Photosynthetic Pigments and Heavy Metal Accumulation in Urban Tree Species as Bioindicators of Vehicular Pollution

Photosynthetic Pigments and Heavy Metal Accumulation in Urban Tree Species as Bioindicators of Vehicular Pollution | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Photosynthetic Pigments and Heavy Metal Accumulation in Urban Tree Species as Bioindicators of Vehicular Pollution Francis Kwaku Nkansah, Ebenezer J. D. Belford, Jonathan Nartey Hogarh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6085946/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This study investigated the impact of vehicular pollution on photosynthetic pigments and heavy metal accumulation in four dominant roadside tree species, Albizia lebbeck , Azadirachta indica , Khaya senegalensis , and Senna siamea . Leaf samples were collected along a major arterial road with heavy traffic and compared with those from a low-traffic control road. Photosynthetic pigments (chlorophyll a , chlorophyll b , and carotenoids) were quantified using spectrophotometry, while heavy metal concentrations (Cr, Cu, Fe, Mn, and Ni) were analyzed using inductively coupled plasma-optical emission spectrometry (ICP-OES). The results indicated significant reductions in photosynthetic pigments in leaves under pollution, with A. lebbeck showing the highest reduction in total chlorophyll (91.95%), while S. siamea exhibited minimal reductions (4.73%), indicating species-specific differences in pollution tolerance. Heavy metal concentrations were significantly higher in leaves from polluted road, with K. senegalensis showing the highest chromium uptake (85.71%). Correlation analysis revealed negative associations between heavy metal concentrations and photosynthetic pigments in most species, suggesting oxidative stress-induced pigment degradation. The Metal Accumulation Index (MAI) identified K. senegalensis and A. indica as effective bioindicators for Chromium and Copper pollution, respectively. These findings emphasize the role of urban trees in mitigating vehicular pollution by acting as bioindicators and sinks for heavy metals. The study highlights the importance of selecting pollution-tolerant species for urban greening and phytoremediation efforts, particularly in rapidly urbanizing regions. Bioindicator Heavy metals Photosynthetic pigments Urban trees Vehicular air pollution Introduction The 21st century faces one of its greatest challenges in air pollution, which not only contributes to climate change but also poses serious risks to human health by increasing morbidity and mortality [ 1 ]. Air pollutants linked to human diseases include particulate matter of varying sizes, which are associated with respiratory and cardiovascular diseases, as well as reproductive and central nervous system dysfunctions, and cancer, particularly among urban population [ 1 , 2 ]. Although stratospheric ozone plays a crucial role in protecting the anthroposphere from ultraviolet radiation, elevated concentrations of ozone at ground level are harmful, adversely affecting the respiratory and cardiovascular system. Nonetheless, air pollutants such as volatile organic compounds (VOCs), dioxins, sulphur dioxide (SO₂), nitrogen oxides (NOₓ), and polycyclic aromatic hydrocarbons (PAHs) have been linked to human diseases, including chronic obstructive pulmonary disease (COPD), asthma, lung cancer, and neurological disorders [ 1 , 2 ]. Exposure to elevated levels of carbon monoxide (CO) and lead (Pb) is known to cause direct poisoning by impairing oxygen transport in the bloodstream, potentially leading to neurological and cardiovascular impairments [ 1 , 3 , 4 ]. Climate change, driven by environmental pollution and natural disasters, alters the geographic distribution of infectious diseases by influencing vector habitats through factors like temperature and humidity. The presence of suitable vectors is essential for the emergence of vector-borne pathogens. Worsening climate conditions are expected to have severe impacts on human, animal, and ecosystem health, leading to the emergence and re-emergence of neglected tropical diseases in regions such as Europe and North America [ 1 , 5 ]. Development-induced urbanization and population growth have significantly exacerbated pollution levels across Africa. In the region, open burning, transported dust, and traffic emissions are the primary contributors to air pollution. One of the major sources of transported dust is the Sahara Desert, particularly in West Africa during the Harmattan season [ 6 , 7 ]. In developing nations such as Ghana, the rapid expansion of urban areas and increasing vehicular traffic have resulted in alarming concentrations of airborne pollutants, including particulate matter and heavy metals [ 8 ]. In the Kumasi Metropolis, Agyemang-Bonsu, Dontwi [ 9 ], estimated the emissions of ten different automobile pollutants, all of which showed a steady increase over a five-year period due to rising vehicular pollution. This trend poses a significant threat to air quality, potentially contributing to climatic variability and associated health risks. Similarly, Uka, Belford [ 10 ], reported elevated concentrations of air pollutants, including carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and volatile organic compounds (VOCs), along major arterial roads in Kumasi, with levels exceeding those recorded at control sites. Further supporting these findings, Safo-Adu, Attiogbe [ 11 ], conducted a study in Winneba, where the present research was carried out. Their analysis identified PM₁₀ particles originating from sources such as soil dust, vehicle exhaust, brake and tire wear, and two-stroke engines. These sources contribute to the degradation of air quality, underscoring the pressing need for effective air pollution mitigation strategies. Plants in polluted areas such as roadside, and industries exhibit a number of changes in leaf morphology, physiology, and biochemistry [ 12 – 14 ]. Heavy metals such as chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), and nickel (Ni) from vehicular emissions are readily absorbed by plant leaves, causing physiological stress and reducing photosynthetic efficiency [ 13 , 15 ]. For example, a study conducted in the Kumasi Metropolis noted a reduction in the total chlorophyll and carotenoid pigments in the leaves of Ficus platyphylla and Terminalia catappa tree species under vehicular air pollution [ 16 ], when Cu, Pb, Zn and Cd were assessed in the leaves of these plants. Around the world, researchers have conducted studies on how vehicle-induced air pollution affects the biochemistry, anatomy, and physiology of plants [ 17 – 20 ]. Plant metabolic processes rely on photosynthetic pigments such as chlorophyll a , chlorophyll b , and carotenoids, which are highly susceptible to environmental stressors like heavy metal pollution. Exposure to heavy metals can lead to a reduction in these pigments, indicating oxidative stress within the plant. For instance, a study by Chandra and Kang [ 21 ], have shown that heavy metal exposure leads to significant decreases in photosynthetic pigments in poplar hybrids. Additionally, the accumulation of heavy metals in plant leaves reflects both the level of environmental contamination and the species' capacity for bioaccumulation, making these metrics reliable indicators of pollution impact. Trees can easily accumulate metals, especially in leaf tissue; therefore, they have often been recommended for biomonitoring purposes [ 22 – 24 ]. Urban trees, which serve as natural biofilters, play a critical role in mitigating air pollution by trapping airborne particles and accumulating heavy metals [ 23 , 24 ]. Roadside trees are a promising nature-based solution for enhancing urban environments by providing multiple ecosystem services. They help improve air quality, support urban biodiversity, mitigate the urban heat island effect, enhance psychological well-being, reduce building energy demands, and improve stormwater management [ 25 ]. However, the extent to which different tree species tolerate and respond to such pollutants remains poorly understood, particularly in developing regions like Ghana, where urbanization is accelerating. In this study, we examined the interplay between heavy metal accumulation and photosynthetic pigment dynamics in four tree species exposed to vehicular pollution. By comparing samples from a heavily polluted arterial road with those from a low-traffic control road, we aimed to identify species-specific differences in pollutant tolerance and metal accumulation. Materials and Methods Study area Winneba, located in Ghana's Central Region, is a coastal town bordered by the Gulf of Guinea to the south and surrounded by rolling hills to the north. Geographically, it lies between 5°20′ and 5°35′ north latitudes and 0°37′ and 0°48′ west longitudes, covering an area of approximately 17.5 km² with a population of about 60,000 [ 26 ]. The town experiences a tropical climate characterized by warm and humid conditions, with average annual temperatures ranging from 24°C to 30°C and rainfall averaging 850 mm per year. As a hub for vehicular traffic due to its proximity to major highways, Winneba faces significant air pollution, particularly along its main sites [ 11 , 27 ]. For the study, a major arterial (latitude 05º22.649'N and longitude 000º38.432'W) site in Winneba, of about 3.2 km in length, was selected. The University of Education, Winneba campus, situated at 5°20′17″N latitude and 0°37′31″W longitude, was chosen as a control site due to its low traffic levels. Air quality monitoring in the sampling sites In Auckland, New Zealand, stationary monitoring equipment, EPAM-7500 particle monitors, and Aeroqual Series 500 gas monitors were used to measure the concentrations of PM₂, PM₁₀, CO, and NOₓ, as well as temperature and relative humidity, five days a week for three months (morning: 6:30 am to 8:30 am, afternoon: 12:00 pm to 2:00 pm, and evening: 4:00 pm to 6:00 pm). To assess pollutant fluctuation during periods of high traffic, data collection was purposefully alternated among the chosen roadways, with three replications per session. The 1-minute averages were then added to determine the 2-hour means. To get precise readings, the apparatus was placed about a meter above the ground. Tree species selection Along the major arterial site in Winneba, including the control site, the most dominant and well distributed tree species are Albizia lebbeck, Azadirachta indica, Khaya senegalensis, and Senna siamea [ 28 ] were selected for the study. Using the University of Education, Winneba South campus as the control site, leaf samples for photosynthetic pigment and heavy metal analysis were gathered at along Winneba junction to Windbay