Vegetation reflectance spectroscopy for biomonitoring of heavy metal pollution in urban soils

Spectral characteristics of the correlation between elemental arsenic and vegetation stress in the Yueliangbao gold mining

Some soils in the Yueliangbao gold mining area have been contaminated by heavy metals, resulting in variations in vegetation. Hyperspectral remote sensing provides a new perspective for heavy metal inversion in vegetation. In this paper, we collected ground truth spectral data of three dominant vegetation species, Miscanthus floridulus, Equisetum ramosissimum and Eremochloa ciliaris, from contaminated and healthy non-mining areas of the Yueliangbao gold mining region, and determined their heavy metal contents. Firstly, we compared the spectral characteristics of vegetation in the mining and non-mining areas by removing the envelope and derivative transformation. Secondly, we extracted their characteristic identification bands using the Mahalanobis distance and PLS-DA method. Finally, we constructed the inverse model by selecting the vegetation index (such as the PRI, DCNI, MTCI, etc.) related to the characteristic band combined with the heavy metal content. Compared to previous studies, we found that the pollution level in the Yueliangbao gold mining area had greatly reduced, but arsenic metal pollution remained a serious issue. Miscanthus floridulus and Eremochloa ciliaris in the mining area exhibited obvious arsenic stress, with a large "red-edge blue shift" (9 and 6 nm). The extracted characteristic wavebands were around 550 and 680-740 nm wavelengths, and correlation analysis showed significant correlations between vegetation index and arsenic, allowing us to construct a prediction model for arsenic and realize the calculation of heavy metal content using vegetation spectra. This provides a methodological basis for monitoring vegetation pollution in other gold mining areas.

Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy

Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.

Monitoring the soil copper pollution degree based on the reflectance spectrum of an arid desert plant

Visible and near-infrared reflectance spectroscopy offers a rapid, inexpensive, and environmentally friendly method for monitoring copper pollution in the soil. However, the application of this approach in vegetation-covered areas is still a challenge due to interference from plants, making it difficult to acquire soil reflectance spectra. To address this problem, this study assesses whether the reflectance spectrum of a widely distributed arid desert plant (Seriphidium terrae-albae) can be used to rapidly evaluate copper pollution in the soil. A pot experiment was conducted for five months from April to September 2019. The reflectance spectra of the plants were measured in June, July, and August 2019 using an ASD Fieldspec3 spectrometer. Each month, the five vegetation indexes with the highest correlation with the evaluation value of the copper pollution degree were input into an extreme learning machine (ELM) to build a model to monitor the degree of copper pollution in the soil. The results showed that the model could quickly evaluate the degree of copper pollution, but the accuracy varied widely among the calculated vegetation indexes depending on the month when the spectral data were extracted. The model constructed by selecting ten vegetation indexes composed of plant spectra collected in June and July provides high recognition accuracy, reaching 89.02%. Only seven bands were needed due to the model's low complexity, which means that it has great potential to be applied to remote sensing images to establish a real-time monitoring system to detect copper pollution in the soil. This study proposed a simple and rapid method for monitoring copper pollution in soil using plant spectra, and this method could provide extremely valuable for soil protection and management in arid desert areas.

A novel approach for long-term spectral monitoring of desert shrubs affected by an oil spill

Crude oil pollution is a global environmental concern since it persists in the environment longer than most conventional carbon sources. In December 2014, the hyper-arid Evrona Nature Reserve, Israel, experienced large-scale contamination when crude oil spilled. The overarching goal of the study was to investigate the possible changes, caused by an accidental crude oil spill, in the leaf reflectance and biochemical composition of four natural habitat desert shrubs. The specific objectives were (1) to monitor the biochemical properties of dominant shrub species in the polluted and control areas; (2) to study the long-term consequences of the contamination; (3) to provide information that will assist in planning rehabilitation actions; and (4) to explore the feasibility of vegetation indices (VIs), along with the machine learning (ML) technique, for detecting stressed shrubs based on the full spectral range. Four measurement campaigns were conducted in 2018 and 2019. Along with the various stress indicators, field spectral measurements were performed in the range of 350-2500 nm. A regression analysis to examine the relation of leaf reflectance to biochemical contents was carried out, to reveal the relevant wavelengths in which polluted and control plants differ. Vegetation indices applied in previous studies were found to be less sensitive for indirect detection of long-term oil contamination. A novel spectral index, based on indicative spectral bands, named the "normalized blue-green stress index" (NBGSI), was established. The NBGSI distinguished significantly between shrubs located in the polluted and in the control areas. The NBGSI showed a strong linear correlation with pheophytin a. Machine learning classification algorithms obtained high overall prediction accuracy in distinguishing between shrubs located in the oil-polluted and the control sites, indicating internal component differences. The findings of this study demonstrate the efficacy of indirect and non-destructive spectral tools for detecting and monitoring oil pollution stress in shrubs.

Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review

This review outlines the advances achieved in monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing over the last 50 years. A broad diversity of methods based on field and imaging spectroscopy were developed in that field for precision farming and environmental monitoring purposes. From the 466 articles reviewed, we identified the main factors to consider to achieve accurate monitoring of plant stress, namely: The plant species and the stressor to monitor, the goal (detection or quantification), and scale (field or broad-scale) of monitoring, and the need for controlled experiments. Based on these factors, we then provide recommendations and guidelines for the development of reliable methods to monitor 11 major biotic and abiotic plant stressors. For each stressor, the effects on plant health and reflectance are described and the most suited spectral regions, scale, spatial resolution, and processing approaches to achieve accurate monitoring are presented. As a perspective, we discuss two major components that should be implemented in future methods to improve stress monitoring: The discrimination of plant stressors with similar effects on plants and the transferability of the methods across scales.

Extremely high concentrations of zinc in birch tree leaves collected in Chelyabinsk, Russia

Zinc is an essential trace element and a vital microelement for human health. Zinc can be toxic when exposures exceed physiological needs. Toxic effects in humans are most evident from inhalation exposure to high concentrations of Zn compounds. Urban air pollution can be especially dangerous due to the Zn content in airborne dust. Tree leaves can absorb significant levels of zinc. In this study, leaf deposition of Zn was investigated in Chelyabinsk, Russia. Russian zinc production plant and metallurgical plant are located in Chelyabinsk. Extremely high concentrations of Zn (316-4000 mg kg^-1) were found in the leaves of birch trees. It is well known that traffic also is Zn source in an urban environment. Trees, growing at the different distances from zinc production and metallurgical plants and road to identify the contribution of each source (road or industry), were studied. Through SEM analysis, the prevalence of small particulates (PM10 and less), containing Zn, illustrated leaf Zn deposition from the air by passing root accumulation. It was shown that emission of zinc production plant and the metallurgical plant is the main source of leaf Zn deposition in Chelyabinsk.

Metallothionein production is a common tolerance mechanism in four species growing in polluted Cu mining areas in Peru

Cu pollution is a problem in mining areas in Peru. Here we evaluate the phytoextraction capacity, physiological and proteomic responses of four species growing in copper-contaminated areas in Arequipa, Peru. The plants used in the experiments were obtained by collecting seedlings (Tessaria integrifolia, Bacharis salicifolia), rhizomes (Eleocharis montevidensis) and seeds (Chenopodium murale) along a polluted river. They were exposed to solutions containing 2, 4, 8, 16 and 32 mg Cu L^-1 during 20 days. Growth was affected in a concentration-dependent way. According to the tolerance index, B. salicifolia and C. murale were the most sensitive species, but with greater Cu phytoextraction capacity and accumulation in the biomass. The content and ratio of photosynthetic pigments changed differently for each specie and carotenoids level were less affected than chlorophyll. Cu also induced changes in the protein and sugar contents. Antioxidant enzyme activities (catalase and superoxide dismutase) increased with a decrease in the malondialdehyde. There were marked changes in the protein 2D-PAGE profiles with an increase in the abundance of metallothioneins (MT) of class II type I and II. Our results suggest that these species can grow in Cu polluted areas because they developed multiple tolerance mechanisms, such as and MTs production seems a important one.

Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

Monitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ - 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.

Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy

Declining urban tree health can affect critical ecosystem services, such as air quality improvement, temperature moderation, carbon storage, and biodiversity conservation. The application of state-of-the-art remote sensing data to characterize tree health has been widely examined in forest ecosystems. However, such application to urban trees has not yet been fully explored—due to the presence of heterogeneous tree species and backgrounds, severely complicating the classification of tree health using remote sensing information. In this study, tree health was represented by a set of field-assessed tree health indicators (defoliation, discoloration, and a combination thereof), which were classified using airborne laser scanning (ALS) and hyperspectral imagery (HSI) with a Random Forest classifier. Different classification scenarios were established aiming at: (i) Comparing the performance of ALS data, HSI and their combination, and (ii) examining to what extent tree species mixtures affect classification accuracy. Our results show that although the predictive power of ALS and HSI indices varied between tree species and tree health indicators, overall ALS indices performed better. The combined use of both ALS and HSI indices results in the highest accuracy, with weighted kappa coefficients (Kc) ranging from 0.53 to 0.79 and overall accuracy ranging from 0.81 to 0.89. Overall, the most informative remote sensing indices indicating urban tree health are ALS indices related to point density, tree size, and shape, and HSI indices associated with chlorophyll absorption. Our results further indicate that a species-specific modelling approach is advisable (Kc points improved by 0.07 on average compared with a mixed species modelling approach). Our study constitutes a basis for future urban tree health monitoring, which will enable managers to guide early remediation management.

Ecological risk assessment and source identification of heavy metal pollution in vegetable bases of Urumqi, China, using the positive matrix factorization (PMF) method

Heavy metal pollution is a widespread problem and strongly affects human health through the food chain. In this study, the overall pollution situation and source apportionment of heavy metals in soil (Hg, Cd, As, Pb, Ni, Zn, Cu and Cr) were evaluated using various methods including geo-accumulation index (Igeo), potential ecological risk index (RI) and positive matrix factorization combined with Geographical Information System (GIS) to quantify and identify the possible sources to these heavy metals in soils. The results of Igeo showed that this farmland top soil moderate contaminated by Hg, other selected elements with noncontamination level. And the average RI in the top soil was 259.89, indicating a moderate ecological risk, of which Hg and Cd attributed 88.87% of the RI. The results of the PMF model showed that the relative contributions of heavy metals due to atmospheric depositions (18.70%), sewage irrigations (21.17%), soil parent materials (19.11%), industrial and residential coal combustions (17.43%) and agricultural and lithogenic sources (23.59%), respectively. Of these elements, Pb and Cd were came from atmospheric deposition. Cr was attributed to sewage irrigations. As was mainly derived from the soil parent materials. Hg originated from industrial and residential coal combustions, and most of the Cu, Zn and Ni, except for Pb, were predominantly derived from agricultural and lithogenic sources. These results are important in considering management plans to control the aggravation of heavy metal pollution and ultimately to protect soil resources in this region. In addition, this study enhances the understanding of heavy metal contamination occurrence in agroecosystem that helps predicting and limiting the potential of heavy metal exposure to people and ecosystem.

Soil high Cd exacerbates the adverse impact of elevated O3 on Populus alba 'Berolinensis' L

Pollution with both heavy metal and ground-level ozone (O₃) has been steadily increasing, especially in the cities with heavy industry. Little information is known about their combined impacts on urban tree. This study was aimed at characterizing the interactive effects of soil cadmium (Cd) addition and O₃ fumigation on visible injury and growth, photosynthesis, oxidative stress, antioxidant enzyme activities, abscisic acid (ABA) content and bioaccumulation of Cd in one-year-old Populus alba 'Berolinensis' saplings by using open top chambers in Shenyang city with developed heavy industry, Northeast China. In this study, poplar saplings were grown in the pots containing soil with different concentrations of Cd (0, 100 and 500 mg kg^-1) under ambient air (40 µg L^-1) and elevated O₃ (120 µg L^-1). The results showed that EO and its combination with high Cd (500 mg kg^-1) induced significant foliar injury symptoms, decreased root weight (by 41.6%) and total biomass (by 17.4%), inhibited net photosynthetic rate and stomatal conductance, and increased malondialdehyde and ABA contents after 4 weeks of O₃ exposure. Elevated O₃ exacerbated the accumulation of Cd in leaves and stems of poplar plants grown in the pots with high Cd-polluted soil. Our results also indicated that high Cd pollution in soil increased the susceptibility of plants to O₃ and exacerbated the adverse impact of elevated O₃ on physiological metabolisms of poplar species, which implied that it was very necessary to take into consideration for O₃-tolerance of tree species during phytoremediation of Cd-polluted soil in heavy industrial areas.