integrating LAI extraction from remote sensing imagery, weight of rice roots (WRT) estimation by the World Food Study (WOFOST) model, and heavy metal stress indicator identification with the wavelet packet decomposition (WPD) method. First, LAI was retrieved from the HJ CCD data over three continuous years through constructing a relationship between LAI and normalized difference vegetation index (NDVI). Next, dry weight of rice roots (WRT) curves over these three continuous years were estimated using the WOFOST model with multi-temporal LAIs are inputs. Finally, a component (e.g. cfs 14) was identified to represent the heavy metal pollution status with the Wavelet Packet Decomposition (WPD) of the WRT curves of these three years. Validation results suggest that the identified component can successfully represent different levels of heavy metal stress.

Collaborative inversion heavy metal stress in rice by using two-dimensional spectral feature space based on HJ-1 A HSI and radarsat-2 SAR remote sensing data

Accurate and extensive monitoring of heavy metal pollution levels in rice fields is crucial to agricultural production and food safety.Most previous studies used optical remote sensing data to monitor heavy metal stress in rice in which way the remote sensing features are not diversified and the monitoring accuracy is relatively low. Compared with that optical remote sensing can monitor features like color and cell internal structure, microwave remote sensing can monitor the morphology and geometrical features. The complementary characteristics of multi-source remote sensing data are reflected in two aspects. On the one hand, the high spectral resolution characteristics of hj-1a HIS and the high spatial resolution characteristics of radarsat-2 SAR satellite are used for feature fusion to obtain the image data of high altitude spectral resolution; on the other hand, the two remote sensing data can depict the vegetation characteristics from different angles. In order to fully extract the characteristics of crop stress, the model was constructed by utilizing the complementary characteristics of multi-source remote sensing data. This paper synergized optical and microwave remote sensing to construct the crop heavy metal stress monitoring model. To this end, certain rice-growing areas polluted by heavy metal in Suzhou city were selected as the experimental areas, where ASD spectrum data, biochemical parameters and heavy metal content data of rice were collected during critical growth periods on one hand, HJ-1 A HIS and Radarsat-2 SAR satellite data were obtained almost simultaneously on the other hand. Based on heavy metal stress-responsive mechanism of rice, NVI (R598, R508) and SVI(HV,VH,HH,VV), spectral indexes sensitive to heavy metal stress, were extracted from the optical and radar data respectively to construct a two-dimensional feature space, based on which an optical-and-radar-remote-sensing-combined model for monitoring heavy metal stress in rice was constructed. In this paper, the study and main conclusions are as follows:(1) It used spectral characteristics analysis combined with statistic methods or random forest algorithm to build the canopy chlorophyll index NVI, which is sensitive to chlorophyll content changes of rice under heavy metal stress, and a heavy metal stress level inversion model was built based on hyperspectral HSI data accordingly. Also, it used statistic method to build the microwave index SVI, which is sensitive to biomass changes of rice under heavy metal stress, and a heavy metal stress level inversion model was built based on microwave SAR images as a result. (2) A combination of biochemical and morphological parameters, both responding to heavy metal stress, was synergized with NVI and SVI, sensitive to chlorophyll and biomass changes respectively, to build a two-dimensional feature space. Heavy metal stress levels were classified in this space. Thus, a synergetic model to retrieve heavy metal stress based on multi-source remote sensing data was developed. In this paper, the innovation lies in a model constructed by synergizing optical and radar remote sensing data to monitor heavy metal stress in rice based on a multi-dimensional spectral feature space and this model can be applied to monitoring multiple environmental stresses in crops.

Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms.

Heavy metal pollution in crops leads to phenological changes, which can be monitored by remote sensing technology. The present study aims to develop a method for accurately evaluating heavy metal stress in rice based on remote sensing phenology. First, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to blend Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat to generate a time series of fusion images at 30 m resolution, and then the vegetation indices (VIs) related to greenness and moisture content of the rice canopy were calculated to create the time-series of VIs. Second, phenological metrics were extracted from the time-series data of VIs, and a feature selection scheme was designed to acquire an optimal phenological metric subset. Finally, an ensemble model with optimal phenological metrics as classification features was built using random forest (RF) and gradient boosting (GB) classifiers, and the classification of stress levels was implemented. The results demonstrated that the overall accuracy of discrimination for different stress levels is greater than 98%. This study suggests that fusion images can be utilized to detect heavy metal stress in rice, and the proposed method may be applicable to classify stress levels.