Avenue (polluted site). This activity took place from May to July in 2024. Each tree species was sampled in three repetitions with a diameter at breast height (DBH) of greater than 10 cm and a height of 5 to 20 m over a 3.2 km stretch and sampled within a segment of 20 m from the site's side [ 28 ]. Along each site, sampling intervals between each replicated tree species varied from 0.01 km to 1 km. Collection of leave samples Between 7:00 and 9:00 a.m. on each sampling site, approximately 25 physiologically active leaves were collected from branches facing the road, positioned about 1.9 m aboveground level. Sampling was conducted for each of the four tree species, with three leaves collected per individual tree. The leaves were taken from the third position from the tip of the apical bud, resulting in a total sample size of 300 leaves per sampling site. The collected leaves were stored in labeled self-sealing black polythene bags and placed in a polystyrene box with ice for transportation to the laboratory. For pigment analysis, the samples were kept at − 40°C in a deep freezer. Chlorophyll a , chlorophyll b , and carotenoid levels were measured within 24 hours of collection to maintain sample integrity and accuracy. Quantification of chlorophyll and carotenoid pigments The chlorophyll and carotenoid contents were determined using the spectrophotometric method as described by Bojović and Stojanović [ 29 ]. Leaf samples weighing 0.005 g of each tree species replicate were homogenized in 5 ml of 80% acetone solution using a mortar and pestle. The homogenate was then transferred to a test tube and centrifuged at 4000 rpm for 2 minutes using Chalice Centrifuge by Wagtech International. The resulting supernatant was transferred into a 5 ml volumetric flask and made up to 5 ml using 80% acetone. The colour intensity of the green pigment was read using a 721 Vis Spectrophotometer (7052208009, made in England) at wavelengths of 645 nm, 663 nm, and 480 nm for Chl “a”, Chl “b”, and carotenoid respectively, against a blank. The chlorophyll and carotenoid contents were calculated as follows [ 30 , 31 ]: Chlorophyll a = 12.7 (A. 663) – 2.69 (A.645) × V/1000 × W mg/g Chlorophyll b = 22.9 (A.645) – 4.68 (A.663) × V/1000 × W mg/g Total chlorophyll (TCC) = 20.2 (A 645) + 8.02 (A 663) x V/1000 x W Carotenoids = A480 + 11.4(A.663) − 6.38(A.645) × V/1000 x W mg/g Where, A = Absorbance of the extract, V = total volume of extract (ml), and W = weight of fresh leaf (g). Determination of heavy metals Freeze drying Leaf samples for heavy metal analysis were collected from each tree species replicate and were arranged in a single layer on trays. Samples in the tray were made not to exceed the height of the trays. Prior to the freeze drying samples were pre-frozen at 0 o C (i.e. 32 o F) and freeze dried for 24 hrs using HarvestRight Freeze Dryer (Model no. OP-VLU22050-PREM). ICP-OES determination of heavy metal content in leaves After freeze-drying and grinding the samples into powder, 0.5 g of each sample (300 samples, including replicates) was digested using a three-acid mixture: 1.5 ml HClO₃, 5 ml HNO₃, and 5 ml HCl. The digested samples were diluted to a final volume of 25 ml with distilled water, followed by filtration. The filtrates were analyzed for heavy metal content using a method based on the EPA [ 32 ] guidelines. For total recoverable analysis, an aliquot of a well-mixed, homogeneous aqueous sample was accurately measured and processed. To solubilize analytes in aqueous samples containing undissolved material, the samples were gently refluxed with nitric and hydrochloric acids. After cooling, the solution was diluted to the desired volume, thoroughly mixed, and allowed to settle overnight prior to analysis. The analysis utilized inductively coupled plasma optical emission spectrometry (ICP-OES-model no. MY14050009) for multi-element determination. During the procedure, samples were nebulized, and the resulting aerosol was transported to the plasma torch. The plasma, energized by a radio-frequency inductively coupled system, produced element-specific atomic emission spectra. These spectra were dispersed by a grating spectrometer, and the intensities of the emission lines were monitored at specific wavelengths using a photosensitive device. Photocurrents generated by the device were processed and controlled by a computer system. Background was measured adjacent to the analyte wavelength during the analysis to compensate for variable background contribution to the determination of analytes. Quality control and assurance A number of performance tests were conducted to check the instrument reliability which included radial torch alignment: A standard solution containing 10 ppm Mn was used to automatically align the torch viewing position for the highest signal intensity, detector/wavelength calibration: Agilent’s wavelength calibration solution (P/N 6610030100) was used to determine the relationship between the physical settings of the spectrometer and the wavelengths at which it takes the measurement, as well as precision/calibration/accuracy: A blank and five-point calibration curve were generated using the following concentrations: 0.1, 0.2, 0.3 and 0.5ppm standard solution (VWR ICP Quality Control Standard Lot NoB8105086-1A). A linear fit of the curve was chosen in the ICP Expert II software. After analysis of the last standard the R 2 for the calibration curve was 0.99. Quality Control (QC) samples were analyzed to check the accuracy and precision of the calibration curve for each analysis and were used to accept or reject the calibration curve and the batch run. Metal accumulation index (MAI) The metal accumulation index (MAI), which was calculated using the following formula, was used to assess the overall metal deposition on the tree species. $$\:\text{M}\text{A}\text{I}=(1/\text{N})\sum\:_{j=\text{I}}^{N}{I}_{j}$$ where N is the total number of metals analyzed, and the sub-index for variable j is I j = x/dx, which is calculated by dividing each metal's mean value (x) by its standard deviation (dx) [ 33 ]. By applying this index, MAI= ( I Cr + I Cu + I Fe + I Mn + I Ni )/5 = (xCr/dCr + xCu/dCu + xFe/dFe + xMn/dMn + xNi/dNi)/5. Data analysis Descriptive statistics (mean and standard error) were applied to the obtained data, and the independent t test was employed to assess the variations in the photosynthetic contents and heavy metal levels in the leaves of the tree species under study throughout the observational locations. Using SPSS version 26 software, bivariate correlation tests with Pearson's correlation coefficient and two-tailed tests of significance parameters were used to examine correlations between various metal concentrations and metal and pigment variables at significance levels of p < 0.05 and 0.001. The following formula was used to calculate the percentage decrease of heavy metals in the leaves obtained from the polluted sites compared to the leaves of the control site [ 34 – 36 ]. \(\:\%\:reduction=\frac{\text{C}-\text{P}}{\text{C}}\) *100 C = mean value for leaf samples of control site; P = mean value for leaf samples of polluted site. Results and Discussion Ambient air quality during the sampling period The analysis of ambient air quality along the studied roads revealed significant variations in pollutant concentrations and meteorological parameters between the polluted and control sites. Notably, carbon monoxide (CO) levels were significantly higher at the polluted site (2125 ± 182.40 µg/m³) compared to the control (821 ± 485.87 µg/m³), indicating an increase of 159% (Table 1 ). The CO levels observed aligns with findings from previous studies, which attribute heightened CO concentrations near roadsides to vehicular emissions, especially from incomplete fuel combustion in gasoline and diesel engines [ 17 ]. Elevated CO exposure poses severe health risks, including reduced oxygen transport in the bloodstream, leading to cardiovascular and neurological impairments [ 1 , 3 ]. Nitrogen oxides (NOx), a key indicator of vehicular pollution, were also higher at the polluted site (185 ± 95.73 µg/m³) than at the control (168 ± 51.926 µg/m³), reflecting a 10.12% increase. The presence of NOx is strongly linked to traffic density and combustion efficiency in motor vehicles [ 37 , 38 ]. Prolonged exposure to NOx is associated with respiratory issues, including airway inflammation and decreased lung function, particularly among vulnerable populations such as children and the elderly [ 39 , 40 ]. Particulate matter concentrations exhibited a contrasting trend. PM₂.₅ levels were slightly lower at the polluted site (871 ± 79.54 µg/m³) than at the control (902 ± 107.16 µg/m³), marking a decrease of 3.44%. This discrepancy may be attributed to localized factors such as meteorological influences, surface characteristics, or temporary emission fluctuations [ 41 ]. In contrast, PM₁₀ concentrations were higher at the polluted site (931 ± 51.29 µg/m³) compared to the control (874 ± 90.42 µg/m³), showing a 6.52% increase. Increased PM₁₀ levels in high-traffic areas have been widely documented and are often linked to site dust resuspension, tire wear, and emissions from heavy-duty vehicles [ 38 , 42 , 43 ]. Exposure to elevated levels of particulate matter is strongly associated with adverse respiratory and cardiovascular health effects, particularly in urban populations [ 2 ]. Meteorological parameters also exhibited slight variations between the two sites. The average temperature at the polluted site (29.67 ± 2.52°C) was marginally lower than at the control site (30.33 ± 2.88°C), showing a 2.18% decrease. This trend could be influenced by microclimatic factors such as land cover, vegetation presence, or shading effects [ 44 , 45 ]. Conversely, relative humidity was slightly higher at the polluted site (75.67 ± 12.22%) compared to the control (75.33 ± 12.86%), reflecting a 0.45% increase. This minor variation may be linked to differences in local atmospheric conditions and emission-induced changes in humidity levels [ 46 ]. Overall, the results highlight the significant impact of vehicular emissions on ambient air quality, with higher concentrations of CO, NOx, and PM₁₀ in polluted areas. Table 1 Ambient air quality at the study area during the sampling period Sampling sites Parameter CO (µg/m 3 ) NO x (µg/m 3 ) PM 2.5 (µg/m 3 ) PM 10 (µg/m 3 ) Temperature Relative humidity Polluted 2125 ± 182.40 185 ± 95.73 871 ± 79.54 931 ± 51.29 29.67 ± 2.52 75.67 ± 12.22 Control 821 ± 485.87 168 ± 51.926 902 ± 107.16 874 ± 90.42 30.33 ± 2.88 75.33 ± 12.86 % Increase 1.59 10.12 -3.44 6.52 -2.18 0.45 (−) = Values indicate that value of control site is higher than polluted site Chlorophyll and carotenoid content The photosynthetic pigment content