Biochemical and Physiological Detection of Sulfur Dioxide Injury to Pea Plants (Pisum sativum)

Biochemical and physiological experiments were conducted on pea plants (Pisum sativum) continuously exposed in growth chambers to SO₂ gas for 18 days. S0₂ gas concentrations were 0.1, 0.15, and 0.25 ppm. In plants exposed to 0.1 and 0.15 ppm it was clearly demonstrated that there was a greater accumulation of inorganic sulfur, a reduced buffer capacity of the cells relative to H-ions, and a stimulation of glutamate dehydrogenase activity. The only macroscopic symptom seen was slight chlorosis of the older leaves. There was only a slight decrease in fresh and dry weights of these plants compared to the control plants whereas in the group of plants exposed to 0.25 ppm SO₂ foliage necrosis was considerable. In addition, there was a marked reduction in the fresh and dry weights of the latter plants. However, the relationship among accumulated inorganic sulfur, reduced buffer capacity, and increased glutamate dehydrogenase activity as seen for the lower S0₂ concentrations was close. Accordingly, if might be possible to use these three parameters to diagnose S0₂ injury before any significant symptoms appear. In the case of severe SO₂ injury there was a marked increase in glutamine and ammonia concentrations suggesting that these factors in addition to the above could be used in diagnosing severe SO₂ injury. There was no significant difference between plants treated with 0.1 or 0.15 ppm SO₂ and control plants in the contents of K, Ca, P, and N fractions. Therefore, these factors would not be useful in the early detection of SO₂ injury.     

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.     

Enhanced retention of linuron, alachlor and metalaxyl in sandy soil columns intercalated with wood barriers

A study has been made of the effect a reactive barrier made of pine (softwood) or oak (hardwood) wood intercalated in a sandy soil column has on the retention of linuron, alachlor and metalaxyl (pesticides with contrasting physicochemical characteristics). The leaching of pesticides has been carried out under a saturated flow regime and breakthrough curves (BTCs) have been obtained at flow rates of 1 mL min⁻¹ (all pesticides) and 3 mL min⁻¹ (linuron). The cumulative curves in the unmodified soil indicate a leaching of pesticides >80% of the total amount of compound added. After barrier intercalation, linuron leaching decreases significantly and a modification of the leaching kinetics of alachlor and metalaxyl has been observed. The theoretical R factors increased ∼2.6-3.3, 1.2-1.6-fold, and 1.4-1.7-fold and the concentration of the maximum peak decreased ∼6-12-fold, 2-4-fold and 1.2-2-fold for linuron, alachlor and metalaxyl, respectively. When considering the three pesticides, significant correlations have been found between the theoretical retardation factor (R) and the pore volume corresponding to the maximum peaks of the BTCs (r = 0.77; p < 0.05) or the total volume leached (r = −0.78; p < 0.05). The results reveal the efficacy of reactive wood barriers to decrease the leaching of pesticides from point sources of pollution depends on the type of wood, the hydrophobicity of the pesticide and the adopted water flow rate. Pine was more effective than oak in decreasing the leaching of hydrophobic pesticide linuron or in decreasing the maximum peak concentration of the less hydrophobic pesticides in soils. Efficacy of these wood barriers was limited for the least hydrophobic pesticide metalaxyl.     

Biodegradable chelate enhances the phytoextraction of copper by Oenothera picensis grown in copper-contaminated acid soils

Oenothera picensis plants (Fragrant Evening Primrose) grow in the acid soils contaminated by copper smelting in the coastal region of central Chile. We evaluated the effects of the biodegradable chelate MGDA (methylglycinediacetic acid) on copper extraction by O. picensis and on leaching of copper through the soil profile, using an ex situ experiment with soil columns of varying heights. MGDA was applied in four rates: 0 (control), 2, 6 and 10 mmol plant⁻¹. MGDA application significantly increased biomass production and foliar concentration, permitting an effective increase in copper extraction, from 0.09 mg plant⁻¹ in the control, to 1.3 mg plant⁻¹ in the 6 and 10 mmol plant⁻¹ treatments. With 10 mmol plant⁻¹ rate of MGDA, the copper concentration in the leachate from the 30 cm columns was 20 times higher than in the control. For the 60 cm columns, copper concentration was 2 times higher than the control. It can be concluded that at increased soil depths, copper leaching would be minimal and that MGDA applications at the studied rates would not pose a high risk for leaching into groundwater. It can thus be stated that applications of MGDA are an effective and environmentally safe way to improve copper extraction by O. picensis in these soils.     

