Oct 13, 1995 - level of pollutants in the atmosphere is high enough to be considered a health threat. Several ... Córdoba city, the capital of Córdoba Province, is in the centre of the ...... submitted to the Whitecourt Environmental Study Group.
Journal of Environmental Management (1997) 49, 167–181
Correlation Between Environmental Conditions and Foliar Chemical Parameters in Ligustrum lucidum Ait. Exposed to Urban Air Pollutants Martha S. Can˜as, Hebe A. Carreras, Liliana Orellana and Maria L. Pignata∗ Ca´tedra de Quı´mica General, Facultad de Ciencias Exactas, Fı´sicas y Naturales, Universidad Nacional de Co´rdoba, Avda, Ve´lez Sa´rsfield 299, 5000 Co´rdoba, Argentina Received 13 October 1995; accepted 28 December 1995
A diagnostic study was made of Ligustrum lucidum Ait. in relation to atmospheric pollutants in Co´rdoba city, Argentina. The study area receives regional pollutants, and it was categorized by taking into account traffic level, industrial density, type of industry, position of the sample point in relation to the corner, treeless condition and topographic level. Dried weight/fresh weight ratio (DW/FW) and Specific Leaf Area (SLA) were calculated; and concentration of chlorophylls, carotenoids, total sulphur, soluble proteins, malondialdehyde and hydroperoxy conjugated dienes were determined for leaf samples. Malondialdehyde correlates positively with traffic density; foliar sulphur correlates negatively with type of industry; and, finally, DW/FW ratio and SLA correlates negatively with treeless conditions. On the other hand, the concentration of photosynthetic pigments correlates negatively with sulphur, hydroperoxy conjugated dienes, proteins, DW/FW ratio and SLA. Pollution-induced changes in one parameter alone may not be sufficient to draw a clear picture of the situation. An approximation to predict tree injury may be obtained by measuring sulphur content, SLA, hydroperoxy conjugated dienes, proteins, chlorophyll a and chlorophyll b. These parameters could be combined to obtain a general index of air pollution, as they are responsible for the major variability of the data. 1997 Academic Press Limited
Keywords: tree damage, air pollution, L. lucidum Ait.
1. Introduction The use of plants as bioindicators has been studied extensively and has been reviewed by Manning and Feder (1980), Burton (1986) and Lefohn (1991). In the last few years, the effects of air pollution on plant growth and the use of higher plants as bioindicators ∗ Corresponding author. 0301–4797/97/020167+15 $25.00/0/ev950090
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in industrial and urban areas have attracted the attention of researchers (Ali, 1993). However, this area of research is complicated by the interaction of pollutants with other environmental factors. Moreover, no research can be found on this important subject in Co´rdoba, which is one of the most polluted cities in Argentina. Although the situation is critical, there are no data on the quantity or quality of pollutants discharged into the atmosphere, but all indicators seem to point to a worsening situation. In spite of the absence of official records, it would be reasonable to conclude that the level of pollutants in the atmosphere is high enough to be considered a health threat. Several studies have been published on the effects of pollutants, either alone or in combination, on plants grown under controlled conditions (Koziol and Whatley, 1984; Winner et al., 1985; Muir and McCune, 1988; Wallin and Skarby, 1992), but less is known about the behaviour of trees subjected to naturally occurring concentrations of pollutants in urban areas (Guderian et al., 1985a). The main air pollutants of the urban forest are: sulphur dioxide, ozone, fluorides, ethylene, oxides of nitrogen, ammonia, chlorine and hydrogen chlorine, particulates and herbicides. Of these pollutants, sulphur dioxide, ozone and herbicides are of major importance (Grey and Deneke, 1978). The chemical parameters which are affected by air pollutants in leaves are: pigment concentration (Reich, 1983), the composition of polar and neutral lipids (Wolfenden and Wellburn, 1991), the levels of antioxidant compounds (Chen et al., 1991) and relative water content (Rao, 1979). Peroxidative processes have also been cited as pollution damage indicators (Mehlhorn et al., 1990). In plants, these alterations occur before the appearance of any visible injury or a decline in yield (Krause and Prinz, 1986). The suitability of plants as indicators of air pollutants depends on their particular sensitivity, on possible specific reactions and on the differences in resistance of different species and varieties. Normally, two types of plants are used: very sensitive plants, in which visible reactions provide proof of pollution effects, and relatively resistant plants, from which proof is obtained by assessing the absorption of the pollutant into the plant material (Singh et al., 1991). The present investigation was conducted in Co´rdoba city, to fill a gap in knowledge on the interactions between air pollution and vegetation under actual field conditions. The aim of the present work, therefore, was to study the behaviour of chemical parameters of Ligustrum lucidum Ait. exposed to different conditions of environmental pollution. The results of this preliminary study will guide future studies by screening species and measurement variables for their relationship with air pollution.