in Albizia lebbeck exhibited substantial reductions when exposed to vehicular pollution. The chlorophyll a content decreased by 53.54%, while chlorophyll b and total chlorophyll decreased by 93.35% and 91.95%, respectively, when compared to the control (Table 2 ). Carotenoid content showed a decrease of 87.64%. These reductions are statistically significant for chlorophyll a and b (p < 0.05), indicating a notable decline in pigment synthesis under pollution stress. The significant reduction in chlorophyll pigments in particular is indicative of oxidative stress, which can impair photosynthesis, as suggested by research on the effects of urban pollution on plant physiology [ 13 , 15 , 47 ]. Similarly, the reduction in carotenoids, which serve as antioxidants protecting chlorophyll from oxidative damage, corroborates findings by [ 35 , 48 ], which highlight how pollutants like CO, VOCs, NO x , SO x , respirable suspended particulate matter (RSPM) and suspended particulate matter (SPM) compromise antioxidant systems in plants. In Azadirachta indica , the chlorophyll a content decreased by 67.37%, while chlorophyll b showed a slight decrease of only 5.63%. The total chlorophyll content dropped by 33.72%, and carotenoid levels reduced by 16.52%. These reductions in chlorophyll content were statistically significant for chlorophyll a and total chlorophyll (p 0.05) (Table 2 ). The findings in A. indica suggest that vehicular pollution significantly impacts chlorophyll synthesis, while carotenoids remain relatively stable in comparison. Such a selective reduction in chlorophylls, with carotenoids remaining less affected, could be a mechanism for plants to minimize oxidative damage which is line with a study by Anjum et al. who noted higher antioxidant activity and free amino acid contents in siteside plants and attributed to a probable defensive response to traffic pollutants [ 49 ]. Carotenoids are involved in light-harvesting and photoprotection, which might help plants manage environmental stress. The significant decrease in chlorophyll a and b aligns with earlier studies on the vulnerability of chlorophyll to pollutants, especially in urban environments [ 50 , 51 ]. The Khaya senegalensis data shows a marked reduction in chlorophyll a (52.42%) and b (70.17%) content, as well as total chlorophyll (63.79%) when exposed to vehicular pollution. Carotenoid content decreased by 52.09%, but these reductions were not statistically significant in all cases, with the exception of chlorophyll b (p < 0.05). The observed reductions in chlorophyll content support the findings of Yadav et al. who noted that exposure to particulate matter and vehicular emissions in urban environments often leads to significant reductions in chlorophyll levels, impairing photosynthetic efficiency [ 52 , 53 ]. The decrease in carotenoids, although less pronounced, suggests that the plant’s antioxidant capacity might be compromised under long-term exposure to pollutants, as reported that vehicular pollution leads to a decline in the efficiency of protective pigments in several tree species [ 49 , 54 ]. For Senna siamea , the pigment reductions were much less pronounced, with chlorophyll a reducing by only 1.37%, chlorophyll b by 7.36%, total chlorophyll by 4.73%, and carotenoids by 1.95%. The minimal reductions in pigment content suggest that S. siamea may have a higher tolerance to vehicular pollution compared to the other tree species. The lack of significant change (p > 0.05) in the chlorophyll and carotenoid content might reflect a higher capacity for pigment stability or an efficient adaptive mechanism, as suggested by studies on pollution-tolerant species [ 55 , 56 ]. This result is consistent with research by Banerjee et al. who observed that certain tree species, such as Senna siamea , exhibit higher resilience to environmental stresses, including air pollution, due to their adaptive antioxidant responses and less susceptibility to oxidative stress [ 14 ]. The results of this study indicate that vehicular pollution has a significant impact on the photosynthetic pigments of tree species, with varying levels of susceptibility across species. Albizia lebbeck , Azadirachta indica , and Khaya senegalensis exhibited substantial reductions in chlorophyll and carotenoid contents, which could impair their photosynthetic efficiency and overall health in urban environments. In contrast, Senna siamea showed minimal reductions, suggesting a higher tolerance to vehicular pollution. Changes in assimilation of photosynthetic pigments of selected tree species The alterations in photosynthetic pigment assimilation of selected tree species exposed to vehicular air pollution are presented in Table 3 . The chlorophyll a/b ratio increased at the polluted site (1.48) in Albizia lebbeck compared to the control (0.21), indicating a shift in pigment composition to adapt to stress. However, the total pigment content (Chl a + Chl b + Carotenoid) was significantly reduced at the polluted site (2.58) relative to the control (20.67), which aligns with findings from Talebzadeh and Vale, who reported a decline in chlorophyll content under polluted conditions as a response to oxidative stress [ 57 ]. The slight increase in the chlorophyll (a + b)/carotenoid ratio at the polluted site (0.15) compared to the control (0.13) suggests a compensatory mechanism involving carotenoids for photoprotection. The chlorophyll a/b ratio was lower at the polluted site (0.58) for Azadirachta indica than at the control (1.68), which could indicate stress-induced damage to chlorophyll a, as supported by Singh and Verma [ 58 ]. The total pigment content also decreased from 12.79 at the control to 10.36 at the polluted site, consistent with the findings of Gupta [ 59 ], who observed a reduction in pigment content due to vehicular emissions. Additionally, the chlorophyll (a + b)/carotenoid ratio decreased at the polluted site (0.07), further confirming the reliance on carotenoids to mitigate oxidative damage. For Khaya senegalensis , the chlorophyll a/b ratio increased at the polluted site (2.01) compared to the control (1.26), reflecting a shift in pigment allocation. The reduction in total pigment content at the polluted site (2.27) compared to the control (4.84) aligns with studies by Rai and Panda [ 60 ], who demonstrated significant pigment degradation in tree species under high vehicular pollution. The lower chlorophyll (a + b)/carotenoid ratio at the polluted site (0.13) compared to the control (0.16) suggests enhanced carotenoid activity for photoprotection. With Senna siamea , the chlorophyll a/b ratio showed a slight increase at the polluted site (1.91) relative to the control (1.80). The total pigment content exhibited minimal variation between the polluted (11.41) and control sites (11.67), which may indicate species resilience, as noted by Rai and Panda [ 60 ]. However, the chlorophyll (a + b)/carotenoid ratio was marginally lower at the polluted site (0.14) compared to the control (0.15), highlighting the protective role of carotenoids under pollution stress. These results indicate that vehicular pollution significantly alters photosynthetic pigment assimilation in tree species, with variations in response mechanisms across species. Table 2 Variation in the chlorophyll and carotenoid contents of selected tree species exposed to vehicular pollution Tree species Photosynthetic pigments (mg/g) Sampling sites Chlorophyll “ a ” p value Chlorophyll “ b ” p value Total chlorophyll p value Carotenoid p value A. lebbeck Polluted 0.197 ± 0.17 0.121** 0.133 ± 0.14 0.101** 2.032 ± 1.98 0.142** 2.254 ± 2.01 0.054* Control 0.424 ± 0.39 2.001 ± 1.45 25.246 ± 15.40 18.241 ± 7.27 % R 53.54 93.35 91.95 87.64 A. indica Polluted 0.233 ± 0.20 0.263** 0.402 ± 0.16 0.274** 7.513 ± 3.18 0.386** 9.721 ± 4.40 0.607** Control 0.714 ± 0.36 0.426 ± 0.28 11.336 ± 4.79 11.645 ± 5.39 % R 67.37 5.63 33.72 16.52 K. senegalensis Polluted 0.177 ± 0.06 0.143** 0.088 ± 0.01 0.049* 1.538 ± 0.26 0.080* 2.001 ± 0.61 0.161** Control 0.372 ± 0.36 0.295 ± 0.34 4.248 ± 4.61 4.177 ± 4.13 % R 52.42 70.17 63.79 52.09 S. siamea Polluted 0.939 ± 0.82 0.352** 0.491 ± 0.38 0.349** 8.374 ± 6.81 0.285** 9.984 ± 8.44 0.324** Control 0.952 ± 0.47 0.530 ± 0.25 8.790 ± 3.95 10.183 ± 4.71 % R 1.37 7.36 4.73 1.95 * = significantly different, ** = not significantly different, p = probability value (p < 0.05), % R = percentage reduction Table 3 Alterations in photosynthetic pigments assimilation of selected tree species under vehicular pollution Tree species Chl a / b ratio Chl ‘ a ’ + Chl ‘ b ’ + Carotenoid Chl ( a + b )/Carotenoid Polluted Control Polluted Control Polluted Control A. lebbeck 1.48 0.21 2.58 20.67 0.15 0.13 A. indica 0.58 1.68 10.36 12.79 0.07 0.09 K. senegalensis 2.01 1.26 2.27 4.84 0.13 0.16 S. siamea 1.91 1.80 11.41 11.67 0.14 0.15 Heavy metals in the leaves of trees Leave Cr content and variation in selected tree species The Cr concentration in all tree species was higher at the polluted sites compared to the control, except for Albizia lebbeck and Senna siamea , which showed slight reductions of 23.73% and 11.11%, respectively (Table 4 ). Khaya senegalensis exhibited the most significant increase in Cr concentration (85.71%). This suggests that K. senegalensis has a higher capacity for Cr uptake in polluted environments, making it a potential bioindicator for Cr contamination. Research by Ao, Chen [ 61 ], support the notion that species with enhanced Cr uptake capabilities under polluted conditions can serve as effective monitors of soil and air pollution just as indicated by Sharma, Sodhi [ 62 ]. Leave Cu content and variation in selected tree species The concentration of Cu showed varied responses among species, with Albizia lebbeck and Senna siamea displaying reductions of 15.15% and 2.78%, respectively. On the contrary, Azadirachta indica and Khaya senegalensis demonstrated significant increases of 63.16% and 63.64%, respectively. These findings align with a study conducted by Sharma, Sodhi [ 62 ], who indicated that certain tree species accumulate more Cu due to their differential tolerance and uptake mechanisms, making them useful in phytoremediation of Cu-polluted environments. Leave Fe content and variation in selected tree species The Fe content at polluted sites was inconsistent across species. Albizia lebbeck and Khaya senegalensis experienced reductions of 21.11% and 56.17%, respectively, whereas Azadirachta indica and Senna siamea exhibited increases of 42.69% and 51.77%, respectively. These results indicate that Fe uptake may depend on species-specific physiological mechanisms. Similar findings were reported by Gupta [ 59 ], who noted that Fe availability and accumulation in polluted environments could be influenced