A New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice.

Heavy metal stress in crops is a worldwide problem that requires accurate and timely monitoring. This study aimed to improve the accuracy of monitoring heavy metal stress levels in rice by using multiple Sentinel-2 images. The selected study areas are in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. A novel vegetation index called heavy metal stress sensitive index (HMSSI) was proposed. HMSSI is the ratio between two red-edge spectral indices, namely the red-edge chlorophyll index (CIred-edge) and the plant senescence reflectance index (PSRI). To demonstrate the capability of HMSSI, the performances of CIred-edge and PSRI in discriminating heavy metal stress levels were compared with that of HMSSI at different growth stages. Random forest (RF) was used to establish a multitemporal monitoring model to detect heavy metal stress levels in rice based on HMSSI at different growth stages. Results show that HMSSI is more sensitive to heavy metal stress than CIred-edge and PSRI at different growth stages. The performance of a multitemporal monitoring model combining the whole growth stage images was better than any other single growth stage in distinguishing heavy metal stress levels. Therefore, HMSSI can be regarded as an indicator for monitoring heavy metal stress levels with a multitemporal monitoring model.

Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology.

Heavy metal pollution of croplands is a major environmental problem worldwide. Methods for accurately and quickly monitoring heavy metal stress have important practical significance. Many studies have explored heavy metal stress in rice in relation to physiological function or physiological factors, but few studies have considered phenology, which can be sensitive to heavy metal stress. In this study, we used an integrated Normalized Difference Vegetation Index (NDVI) time-series image set to extract remote sensing phenology. A phenological indicator relatively sensitive to heavy metal stress was chosen from the obtained phenological periods and phenological parameters. The Dry Weight of Roots (WRT), which directly affected by heavy metal stress, was simulated by the World Food Study (WOFOST) model; then, a feature space based on the phenological indicator and WRT was established for monitoring heavy metal stress. The results indicated that the feature space can distinguish the heavy metal stress levels in rice, with accuracy greater than 95% for distinguishing the severe stress level. This finding provides scientific evidence for combining rice phenology and physiological characteristics in time and space, and the method is useful to monitor heavy metal stress in rice.

Thermal infrared imaging of the variability of canopy-air temperature difference distribution for heavy metal stress levels discrimination in rice

This paper addresses the assessment and interpretation of the canopy-air temperature difference ( T c - T a ) distribution as an indicator for discriminating between heavy metal stress levels. T c - T a distribution is simulated by coupling the energy balance equation with modified leaf angle distribution. Statistical indices including average value (AVG), standard deviation (SD), median, and span of T c - T a in the field of view of a digital thermal imager are calculated to describe T c - T a distribution quantitatively and, consequently, became the stress indicators. In the application, two grains of rice growing sites under “mild” and “severe” stress level were selected as study areas. A total of 96 thermal images obtained from the field measurements in the three growth stages were used for a separate application of a theoretical variation of T c - T a distribution. The results demonstrated that the statistical indices calculated from both simulated and measured data exhibited an upward trend as the stress level becomes serious because heavy metal stress would o

Distinguishing Heavy-Metal Stress Levels in Rice Using Synthetic Spectral Index Responses to Physiological Function Variations

Accurately assessing the heavy-metal contamination in crops is crucial to food security. This study provides a method to distinguish heavy-metal stress levels in rice using the variations of two physiological functions as discrimination indices, which are obtained by assimilation of remotely sensed data with a crop growth model. Two stress indices, which correspond to daily total $\text{CO}_{2}$ assimilation and dry-matter conversion coefficient were incorporated into the World Food Study (WOFOST) crop growth model and calculated by assimilating the model with leaf area index (LAI), which was derived from time-series HJ1-CCD data. The stress levels are not constant with rice growth; thus, to improve the reliability, the two stress indices were obtained at both the first and the latter half periods of rice growth. To compare the stress indices of different stress levels, a synthetic stress index was established by combining the two indices; then, three types of stress index discriminant spaces based on the synthetic index of different growth periods were constructed, in which the two-dimensional discriminant space based on two growth periods showed the highest accuracy, with a misjudgment rate of 4.5%. When the discrimination rules were applied at a regional scale, the average correct discrimination rate was 95.0%.