Arsenic distribution in soils and plants of an arsenic impacted former mining area

A mining area affected by the abandoned exploitation of an arsenical tungsten deposit was studied in order to assess its arsenic pollution level and the feasibility of native plants for being used in phytoremediation approaches. Soil and plant samples were collected at different distances from the polluting sources and analysed for their As content and distribution. Critical soil total concentrations of As were found, with values in the range 70-5330 mg kg⁻¹ in the uppermost layer. The plant community develops As tolerance by exclusion strategies. Of the plant species growing in the most polluted site, the shrubs Salix atrocinerea Brot. and Genista scorpius (L.) DC. exhibit the lowest bioaccumulation factor (BF) values for their aerial parts, suggesting their suitability to be used with revegetation purposes. The species Scirpus holoschoenus L. highlights for its important potential to stabilise As at root level, accumulating As contents up to 3164 mg kg⁻¹.     

Spectral estimation of global levels of atmospheric pollutants

Underlying levels of atmospheric pollutants, assumed to be governed by smoothing mechanisms due to atmospheric dispersion, can be estimated from global emissions source databases on greenhouse gases and ozone-depleting compounds. However, spatial data may be contaminated with noise or even missing or zero-valued at many locations. Therefore, a problem that arises is how to extract the underlying smooth levels. This paper sets out a structural spatial model that assumes data evolve across a global grid constrained by second-order smoothing restrictions. The frequency-domain approach is particularly suitable for global datasets, reduces the computational burden associated with two-dimensional models and avoids cumbersome zero-inflated skewed distributions. Confidence intervals of the underlying levels are also obtained. An application to the estimation of global levels of atmospheric pollutants from anthropogenic emissions illustrates the technique which may also be useful in the analysis of other environmental datasets of similar characteristics.     

Substituting missing data in compositional analysis

Multivariate analysis of environmental data sets requires the absence of missing values or their substitution by small values. However, if the data is transformed logarithmically prior to the analysis, this solution cannot be applied because the logarithm of a small value might become an outlier. Several methods for substituting the missing values can be found in the literature although none of them guarantees that no distortion of the structure of the data set is produced. We propose a method for the assessment of these distortions which can be used for deciding whether to retain or not the samples or variables containing missing values and for the investigation of the performance of different substitution techniques. The method analyzes the structure of the distances among samples using Mantel tests. We present an application of the method to PCDD/F data measured in samples of terrestrial moss as part of a biomonitoring study.     

Remediation of metal-contaminated soils with the addition of materials--part I: characterization and viability studies for the selection of non-hazardous waste materials and silicates

Contamination episodes in soils require interventions to attenuate their impact. These actions are often based on the addition of materials to increase contaminant retention in the soil and to dilute the contaminant concentration. Here, non-hazardous wastes (such as sugar foam, fly ash and a material produced by the zeolitization of fly ash) and silicates (including bentonites) were tested and fully characterized in the laboratory to select suitable materials for remediating metal-contaminated soils. Data from X-ray fluorescence (XRF), N₂ adsorption/desorption isotherms, X-ray diffraction (XRD) and scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM-EDX) analyses revealed the chemical composition, specific surface area and the phases appearing in the materials. A pH titration test allowed the calculation of their acid neutralization capacity (ANC). The metal sorption and desorption capacities of the waste materials and silicates were also estimated. Sugar foam, fly ash and the zeolitic material were the best candidate materials. Sugar foam was selected because of its high ANC (17 000 meq kg⁻¹), and the others were selected because of their larger distribution coefficients and lower sorption reversibilities than those predicted in the contaminated soils.