by the tree's tolerance and the metal's speciation. Leave Mn content and variation in selected tree species Generally, the concentration of Mn was higher at polluted sites for all species, with increases ranging from 23.29% in Azadirachta indica to 97.89% in Khaya senegalensis . This indicates a consistent trend of Mn accumulation in polluted environments. According to literature, Mn accumulation in tree species at polluted sites may be attributed to the availability of Mn in vehicular emissions and industrial pollutants, making these species effective indicators of Mn contamination [ 18 , 63 ]. Leave Ni content and variation in selected tree species Ni accumulation showed significant increases in all species at polluted sites, with the highest increase observed in Senna siamea (50%). Although Ni concentrations were low across all species, its significant variation between polluted and control sites emphasizes the potential of these species to act as bioindicators for Ni pollution. Literature by Kumar, Kumar [ 18 ] corroborates the bioaccumulation of Ni in tree leaves exposed to vehicular and industrial pollutants. Metal Accumulation Index The Metal Accumulation Index (MAI) was highest in Albizia lebbeck at the control site (4.23), while the lowest MAI at the polluted site was recorded in Senna siamea (2.71). This trend suggests that Albizia lebbeck has a strong baseline accumulation capacity across sites, but its effectiveness in polluted environments diminishes. On the other hand, Khaya senegalensis and Azadirachta indica exhibited increases in MAI, indicating their adaptability to polluted conditions. Several studies have emphasized the significance of MAI in assessing species' overall metal uptake efficiency in polluted versus control environments [ 12 , 64 , 65 ]. The observed variations in heavy metal concentration and MAI across the species emphasize the importance of tree species in monitoring environmental pollution. Species like Khaya senegalensis and Azadirachta indica showed potential as bioindicators for Cr, Cu, and Mn. Table 4 Content of heavy metals (mg/kg) and metal accumulation index (MAI) in leaf samples of selected trees Tree species Heavy metals (mg/kg) MAI Sampling sites Cr p value Cu p value Fe p value Mn p value Ni p value A. lebbeck Polluted 0.73 ± 0.17 0.426** 0.28 ± 0.06 0.113** 360.00 ± 246.83 0.707** 84.96 ± 34.26 0.068** 0.03 ± 0.01 0.016* 3.18 Control 0.59 ± 0.08 0.33 ± 0.16 456.33 ± 218.29 111.13 ± 11.54 0.01 ± 0.00 4.23 % V 23.73 -15.15 -21.11 -23.55 200 A. indica Polluted 0.68 ± 0.22 0.768** 0.31 ± 0.09 0.906** 645.00 ± 430.00 0.441** 75.43 ± 25.77 0.247** 0.04 ± 0.01 0.074** 2.99 Control 0.76 ± 0.28 0.19 ± 0.11 452.00 ± 210.41 61.18 ± 51.73 0.03 ± 0.03 1.75 % V -10.53 63.16 42.69 23.29 33.33 K. senegalensis Polluted 0.65 ± 0.09 0.057** 0.36 ± 0.27 0.679** 392.67 ± 149.54 0.097** 52.68 ± 36.84 0.350** 0.02 ± 0.01 0.057** 2.92 Control 0.35 ± 0.30 0.22 ± 0.26 895.83 ± 792.39 26.62 ± 23.10 0.02 ± 0.03 0.99 % V 85.71 63.64 -56.17 97.89 0 S. siamea Polluted 0.72 ± 0.12 0.214** 0.35 ± 0.19 0.682** 430.00 ± 215.00 0.580** 50.34 ± 29.15 0.139** 0.06 ± 0.03 0.842** 2.71 Control 0.81 ± 0.03 0.36 ± 0.14 283.33 ± 127.12 37.80 ± 9.71 0.04 ± 0.03 7.40 % V -11.11 -2.78 51.77 33.17 50 * = significantly different, ** = not significantly different, p = probability value (p < 0.05), % V = percentage variation; (−) = Values indicate that value of control site is higher than polluted site Correlation between photosynthetic pigments and heavy metal contents The analysis of metal–metal and metal–photosynthetic pigment correlations in the leaves of tree species exposed to vehicular pollution is presented in Table 5 . There were differences in the correlation across the studied metals and pigments, possibly reflecting the differences in uptake, accumulation, and biochemical impact of metals on the photosynthetic pigments of tree species. The Chromium (Cr) content in the leaves of Albizia lebbeck , exhibited a significant positive correlation with total chlorophyll (r = 1.000, p < 0.05) and carotenoids (r = 0.964), suggesting that Cr accumulation may not directly impair the photosynthetic pigment levels in this species. This observation aligns with findings by Sharma, Sodhi [ 62 ], who reported that certain tree species can tolerate moderate Cr levels due to the activation of detoxification pathways [ 62 ]. Conversely, negative correlations were observed between iron (Fe) and both total chlorophyll (r = -0.893) and carotenoids (r = -0.975), implying that Fe accumulation may adversely affect photosynthetic pigment synthesis, as reported by Zahra, Hafeez [ 66 ], where Fe toxicity was shown to disrupt chlorophyll biosynthesis by inducing oxidative stress. In Azadirachta indica , Cr displayed a strong negative correlation with both total chlorophyll (r = -0.953) and carotenoids (r = -0.959), indicating potential Cr-induced phytotoxicity. This is consistent with research by Wakeel, Xu [ 67 ], who observed that excessive Cr concentrations can impair chloroplast structure and function, thereby reducing pigment levels [ 67 ]. Interestingly, Fe exhibited a perfect positive correlation with nickel (Ni) (r = 1.000, p < 0.01), suggesting co-accumulation, which has been noted in plants growing in metal-polluted environments [ 68 , 69 ]. With regard to Khaya senegalensis , Fe showed a strong positive correlation with total chlorophyll (r = 0.991, p < 0.05), and manganese (Mn) exhibited a very high positive correlation with total chlorophyll (r = 0.942). These findings suggest that moderate levels of Fe and Mn could enhance chlorophyll production, potentially due to their roles as cofactors in chlorophyll biosynthesis, as highlighted by Tripathi and Nema [ 64 ]. However, Ni exhibited negative correlations with total chlorophyll (r = -0.747) and carotenoids (r = -0.565), consistent with reports that Ni toxicity can impair photosynthetic efficiency by inhibiting pigment synthesis [ 70 , 71 ]. This study revealed that in Senna siamea , Mn displayed a highly significant positive correlation with carotenoids (r = 1.000, p < 0.01), suggesting that Mn might play a protective role against oxidative stress by enhancing carotenoid content, as previously observed by Reichman [ 72 ] and Srivastava and Dubey [ 73 ]. However, Cr showed a strong negative correlation with both total chlorophyll (r = -0.941) and carotenoids (r = -0.969), further highlighting its detrimental effects on pigment stability. Similarly, Ni exhibited negative correlations with both pigments, indicating that Ni toxicity might exacerbate the effects of Cr on photosynthetic pigments [ 18 ]. The metal–metal correlations revealed interesting co-accumulation patterns. In A. indica , K. senegalensis , and S. siamea , Fe and Cr were positively correlated, suggesting shared uptake pathways or common environmental sources of these metals, such as vehicular emissions [ 74 , 75 ]. Additionally, the positive correlation between Mn and Cu in K. senegalensis (r = 0.959) and between Mn and Fe (r = 0.978) highlights potential synergistic interactions that facilitate metal accumulation. These findings align with earlier studies by Gupta, Yadav [ 76 ], which suggested that certain trace metals may enhance each other's uptake and accumulation under polluted conditions. Overall, the differential correlation patterns across species emphasize the species-specific responses of trees to metal pollution. The strong negative correlations between heavy metals (for instance, Cr, Ni) and photosynthetic pigments highlight their potential toxicity, while the positive correlations for Fe and Mn suggest that these metals may support certain physiological functions when present at moderate levels. Table 5 Correlation between metal contents and photosynthetic pigments in the leaves of tree species exposed to vehicular pollution A. lebbeck A. indica K. senegalensis S. siamea Metal–metal correlations Cu x Cr 0.073 -0.383 -0.655 -0.454 Fe x Cr -0.880 0.877 -0.218 0.478 Fe x Cu 0.410 0.108 0.880 0.566 Mn x Cr 0.868 0.263 -0.415 -0.970 Mn x Cu 0.559 0.790 0.959 0.221 Mn x Fe -0.527 0.694 0.978 -0.679 Ni x Cr -0.857 0.877 0.727 0.635 Ni x Cu -0.577 0.108 -0.995 0.400 Ni x Fe 0.509 1.00** -0.828 0.982 Ni x Mn -1.000* 0.694 -0.926 -0.805 Metal–photosynthetic pigments correlations Cr x Total chlorophyll 1.000* -0.953 -0.087 -0.941 Cu x Total chlorophyll 0.045 0.084 0.810 0.126 Fe x Total chlorophyll -0.893 -0.982 0.991 -0.747 Mn x Total chlorophyll 0.853 -0.545 0.942 0.995 Ni x Total chlorophyll -0.842 -0.982 -0.747 -0.859 Cr x Carotenoid 0.964 -0.959 0.156 -0.969 Cu x Carotenoid -0.196 0.105 0.644 0.219 Fe x Carotenoid -0.975 -0.977 0.930 -0.681 Mn x Carotenoid 0.704 -0.526 0.834 1.000** Ni x Carotenoid -0.688 -0.977 -0.565 -0.807 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Conclusion This study revealed significant reductions in photosynthetic pigments, particularly chlorophyll a , chlorophyll b , and carotenoids, in leaf samples from polluted road, indicating the detrimental impact of vehicular emissions on plant health. Among the species, A. lebbeck exhibited the highest reduction in total chlorophyll content, while S. siamea displayed resilience to pollution-induced stress. Heavy metal accumulation was higher in leaves from polluted road, with significant variations observed across species. The Metal Accumulation Index (MAI) highlighted K. senegalensis and A. indica as effective accumulators of chromium (Cr) and copper (Cu), respectively, making them potential bioindicators for these pollutants. Additionally, the correlation analysis revealed complex interactions between heavy metals and photosynthetic pigments, with some metals like Cr and Ni negatively affecting pigment levels, while others like Fe and Mn showed potential positive associations. Overall, the findings emphasize the critical role of urban trees in mitigating vehicular pollution by serving as sinks for heavy metals and bioindicators for environmental pollution. The study highlights the importance of species selection for urban greening initiatives, as pollution-tolerant species like S. siamea can enhance the resilience of urban ecosystems. Declarations Acknowledgement We acknowledge Environ Research Consult Limited, Accra, Ghana for making available their laboratory facilities. We would like to thank the anonymous reviewers for critically reading the manuscript, comments and suggestions for the improved publication. Funding Declaration There was no funding support. Authors’ Contributions Conceptualization: FKN, EJDB and JNH; Methodology: FKN, EJDB and JNH, AKA, SEA Formal analysis: FKN; Investigation: FKN; Resources: FKN; Data curation: FKN, EJDB, JNH and AKA; Writing—original draft preparation FKN; Writing—review and editing: EJDB and JNH; Supervision: EJDB, JNH, and AKA. All authors have read and approved the published version of the manuscript. Ethical considerations This study did not involve humans or animals as subjects, there was no harm anticipated to human or animal life. Ethical issues (Including plagiarism, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors. Consent to Participate (Not applicable) Consent to Publish (Not applicable) Competing Interests The authors have no competing interests to declare that are relevant to the content of this article. Data statement The research data would be made available upon request. 