Regional heavy metal pollution in crops by integrating physiological function variability with spatio-temporal stability using multi-temporal thermal remote sensing

Heavy metal stress in crops is characterized by stability in space and time, which differs from other stressors that are typically more transient (e.g., drought, pests/diseases, and mismanagement). The objective of this study is to assess regional heavy metal stress in rice by integrating physiological function variability with spatio-temporal stability based on multi-temporal thermal infrared (TIR) remote sensing images. The field in which the experiment was conducted is located in Zhuzhou City, Hunan Province, China. HJ-1B images and in-situ measured data were collected from rice growing in heavy metal contaminated soils. A stress index (SI) was devised as an indicator for the degree of heavy metal stress of the rice in different growth stages, and a time-spectrum feature space (TSFS) model was used to determine rice heavy metal stress levels. The results indicate that (i) SI is a good indicator of rice damage caused by heavy metal stress. Minimum values of SI occur in rice subject to high pollution, followed by larger SI with medium pollution and maximum SI for low pollution, for the same growth stage. (ii) SI shows some variation for different growth stages of rice, and the minimum SI occurs at the flowering stage. (iii) The TSFS model is successful at identifying rice heavy metal stress, and stress levels in rice stabilized regardless of the model being applied in the two different years. This study suggests that regional heavy metal stress in crops can be accurately detected using TIR technology, if a sensitive indicator of crop physiological function impairment is used and an effective model is selected. A combination of spectrum and spatio-temporal information appears to be a very promising method for monitoring crops with various stressors.

Optimizing the Temporal Scale in the Assimilation of Remote Sensing and WOFOST Model for Dynamically Monitoring Heavy Metal Stress in Rice

Obtaining precise information regarding the levels of heavy metal stress in crops is vital for food security. The assimilation of remote sensing into the World Food Study (WOFOST) model provides a method for achieving the spatial–temporal evaluation of crop growth status, while the optimization of the temporal scale in assimilation framework has rarely been considered. In this study, the temporal scale was optimized based on a wavelet transform of the leaf area index (LAI) curves. The accurate simulation of LAI laid the foundation for high precision. As the dry weight of rice roots (WRT) was demonstrated to be the most stress-sensitive indicator, the measured WRT values were assimilated into the improved WOFOST model to realize the dynamic simulation of LAI. Finally, four optimal time points were determined based on the extreme areas in the d4 wavelet coefficient, providing a reference for the selection of remote sensing images. The verification in the two sample plots indicated that the assimilation with optimized temporal scale could significantly improve the efficiency on the basis of guaranteeing the accuracy, shortening the run time of model operation by more than 30%. Based on the optimized temporal scale, the RS-WOFOST assimilation framework was driven for each pixel in the study areas, achieving the spatial–temporal evaluation of heavy metal stress in rice. This study suggests that the wavelet transform to LAI is applicable for optimizing the temporal scale in assimilation, providing a reference for the improvement of assimilation results under the premise of balancing accuracy and efficiency.

Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model.

Accurate monitoring of heavy metal stress in crops is of great importance to assure agricultural productivity and food security, and remote sensing is an effective tool to address this problem. However, given that Earth observation instruments provide data at multiple scales, the choice of scale for use in such monitoring is challenging. This study focused on identifying the characteristic scale for effectively monitoring heavy metal stress in rice using the dry weight of roots (WRT) as the representative characteristic, which was obtained by assimilation of GF-1 data with the World Food Studies (WOFOST) model. We explored and quantified the effect of the important state variable LAI (leaf area index) at various spatial scales on the simulated rice WRT to find the critical scale for heavy metal stress monitoring using the statistical characteristics. Furthermore, a ratio analysis based on the varied heavy metal stress levels was conducted to identify the characteristic scale. Results indicated that the critical threshold for investigating the rice WRT in monitoring studies of heavy metal stress was larger than 64 m but smaller than 256 m. This finding represents a useful guideline for choosing the most appropriate imagery.