2. Materials and methods 2.1. Co´rdoba city, the capital of Co´rdoba Province, is in the centre of the Argentine Republic, latitude 31° 24′ S, longitude 64° 11′ W. It is 440 m above sea level, with a population of 1 189 000 (according to the 1991 census). The city has an irregular topography. Its general structure is funnel-shaped with a growing positive slope from the centre towards the surrounding areas. This somewhat concave formation reduces air circulation and causes frequent thermal inversions in autumn and winter. The climate is sub-humid, with an average annual precipitation of 790 mm, concentrated principally in summer. Mean annual temperature is 17·4°C and prevailing winds originate in the NE and SE. The natural vegetation belongs to the
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T 1. Characterization of sampling points in Co´rdoba city Industry Sampling points 1 2 3 4 5 6 7 8 9 10 11 12
Traffic
Type
Dist
Position
Treeless
Level
1 3 4 3 2 3 2 2 3 1 1 3
4 1 1 1 1 1 1 4 1 1 1 1
1 1 3 1 1 1 1 2 1 1 1 1
2 2 3 1 2 3 1 3 2 1 1 1
2 2 2 3 2 3 2 2 3 1 1 2
3 1 3 3 3 2 2 3 3 1 1 2
Espinal Phytogeographical Province (Cabrera, 1976) which consists of low thorny woodlands. Because of human activities, the natural vegetation has almost been eliminated and replaced mostly by exotic trees, mainly species of Fraxinus, Acer, Ligustrum, Melia, Platanus, Ulmus, Populus, etc., and some native genera belonging to different phytogeographical units like Jacaranda, Tabebuia, etc. This study was carried out in the SE sector of the city, an area historically characterized by medium industrial activity and by a high traffic density. Regional background air quality data which reflects its true composition do not exist. A scale was constructed, therefore, in order to categorize the sites of the study area, in which the magnitudes of the contaminant sources are expressed qualitatively. The industries located in the corresponding sector are small and medium sized, predominantly tanneries and metallurgical, such as sheet metal workshops and body shops, carpentry workshops, tyre remoulding businesses, garages, etc. In order to select sampling points, the distribution of L. lucidum Ait. in the area was taken into account in an attempt to represent different traffic conditions. Subsequently, sampling points were categorized to include other characteristics that represent the environmental conditions in each point or microarea (Table 1). In spite of the homogeneity of the sector, it was possible to differentiate areas exhibiting the effects of one or other of the emission sources. 2.2. Different characteristics of each sampling point were valued on a scale comprised of four categories; category 1 included the condition of minimum contamination or minimum pollutant-enhancing effects and category 4 included the condition that represented maximum pollution or maximum pollutant-enhancing effects. The ordinal variables selected for each sampling point were: (1) Traffic density (traffic): evaluated at the maximum vehicular traffic time (10 am–noon) on a scale that ranged from 1 to 4. (2) Type of industry (type): because there are no important industries in this area
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or the ones that appear do not have chimneys that release fumes into the air, an area of approximately 200 m around the sampling point was evaluated. Tanneries were included in category 4, while metallurgies were assigned to category 3 and garages to category 2. Category 1 included points where there were no industries. (3) Distance to the industries (dist): for the following criteria, the distance to the industries was estimated in metres. Category 1: further than 200 m; category 2: distance between 200 and 100 m; category 3: distance between 100 and 50 m; and category 4: less than 50 m. (4) Position of the sampling point with respect to the corner (position). 1: a point in the middle of the block; 2: at a point between the corner and the former point (the middle of the block); 3: at the corner. (5) Situation of the tree in relation with the presence of forestry in the area (treeless). Category 1: surrounded by trees, and 3: without surrounding trees. Categorization of each sampling point was made independently by three researchers. The resulting categories were contrasted with each corresponding variable, and where a discrepancy occurred, it was solved by returning to the sampling point. (6) Topographic level and distance to the river (level). Using a map of the area, the minimum distance from the sampling point to the Suquı´a River was determined. These data were inversely transformed in such a way that their range had the same order in the new scale as in the rest of the categoric variables. Because the topographic level is closely related to the distance to the river, the minimum values of the new variable correspond to the higher areas, where the pollution condition would be minimal because of better wind circulation and lower humidity. 2.3. In the autumn of 1992, we selected roadside evergreen trees of Ligustrum lucidum Ait. on each sample point (n=12); the trees were similar in terms of soil type and soil moisture. L. lucidum Ait. was chosen for this study because it is common in Co´rdoba urban forestry. We collected leaves from the lower crown foliage, at a height of 2–3 m from the ground. Leaves were selected without bias, and foliage from the four cardinal aspects of each tree was included. Lower crown foliage was used because this is the most exposed part of the tree and it has been proposed that this part could reflect more closely the condition of the entire environment (Muir and McCune, 1988). Independent determinations were based on three random sub-samples from a pool of leaves from each tree; assays were carried out in triplicate. 2.4. Leaf samples were placed in paper bags and immediately transferred to the laboratory. The samples were rinsed with distilled water for about 1 min to remove material deposited on foliar surfaces so that the results of chemical analyses of samples collected in various sites could be compared. Leaves were dried for 24 h at room temperature and stored in plastic vials at −15°C in darkness. Only leaves free of petioles were chosen for chemical analyses.