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D. Belford","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology, PMB, University Post Office","correspondingAuthor":false,"prefix":"","firstName":"Ebenezer","middleName":"J. D.","lastName":"Belford","suffix":""},{"id":423113251,"identity":"4dcb2d95-94dc-4949-80dd-a55b3ad5b247","order_by":2,"name":"Jonathan Nartey Hogarh","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology, PMB, University Post Office","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"Nartey","lastName":"Hogarh","suffix":""},{"id":423113252,"identity":"3364bb30-36db-4184-84a1-227e684a8db2","order_by":3,"name":"Alfred Kwablah Anim","email":"","orcid":"","institution":"Ghana Atomic Energy Commission","correspondingAuthor":false,"prefix":"","firstName":"Alfred","middleName":"Kwablah","lastName":"Anim","suffix":""},{"id":423113253,"identity":"dc999f18-2d81-45b4-ad08-c31ac7cdcfec","order_by":4,"name":"Seyram Elom Achoribo","email":"","orcid":"","institution":"Ghana Atomic Energy Commission","correspondingAuthor":false,"prefix":"","firstName":"Seyram","middleName":"Elom","lastName":"Achoribo","suffix":""}],"badges":[],"createdAt":"2025-02-22 13:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6085946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6085946/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77635034,"identity":"b2171b5c-59cb-44b6-94e4-90ae9cd1ba68","added_by":"auto","created_at":"2025-03-03 18:21:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1490480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6085946/v1/d85daae9-8021-46e9-9046-9a697987af78.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Photosynthetic Pigments and Heavy Metal Accumulation in Urban Tree Species as Bioindicators of Vehicular Pollution","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe 21st century faces one of its greatest challenges in air pollution, which not only contributes to climate change but also poses serious risks to human health by increasing morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Air pollutants linked to human diseases include particulate matter of varying sizes, which are associated with respiratory and cardiovascular diseases, as well as reproductive and central nervous system dysfunctions, and cancer, particularly among urban population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although stratospheric ozone plays a crucial role in protecting the anthroposphere from ultraviolet radiation, elevated concentrations of ozone at ground level are harmful, adversely affecting the respiratory and cardiovascular system. Nonetheless, air pollutants such as volatile organic compounds (VOCs), dioxins, sulphur dioxide (SO₂), nitrogen oxides (NOₓ), and polycyclic aromatic hydrocarbons (PAHs) have been linked to human diseases, including chronic obstructive pulmonary disease (COPD), asthma, lung cancer, and neurological disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Exposure to elevated levels of carbon monoxide (CO) and lead (Pb) is known to cause direct poisoning by impairing oxygen transport in the bloodstream, potentially leading to neurological and cardiovascular impairments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Climate change, driven by environmental pollution and natural disasters, alters the geographic distribution of infectious diseases by influencing vector habitats through factors like temperature and humidity. The presence of suitable vectors is essential for the emergence of vector-borne pathogens. Worsening climate conditions are expected to have severe impacts on human, animal, and ecosystem health, leading to the emergence and re-emergence of neglected tropical diseases in regions such as Europe and North America [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDevelopment-induced urbanization and population growth have significantly exacerbated pollution levels across Africa. In the region, open burning, transported dust, and traffic emissions are the primary contributors to air pollution. One of the major sources of transported dust is the Sahara Desert, particularly in West Africa during the Harmattan season [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In developing nations such as Ghana, the rapid expansion of urban areas and increasing vehicular traffic have resulted in alarming concentrations of airborne pollutants, including particulate matter and heavy metals [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the Kumasi Metropolis, Agyemang-Bonsu, Dontwi [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], estimated the emissions of ten different automobile pollutants, all of which showed a steady increase over a five-year period due to rising vehicular pollution. This trend poses a significant threat to air quality, potentially contributing to climatic variability and associated health risks. Similarly, Uka, Belford [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], reported elevated concentrations of air pollutants, including carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and volatile organic compounds (VOCs), along major arterial roads in Kumasi, with levels exceeding those recorded at control sites. Further supporting these findings, Safo-Adu, Attiogbe [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], conducted a study in Winneba, where the present research was carried out. Their analysis identified PM₁₀ particles originating from sources such as soil dust, vehicle exhaust, brake and tire wear, and two-stroke engines. These sources contribute to the degradation of air quality, underscoring the pressing need for effective air pollution mitigation strategies.\u003c/p\u003e \u003cp\u003ePlants in polluted areas such as roadside, and industries exhibit a number of changes in leaf morphology, physiology, and biochemistry [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Heavy metals such as chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), and nickel (Ni) from vehicular emissions are readily absorbed by plant leaves, causing physiological stress and reducing photosynthetic efficiency [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, a study conducted in the Kumasi Metropolis noted a reduction in the total chlorophyll and carotenoid pigments in the leaves of \u003cem\u003eFicus platyphylla\u003c/em\u003e and \u003cem\u003eTerminalia catappa\u003c/em\u003e tree species under vehicular air pollution [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], when Cu, Pb, Zn and Cd were assessed in the leaves of these plants. Around the world, researchers have conducted studies on how vehicle-induced air pollution affects the biochemistry, anatomy, and physiology of plants [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Plant metabolic processes rely on photosynthetic pigments such as chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e, and carotenoids, which are highly susceptible to environmental stressors like heavy metal pollution. Exposure to heavy metals can lead to a reduction in these pigments, indicating oxidative stress within the plant. For instance, a study by Chandra and Kang [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], have shown that heavy metal exposure leads to significant decreases in photosynthetic pigments in poplar hybrids. Additionally, the accumulation of heavy metals in plant leaves reflects both the level of environmental contamination and the species' capacity for bioaccumulation, making these metrics reliable indicators of pollution impact. Trees can easily accumulate metals, especially in leaf tissue; therefore, they have often been recommended for biomonitoring purposes [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUrban trees, which serve as natural biofilters, play a critical role in mitigating air pollution by trapping airborne particles and accumulating heavy metals [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Roadside trees are a promising nature-based solution for enhancing urban environments by providing multiple ecosystem services. They help improve air quality, support urban biodiversity, mitigate the urban heat island effect, enhance psychological well-being, reduce building energy demands, and improve stormwater management [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the extent to which different tree species tolerate and respond to such pollutants remains poorly understood, particularly in developing regions like Ghana, where urbanization is accelerating. In this study, we examined the interplay between heavy metal accumulation and photosynthetic pigment dynamics in four tree species exposed to vehicular pollution. By comparing samples from a heavily polluted arterial road with those from a low-traffic control road, we aimed to identify species-specific differences in pollutant tolerance and metal accumulation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eWinneba, located in Ghana's Central Region, is a coastal town bordered by the Gulf of Guinea to the south and surrounded by rolling hills to the north. Geographically, it lies between 5\u0026deg;20\u0026prime; and 5\u0026deg;35\u0026prime; north latitudes and 0\u0026deg;37\u0026prime; and 0\u0026deg;48\u0026prime; west longitudes, covering an area of approximately 17.5 km\u0026sup2; with a population of about 60,000 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The town experiences a tropical climate characterized by warm and humid conditions, with average annual temperatures ranging from 24\u0026deg;C to 30\u0026deg;C and rainfall averaging 850 mm per year. As a hub for vehicular traffic due to its proximity to major highways, Winneba faces significant air pollution, particularly along its main sites [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For the study, a major arterial (latitude 05\u0026ordm;22.649'N and longitude 000\u0026ordm;38.432'W) site in Winneba, of about 3.2 km in length, was selected. The University of Education, Winneba campus, situated at 5\u0026deg;20\u0026prime;17\u0026Prime;N latitude and 0\u0026deg;37\u0026prime;31\u0026Prime;W longitude, was chosen as a control site due to its low traffic levels.