Antioxidative and Biochemical Responses in Dalbergia sissoo Roxb. Seedlings Growing Under Cobalt and Lead Stress

Dalbergia sissoo Roxb. is one of the most important timber species of India. It is used for high quality furniture, cabinets making and has pesticidal property. It is commonly planted tree species in urban and suburban areas, roadsides and industrial areas where the soil may be polluted with heavy metals such as Co or Pb. Lead (Pb) and Cobalt (Co) are one of the non essential and toxic heavy metals which can cause oxidative stress in plants. The concentrations of these heavy metals in the environment are currently increasing, due mainly to anthropogenic activities. The effect of these heavy metals on biochemical parameters and antioxidant activity were studied in leaves of Dalbergia sissoo Roxb. grown under control laboratory conditions in pot culture and four levels of heavy metal stress (10, 20, 30 and 40ppm Pb2+, Co2+). Six months exposure of Dalbergia sissoo Roxb. plantlets to different concentration of Co or Pb in normal soil conditions elicited an antioxidative response, measured in terms of ascorbic acid, proline accumulation and biochemical responses in terms of protein and free amino acids. The results indicate that the proline, ascorbic acid contents increased with increase in Co and Pb amount in soil whereas the protein and total chlorophyll contents showed opposite trend.

The dynamic simulation of rice growth parameters under cadmium stress with the assimilation of multi-period spectral indices and crop model

Obtaining precise information regarding the levels of heavy metal stress in crops is vital for food security, agricultural production, and ecological protection. In this study, we realized the dynamic simulation of rice growth parameters in three experiment fields that were exposed to varying levels of soil Cd (cadmium) concentration, intending to monitor the stress-induced changes of growth parameters on time scale. To simulate the growth parameters Leaf Area Index (LAI), Weight of Storage Organs (WSO) and Total Above Ground Production (TAGP) more accurately, we imbedded a Cd stress factor fCd into the initial World Food Study (WOFOST) model. Then, as the spectral sensing technology is a potentially promising method to monitor crop stress conditions, an optimized methodology of assimilating multi-period spectral indices into the coupled WOFOST+PROSPECT+SAIL model was adopted to obtain the optimum value of stress factor; next, the dynamic simulation of growth parameters was adjusted. Particularly, based on the specific sensibility to contamination levels at different growth stages, TCARI (Transformed Chlorophyll Absorption in Reflectance Index), REP (Red Edge Position) and RH (Reflectance Height) were selected as the multi-period spectral indices, serving as the compared targets of the cost function in the process of assimilation. The growth parameters simulated by the modified WOFOST model preferably reflected the variations of rice growth status on a time scale with R2 over 94% at all of the three levels. This study indicates that the optimized methodology of assimilating multi-period spectral indices into the crop model is applicable for simulating growth parameters under Cd stress, which provides a reference for dynamically monitoring heavy metal contamination in farmland environments.

An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice

Heavy metal contamination in crops is a worldwide problem that requires accurate and timely monitoring. This study is aimed at improving the accuracy of monitoring heavy metal stress levels in rice utilizing remote sensing data. An assimilation framework based on remote sensing and improved crop growth model was developed to continuously monitor heavy metal stress levels over the entire period of crop growth based on the growth law of crops and the stress mechanism. Compared with other physiological indices, dry weight of rice roots (WRT) was selected as the best indicator to estimate heavy metal stress levels. The World Food Study (WOFOST) model, widely used for the description of crop growth, was improved by incorporating stress factors with overall consideration for the changes in physiological status under heavy metal stress. Three scenarios were put forward based on the stress factors fDTGA and fCVF, which, respectively, correspond to the daily total gross assimilation of CO2 and carbohydrate-to-dry matter conversion coefficient, and were analyzed for their efficiency of simulating WRT. A method of assimilating the leaf area index (LAI) retrieved from remotely sensed data into the improved WOFOST model was applied to optimize fDTGA and fCVF. The results suggested that the scenario using both factors can simulate WRT under heavy metal stress more accurately, with a relative percent error (RPE) lower than 14%. Based on the RS-WOFOST assimilation framework, continuous spatial-temporal evaluation of heavy metal stress levels based on WRT can be accomplished.