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2.5. Five millilitres of Mg(NO3)2 saturated solution were added to 1 g of plant material and dried in an electric heater. The sample was heated in an oven for 30 min at 500°C. The ashes were then suspended in 10 ml of HCl 6 M, filtered and the resulting solution boiled for 3 min. The solution was finally brought to 50 ml with distilled water. The amount of SO2− 4 in the solution was determined by the acidic suspension method with barium chloride (Toennies and Bakay, 1953). The concentration is expressed in mg g−1 DW. 2.6. / The dry weight/fresh weight (DW/FW) ratio of the samples was determined by storing 1 g of fresh material at 60±2°C until the weight readings were constant. The results are expressed in g g−1 FW of plant material. 2.7. Specific Leaf Area (SLA) was determined by calculating the area of five leaf discs, whose weights were then averaged (Martin and Coughtrey, 1982). The results are expressed in m2 g−1 FW of plant material. 2.8. Malondialdehyde (MDA) was measured by a colorimetric method (Heath and Packer, 1968). The amount of MDA present was calculated from the extinction coefficient of 155 mM−1 cm−1 (Kosugi et al., 1989). Results are expressed in lmol g−1 DW. Hydroperoxy conjugated dienes (HPCD) were extracted by homogenization of the plant material in 96% v/v ethanol at a ratio of 1:50 FW/v with an Ultra Turrax homogenizer. Absorption was measured in the supernatant at 234 nm and diene concentration was calculated using the constant e=2·65×104 M−1 cm−1 (Boveris et al., 1980). Results are expressed as nmol g−1 DW. 2.9. Leaves were powdered in a Polytron grinder. Soluble proteins were extracted with Naphosphate buffer 0·1 M, pH 7·0. Protein determination was carried out according to the Biuret colorimetric method (Gornall et al., 1949). Absorption was recorded in a Bausch & Lomb Spectronic 21, Spectrophotometer. The concentration is expressed in mg g−1 DW. 2.10. Five hundred milligrams of leaves were ground with glass in a mortar and homogenized in 15 ml of ethanol at 96% v/v. Extraction took place over a 24 h period in darkness. Subsequently, the supernatant was separated, in which the absorption was measured at 665 nm, 649 nm and 470 nm to quantify chlorophyll a (Chl. a), chlorophyll b (Chl. b) and carotenoids, respectively (Lichtenthaler and Wellburn, 1983). Based on the quantities of chlorophyll a and b, chlorophyll a+b was calculated. The concentrations are expressed in mg g−1 DW.