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAir quality monitoring in the sampling sites\u003c/h3\u003e\n\u003cp\u003eIn Auckland, New Zealand, stationary monitoring equipment, EPAM-7500 particle monitors, and Aeroqual Series 500 gas monitors were used to measure the concentrations of PM₂, PM₁₀, CO, and NOₓ, as well as temperature and relative humidity, five days a week for three months (morning: 6:30 am to 8:30 am, afternoon: 12:00 pm to 2:00 pm, and evening: 4:00 pm to 6:00 pm). To assess pollutant fluctuation during periods of high traffic, data collection was purposefully alternated among the chosen roadways, with three replications per session. The 1-minute averages were then added to determine the 2-hour means. To get precise readings, the apparatus was placed about a meter above the ground.\u003c/p\u003e\n\u003ch3\u003eTree species selection\u003c/h3\u003e\n\u003cp\u003eAlong the major arterial site in Winneba, including the control site, the most dominant and well distributed tree species are \u003cem\u003eAlbizia lebbeck, Azadirachta indica, Khaya senegalensis, and Senna siamea\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] were selected for the study. Using the University of Education, Winneba South campus as the control site, leaf samples for photosynthetic pigment and heavy metal analysis were gathered at along Winneba junction to Windbay Avenue (polluted site). This activity took place from May to July in 2024. Each tree species was sampled in three repetitions with a diameter at breast height (DBH) of greater than 10 cm and a height of 5 to 20 m over a 3.2 km stretch and sampled within a segment of 20 m from the site's side [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Along each site, sampling intervals between each replicated tree species varied from 0.01 km to 1 km.\u003c/p\u003e\n\u003ch3\u003eCollection of leave samples\u003c/h3\u003e\n\u003cp\u003eBetween 7:00 and 9:00 a.m. on each sampling site, approximately 25 physiologically active leaves were collected from branches facing the road, positioned about 1.9 m aboveground level. Sampling was conducted for each of the four tree species, with three leaves collected per individual tree. The leaves were taken from the third position from the tip of the apical bud, resulting in a total sample size of 300 leaves per sampling site. The collected leaves were stored in labeled self-sealing black polythene bags and placed in a polystyrene box with ice for transportation to the laboratory. For pigment analysis, the samples were kept at \u0026minus;\u0026thinsp;40\u0026deg;C in a deep freezer. Chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e, and carotenoid levels were measured within 24 hours of collection to maintain sample integrity and accuracy.\u003c/p\u003e\n\u003ch3\u003eQuantification of chlorophyll and carotenoid pigments\u003c/h3\u003e\n\u003cp\u003eThe chlorophyll and carotenoid contents were determined using the spectrophotometric method as described by Bojović and Stojanović [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Leaf samples weighing 0.005 g of each tree species replicate were homogenized in 5 ml of 80% acetone solution using a mortar and pestle. The homogenate was then transferred to a test tube and centrifuged at 4000 rpm for 2 minutes using Chalice Centrifuge by Wagtech International. The resulting supernatant was transferred into a 5 ml volumetric flask and made up to 5 ml using 80% acetone. The colour intensity of the green pigment was read using a 721 Vis Spectrophotometer (7052208009, made in England) at wavelengths of 645 nm, 663 nm, and 480 nm for Chl \u0026ldquo;a\u0026rdquo;, Chl \u0026ldquo;b\u0026rdquo;, and carotenoid respectively, against a blank. The chlorophyll and carotenoid contents were calculated as follows [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eChlorophyll \u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.7 (A. 663) \u0026ndash; 2.69 (A.645) \u0026times; V/1000 \u0026times; W mg/g\u003c/p\u003e \u003cp\u003eChlorophyll \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.9 (A.645) \u0026ndash; 4.68 (A.663) \u0026times; V/1000 \u0026times; W mg/g\u003c/p\u003e \u003cp\u003eTotal chlorophyll (TCC)\u0026thinsp;=\u0026thinsp;20.2 (A 645)\u0026thinsp;+\u0026thinsp;8.02 (A 663) x V/1000 x W\u003c/p\u003e \u003cp\u003eCarotenoids\u0026thinsp;=\u0026thinsp;A480\u0026thinsp;+\u0026thinsp;11.4(A.663) \u0026minus;\u0026thinsp;6.38(A.645) \u0026times; V/1000 x W mg/g\u003c/p\u003e \u003cp\u003eWhere, A\u0026thinsp;=\u0026thinsp;Absorbance of the extract, V\u0026thinsp;=\u0026thinsp;total volume of extract (ml), and\u003c/p\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;weight of fresh leaf (g).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of heavy metals\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eFreeze drying\u003c/h2\u003e \u003cp\u003eLeaf samples for heavy metal analysis were collected from each tree species replicate and were arranged in a single layer on trays. Samples in the tray were made not to exceed the height of the trays. Prior to the freeze drying samples were pre-frozen at 0\u003csup\u003eo\u003c/sup\u003eC (i.e. 32\u003csup\u003eo\u003c/sup\u003eF) and freeze dried for 24 hrs using HarvestRight Freeze Dryer (Model no. OP-VLU22050-PREM).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eICP-OES determination of heavy metal content in leaves\u003c/h3\u003e\n\u003cp\u003eAfter freeze-drying and grinding the samples into powder, 0.5 g of each sample (300 samples, including replicates) was digested using a three-acid mixture: 1.5 ml HClO₃, 5 ml HNO₃, and 5 ml HCl. The digested samples were diluted to a final volume of 25 ml with distilled water, followed by filtration. The filtrates were analyzed for heavy metal content using a method based on the EPA [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] guidelines. For total recoverable analysis, an aliquot of a well-mixed, homogeneous aqueous sample was accurately measured and processed. To solubilize analytes in aqueous samples containing undissolved material, the samples were gently refluxed with nitric and hydrochloric acids. After cooling, the solution was diluted to the desired volume, thoroughly mixed, and allowed to settle overnight prior to analysis. The analysis utilized inductively coupled plasma optical emission spectrometry (ICP-OES-model no. MY14050009) for multi-element determination. During the procedure, samples were nebulized, and the resulting aerosol was transported to the plasma torch. The plasma, energized by a radio-frequency inductively coupled system, produced element-specific atomic emission spectra. These spectra were dispersed by a grating spectrometer, and the intensities of the emission lines were monitored at specific wavelengths using a photosensitive device. Photocurrents generated by the device were processed and controlled by a computer system. Background was measured adjacent to the analyte wavelength during the analysis to compensate for variable background contribution to the determination of analytes.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQuality control and assurance\u003c/h2\u003e \u003cp\u003eA number of performance tests were conducted to check the instrument reliability which included radial torch alignment: A standard solution containing 10 ppm Mn was used to automatically align the torch viewing position for the highest signal intensity, detector/wavelength calibration: Agilent\u0026rsquo;s wavelength calibration solution (P/N 6610030100) was used to determine the relationship between the physical settings of the spectrometer and the wavelengths at which it takes the measurement, as well as precision/calibration/accuracy: A blank and five-point calibration curve were generated using the following concentrations: 0.1, 0.2, 0.3 and 0.5ppm standard solution (VWR ICP Quality Control Standard Lot NoB8105086-1A). A linear fit of the curve was chosen in the ICP Expert II software. After analysis of the last standard the R\u003csup\u003e2\u003c/sup\u003e for the calibration curve was 0.99. Quality Control (QC) samples were analyzed to check the accuracy and precision of the calibration curve for each analysis and were used to accept or reject the calibration curve and the batch run.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetal accumulation index (MAI)\u003c/h2\u003e \u003cp\u003eThe metal accumulation index (MAI), which was calculated using the following formula, was used to assess the overall metal deposition on the tree species.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{M}\\text{A}\\text{I}=(1/\\text{N})\\sum\\:_{j=\\text{I}}^{N}{I}_{j}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere N is the total number of metals analyzed, and the sub-index for variable \u003cem\u003ej\u003c/em\u003e is \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e = x/dx, which is calculated by dividing each metal's mean value (x) by its standard deviation (dx) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. By applying this index, MAI= (\u003cem\u003eI\u003c/em\u003e\u003csub\u003eCr\u003c/sub\u003e+ \u003cem\u003eI\u003c/em\u003e\u003csub\u003eCu\u003c/sub\u003e + \u003cem\u003eI\u003c/em\u003e\u003csub\u003eFe\u003c/sub\u003e+ \u003cem\u003eI\u003c/em\u003e\u003csub\u003eMn\u003c/sub\u003e+ \u003cem\u003eI\u003c/em\u003e\u003csub\u003eNi\u003c/sub\u003e)/5 = (xCr/dCr\u0026thinsp;+\u0026thinsp;xCu/dCu\u0026thinsp;+\u0026thinsp;xFe/dFe\u0026thinsp;+\u0026thinsp;xMn/dMn\u0026thinsp;+\u0026thinsp;xNi/dNi)/5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics (mean and standard error) were applied to the obtained data, and the independent t test was employed to assess the variations in the photosynthetic contents and heavy metal levels in the leaves of the tree species under study throughout the observational locations. Using SPSS version 26 software, bivariate correlation tests with Pearson's correlation coefficient and two-tailed tests of significance parameters were used to examine correlations between various metal concentrations and metal and pigment variables at significance levels of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and 0.001. The following formula was used to calculate the percentage decrease of heavy metals in the leaves obtained from the polluted sites compared to the leaves of the control site [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\%\\:reduction=\\frac{\\text{C}-\\text{P}}{\\text{C}}\\)\u003c/span\u003e \u003c/span\u003e*100\u003c/p\u003e \u003cp\u003e \u003cem\u003eC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;mean value for leaf samples of control site; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;mean value for leaf samples of polluted site.