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2.11. Statistical analyses were based on the mean value of the determinations performed in the three sub-samples arising from each sampling point. When it was possible, parametric tests resting on the normal distribution assumption were used. Univariate normal assumptions were evaluated using graphical methods on residuals: box-plots, steamleaf and normal probability plots and the Shapiro–Wilks test (Conover, 1980). Bivariate normal distributions were checked using scatter-plots. Homogeneity of variance was evaluated through the F-test when the assumption of normal distribution was not rejected. In most of the chemical variables, the hypothesis of a normal distribution was rejected at the 0·05 level, and scatter-plots of pairs of variables did not show a linear relationship or suggest the presence of outliers. For these reasons, Spearman’s rank correlations were calculated between pairs of chemical variables and between chemical and categorical variables. The Kruskall–Wallis test or the Wilcoxon ranks sum test for independent samples was used to test for differences between “more polluted” and “less polluted” zones as defined by the categories of the “environmental variables”. Principal components based on the correlation matrix of the 12 measured chemical variables, or calculated from them, were obtained. In spite of the large number of correlations calculated and comparisons performed, hypotheses were tested at a probability level of 0·10 because this study is proposed as an explanatory one and the results will be used for the design of future studies. 3. Results and discussion Results on the determinations of the chemical variables in Ligustrum lucidum Ait. leaves are shown in Table 2. 3.1. Table 3 shows the values of the Spearman correlation coefficients between chemical parameters measured in leaves of Ligustrum lucidum Ait. and the variables related to estimates of environmental condition. There were significant correlations of the chemical variables with traffic density, type of industries, position, treeless condition and topographic level. Sulphur content was correlated negatively with type of industry. Trees in areas with most polluting industries (type=4) and lower traffic (traffic=1 and 2) showed foliar sulphur levels significantly lower (median=0·60) than the ones placed in areas with less contaminating industries (type=1) but predominantly dense traffic (traffic=3 and 4) (median=1·14; P=0·072, Wilcoxon ranks sum test). This could be the result of: (a) industries in the area that do not release sulphur compounds into the atmosphere; (b) foliar sulphur accumulation being determined by traffic levels more than types of industry; and (c) the number of samples that determined each environmental situation not being sufficiently representative to detect the correlation between sulphur levels and types of industry. DW/FW ratio correlated with treeless condition. L. lucidum Ait. that stood alone showed higher values of DW/FW ratio (treeless=3, mean±SD: 0·411±0·024) as compared wih neighbouring trees (treeless=1, mean±SD: 0·350±0·048) (P=0·043, Student’s t-test). This ratio has long been used as an important indicator related to leaf
1 2 3 4 5 6 7 8 9 10 11 12
Sample points
0·97 1·74 1·26 1·59 1·62 1·37 1·08 1·13 1·81 1·18 0·96 1·13
Sulphur mg g−1 DW
3·59 3·48 3·55 4·33 3·89 3·85 3·49 4·23 4·15 3·84 3·16 3·58
DW/FW g g−1 FW 1·23 1·63 1·17 1·65 1·73 1·73 1·45 1·85 1·97 1·38 0·97 1·02
Mean±SD 23·15±1·51 26·50±0·98 37·78±3·60 24·72±1·01 28·58±0·00 30·32±1·69 20·05±1·79 27·64±0·41 35·49±3·15 25·07±1·85 56·40±4·46 21·06±0·00
Mean±SD 3·66±0·30 6·03±0·87 8·29±2·51 6·95±0·16 4·32±0·41 6·00±0·04 3·35±0·27 4·08±0·07 4·77±0·18 6·52±2·60 5·98±0·16 2·37±0·52 64·56 80·48 49·42 63·49 81·79 59·53 28·66 75·14 96·48 62·45 85·62 112·2
SLA HPCD MDA Proteins m2 g−1 DW nmol g−1 DW lmol g−1 DW mg g−1 DW Mean±SD 3·79±0·12 3·07±0·06 3·61±0·00 3·25±0·25 2·05±0·19 1·48±0·24 4·17±1·04 2·18±0·00 2·25±0·20 2·59±0·44 3·43±0·15 3·56±0·04
Chl. a mg g−1 DW Mean±SD 3·92±0·50 2·86±0·11 1·77±0·00 2·32±0·00 1·28±0·29 0·62±0·31 2·79±0·03 1·80±0·00 1·05±0·21 1·50±0·61 2·07±0·63 4·63±0·74
Chl. b mg g−1 DW
Mean±SD 7·72±0·37 5·93±0·05 5·38±0·00 5·57±0·25 3·34±0·10 2·10±0·07 6·97±1·07 3·99±0·00 3·30±0·01 4·09±0·17 5·50±0·78 8·19±0·79
Chl. a+b mg g−1 DW
Mean±SD 1·60±0·03 1·26±0·01 1·16±0·01 1·26±0·15 0·65±0·01 0·37±0·06 1·69±0·41 0·71±0·01 0·58±0.08 0·85±0·13 0·99±0·04 1·55±0·11
Carotenoids mg g−1 DW
T 2. Mean values (±standard deviation) of parameters quantified in leaves of Ligustrum lucidum Ait. corresponding to the sampling points in the city of Co´rdoba
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T 3. Spearman correlation coefficient among chemical variables and categorizations corresponding to 12 sampling points in the city of Co´rdoba for Ligustrum lucidum Ait. leaves. Bold coefficients indicate that the correlation was significantly different from zero (∗P