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAmbient air quality during the sampling period\u003c/h2\u003e \u003cp\u003eThe analysis of ambient air quality along the studied roads revealed significant variations in pollutant concentrations and meteorological parameters between the polluted and control sites. Notably, carbon monoxide (CO) levels were significantly higher at the polluted site (2125\u0026thinsp;\u0026plusmn;\u0026thinsp;182.40 \u0026micro;g/m\u0026sup3;) compared to the control (821\u0026thinsp;\u0026plusmn;\u0026thinsp;485.87 \u0026micro;g/m\u0026sup3;), indicating an increase of 159% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The CO levels observed aligns with findings from previous studies, which attribute heightened CO concentrations near roadsides to vehicular emissions, especially from incomplete fuel combustion in gasoline and diesel engines [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Elevated CO exposure poses severe health risks, including reduced oxygen transport in the bloodstream, leading to cardiovascular and neurological impairments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNitrogen oxides (NOx), a key indicator of vehicular pollution, were also higher at the polluted site (185\u0026thinsp;\u0026plusmn;\u0026thinsp;95.73 \u0026micro;g/m\u0026sup3;) than at the control (168\u0026thinsp;\u0026plusmn;\u0026thinsp;51.926 \u0026micro;g/m\u0026sup3;), reflecting a 10.12% increase. The presence of NOx is strongly linked to traffic density and combustion efficiency in motor vehicles [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Prolonged exposure to NOx is associated with respiratory issues, including airway inflammation and decreased lung function, particularly among vulnerable populations such as children and the elderly [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticulate matter concentrations exhibited a contrasting trend. PM₂.₅ levels were slightly lower at the polluted site (871\u0026thinsp;\u0026plusmn;\u0026thinsp;79.54 \u0026micro;g/m\u0026sup3;) than at the control (902\u0026thinsp;\u0026plusmn;\u0026thinsp;107.16 \u0026micro;g/m\u0026sup3;), marking a decrease of 3.44%. This discrepancy may be attributed to localized factors such as meteorological influences, surface characteristics, or temporary emission fluctuations [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In contrast, PM₁₀ concentrations were higher at the polluted site (931\u0026thinsp;\u0026plusmn;\u0026thinsp;51.29 \u0026micro;g/m\u0026sup3;) compared to the control (874\u0026thinsp;\u0026plusmn;\u0026thinsp;90.42 \u0026micro;g/m\u0026sup3;), showing a 6.52% increase. Increased PM₁₀ levels in high-traffic areas have been widely documented and are often linked to site dust resuspension, tire wear, and emissions from heavy-duty vehicles [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Exposure to elevated levels of particulate matter is strongly associated with adverse respiratory and cardiovascular health effects, particularly in urban populations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeteorological parameters also exhibited slight variations between the two sites. The average temperature at the polluted site (29.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u0026deg;C) was marginally lower than at the control site (30.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u0026deg;C), showing a 2.18% decrease. This trend could be influenced by microclimatic factors such as land cover, vegetation presence, or shading effects [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Conversely, relative humidity was slightly higher at the polluted site (75.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.22%) compared to the control (75.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.86%), reflecting a 0.45% increase. This minor variation may be linked to differences in local atmospheric conditions and emission-induced changes in humidity levels [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Overall, the results highlight the significant impact of vehicular emissions on ambient air quality, with higher concentrations of CO, NOx, and PM₁₀ in polluted areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAmbient air quality at the study area during the sampling period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSampling sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003csub\u003ex\u003c/sub\u003e (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2125\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;182.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185\u0026thinsp;\u0026plusmn;\u0026thinsp;95.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e871\u0026thinsp;\u0026plusmn;\u0026thinsp;79.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e931\u0026thinsp;\u0026plusmn;\u0026thinsp;51.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e821\u0026thinsp;\u0026plusmn;\u0026thinsp;485.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168\u0026thinsp;\u0026plusmn;\u0026thinsp;51.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e902\u0026thinsp;\u0026plusmn;\u0026thinsp;107.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e874\u0026thinsp;\u0026plusmn;\u0026thinsp;90.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(\u0026minus;)\u0026thinsp;=\u0026thinsp;Values indicate that value of control site is higher than polluted site\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eChlorophyll and carotenoid content\u003c/h2\u003e \u003cp\u003eThe photosynthetic pigment content in \u003cem\u003eAlbizia lebbeck\u003c/em\u003e exhibited substantial reductions when exposed to vehicular pollution. The chlorophyll \u003cem\u003ea\u003c/em\u003e content decreased by 53.54%, while chlorophyll \u003cem\u003eb\u003c/em\u003e and total chlorophyll decreased by 93.35% and 91.95%, respectively, when compared to the control (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Carotenoid content showed a decrease of 87.64%. These reductions are statistically significant for chlorophyll \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a notable decline in pigment synthesis under pollution stress. The significant reduction in chlorophyll pigments in particular is indicative of oxidative stress, which can impair photosynthesis, as suggested by research on the effects of urban pollution on plant physiology [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Similarly, the reduction in carotenoids, which serve as antioxidants protecting chlorophyll from oxidative damage, corroborates findings by [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which highlight how pollutants like CO, VOCs, NO\u003csub\u003ex\u003c/sub\u003e, SO\u003csub\u003ex\u003c/sub\u003e, respirable suspended particulate matter (RSPM) and suspended particulate matter (SPM) compromise antioxidant systems in plants.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eAzadirachta indica\u003c/em\u003e, the chlorophyll \u003cem\u003ea\u003c/em\u003e content decreased by 67.37%, while chlorophyll \u003cem\u003eb\u003c/em\u003e showed a slight decrease of only 5.63%. The total chlorophyll content dropped by 33.72%, and carotenoid levels reduced by 16.52%. These reductions in chlorophyll content were statistically significant for chlorophyll \u003cem\u003ea\u003c/em\u003e and total chlorophyll (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but carotenoid content was not significantly affected (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The findings in \u003cem\u003eA. indica\u003c/em\u003e suggest that vehicular pollution significantly impacts chlorophyll synthesis, while carotenoids remain relatively stable in comparison. Such a selective reduction in chlorophylls, with carotenoids remaining less affected, could be a mechanism for plants to minimize oxidative damage which is line with a study by Anjum et al. who noted higher antioxidant activity and free amino acid contents in siteside plants and attributed to a probable defensive response to traffic pollutants [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Carotenoids are involved in light-harvesting and photoprotection, which might help plants manage environmental stress. The significant decrease in chlorophyll \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e aligns with earlier studies on the vulnerability of chlorophyll to pollutants, especially in urban environments [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eKhaya senegalensis\u003c/em\u003e data shows a marked reduction in chlorophyll \u003cem\u003ea\u003c/em\u003e (52.42%) and \u003cem\u003eb\u003c/em\u003e (70.17%) content, as well as total chlorophyll (63.79%) when exposed to vehicular pollution. Carotenoid content decreased by 52.09%, but these reductions were not statistically significant in all cases, with the exception of chlorophyll \u003cem\u003eb\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The observed reductions in chlorophyll content support the findings of Yadav et al. who noted that exposure to particulate matter and vehicular emissions in urban environments often leads to significant reductions in chlorophyll levels, impairing photosynthetic efficiency [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The decrease in carotenoids, although less pronounced, suggests that the plant\u0026rsquo;s antioxidant capacity might be compromised under long-term exposure to pollutants, as reported that vehicular pollution leads to a decline in the efficiency of protective pigments in several tree species [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eSenna siamea\u003c/em\u003e, the pigment reductions were much less pronounced, with chlorophyll \u003cem\u003ea\u003c/em\u003e reducing by only 1.37%, chlorophyll \u003cem\u003eb\u003c/em\u003e by 7.36%, total chlorophyll by 4.73%, and carotenoids by 1.95%. The minimal reductions in pigment content suggest that \u003cem\u003eS. siamea\u003c/em\u003e may have a higher tolerance to vehicular pollution compared to the other tree species. The lack of significant change (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the chlorophyll and carotenoid content might reflect a higher capacity for pigment stability or an efficient adaptive mechanism, as suggested by studies on pollution-tolerant species [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This result is consistent with research by Banerjee et al. who observed that certain tree species, such as \u003cem\u003eSenna siamea\u003c/em\u003e, exhibit higher resilience to environmental stresses, including air pollution, due to their adaptive antioxidant responses and less susceptibility to oxidative stress [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of this study indicate that vehicular pollution has a significant impact on the photosynthetic pigments of tree species, with varying levels of susceptibility across species. \u003cem\u003eAlbizia lebbeck\u003c/em\u003e, \u003cem\u003eAzadirachta indica\u003c/em\u003e, and \u003cem\u003eKhaya senegalensis\u003c/em\u003e exhibited substantial reductions in chlorophyll and carotenoid contents, which could impair their photosynthetic efficiency and overall health in urban environments. In contrast, \u003cem\u003eSenna siamea\u003c/em\u003e showed minimal reductions, suggesting a higher tolerance to vehicular pollution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eChanges in assimilation of photosynthetic pigments of selected tree species\u003c/h2\u003e \u003cp\u003eThe alterations in photosynthetic pigment assimilation of selected tree species exposed to vehicular air pollution are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The chlorophyll a/b ratio increased at the polluted site (1.48) in \u003cem\u003eAlbizia lebbeck\u003c/em\u003e compared to the control (0.21), indicating a shift in pigment composition to adapt to stress. However, the total pigment content (Chl \u003cem\u003ea\u003c/em\u003e\u0026thinsp;+\u0026thinsp;Chl \u003cem\u003eb\u003c/em\u003e\u0026thinsp;+\u0026thinsp;Carotenoid) was significantly reduced at the polluted site (2.58) relative to the control (20.67), which aligns with findings from Talebzadeh and Vale, who reported a decline in chlorophyll content under polluted conditions as a response to oxidative stress [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The slight increase in the chlorophyll (a\u0026thinsp;+\u0026thinsp;b)/carotenoid ratio at the polluted site (0.15) compared to the control (0.13) suggests a compensatory mechanism involving carotenoids for photoprotection.\u003c/p\u003e \u003cp\u003eThe chlorophyll a/b ratio was lower at the polluted site (0.58) for \u003cem\u003eAzadirachta indica\u003c/em\u003e than at the control (1.68), which could indicate stress-induced damage to chlorophyll a, as supported by Singh and Verma [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The total pigment content also decreased from 12.79 at the control to 10.36 at the polluted site, consistent with the findings of Gupta [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], who observed a reduction in pigment content due to vehicular emissions. Additionally, the chlorophyll (a\u0026thinsp;+\u0026thinsp;b)/carotenoid ratio decreased at the polluted site (0.07), further confirming the reliance on carotenoids to mitigate oxidative damage.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eKhaya senegalensis\u003c/em\u003e, the chlorophyll a/b ratio increased at the polluted site (2.01) compared to the control (1.26), reflecting a shift in pigment allocation. The reduction in total pigment content at the polluted site (2.27) compared to the control (4.84) aligns with studies by Rai and Panda [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], who demonstrated significant pigment degradation in tree species under high vehicular pollution. The lower chlorophyll (a\u0026thinsp;+\u0026thinsp;b)/carotenoid ratio at the polluted site (0.13) compared to the control (0.16) suggests enhanced carotenoid activity for photoprotection.\u003c/p\u003e \u003cp\u003eWith \u003cem\u003eSenna siamea\u003c/em\u003e, the chlorophyll a/b ratio showed a slight increase at the polluted site (1.91) relative to the control (1.80). The total pigment content exhibited minimal variation between the polluted (11.41) and control sites (11.67), which may indicate species resilience, as noted by Rai and Panda [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. However, the chlorophyll (a\u0026thinsp;+\u0026thinsp;b)/carotenoid ratio was marginally lower at the polluted site (0.14) compared to the control (0.15), highlighting the protective role of carotenoids under pollution stress. These results indicate that vehicular pollution significantly alters photosynthetic pigment assimilation in tree species, with variations in response mechanisms across species.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariation in the chlorophyll and carotenoid contents of selected tree species exposed to vehicular pollution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTree species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003ePhotosynthetic pigments (mg/g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSampling sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChlorophyll \u0026ldquo;\u003cem\u003ea\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChlorophyll \u0026ldquo;\u003cem\u003eb\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal chlorophyll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCarotenoid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. lebbeck\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.197\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.032\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.142**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.254\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.424\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.001\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.246\u0026thinsp;\u0026plusmn;\u0026thinsp;15.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.241\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. indica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.233\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.263**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.402\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.513\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.386**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.721\u0026thinsp;\u0026plusmn;\u0026thinsp;4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.607**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.714\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.426\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.336\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.645\u0026thinsp;\u0026plusmn;\u0026thinsp;5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. senegalensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.088\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.538\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.080*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.161**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.372\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.295\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.248\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.177\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. siamea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.939\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.352**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.491\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.349**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.374\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.285**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.984\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.324**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.952\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.530\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.790\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.183\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* = significantly different, ** = not significantly different, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;probability value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), % \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;percentage reduction\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlterations in photosynthetic pigments assimilation of selected tree species under vehicular pollution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTree species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eChl \u003cem\u003ea\u003c/em\u003e/\u003cem\u003eb\u003c/em\u003e ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eChl \u0026lsquo;\u003cem\u003ea\u003c/em\u003e\u0026rsquo; + Chl \u0026lsquo;\u003cem\u003eb\u003c/em\u003e\u0026rsquo; + Carotenoid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eChl (\u003cem\u003ea\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eb\u003c/em\u003e)/Carotenoid\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. lebbeck\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. indica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. senegalensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. siamea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eHeavy metals in the leaves of trees\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eLeave Cr content and variation in selected tree species\u003c/h2\u003e \u003cp\u003eThe Cr concentration in all tree species was higher at the polluted sites compared to the control, except for \u003cem\u003eAlbizia lebbeck\u003c/em\u003e and \u003cem\u003eSenna siamea\u003c/em\u003e, which showed slight reductions of 23.73% and 11.11%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eKhaya senegalensis\u003c/em\u003e exhibited the most significant increase in Cr concentration (85.71%). This suggests that \u003cem\u003eK. senegalensis\u003c/em\u003e has a higher capacity for Cr uptake in polluted environments, making it a potential bioindicator for Cr contamination. Research by Ao, Chen [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], support the notion that species with enhanced Cr uptake capabilities under polluted conditions can serve as effective monitors of soil and air pollution just as indicated by Sharma, Sodhi [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLeave Cu content and variation in selected tree species\u003c/h2\u003e \u003cp\u003eThe concentration of Cu showed varied responses among species, with \u003cem\u003eAlbizia lebbeck\u003c/em\u003e and \u003cem\u003eSenna siamea\u003c/em\u003e displaying reductions of 15.15% and 2.78%, respectively. On the contrary, \u003cem\u003eAzadirachta indica\u003c/em\u003e and \u003cem\u003eKhaya senegalensis\u003c/em\u003e demonstrated significant increases of 63.16% and 63.64%, respectively. These findings align with a study conducted by Sharma, Sodhi [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], who indicated that certain tree species accumulate more Cu due to their differential tolerance and uptake mechanisms, making them useful in phytoremediation of Cu-polluted environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLeave Fe content and variation in selected tree species\u003c/h2\u003e \u003cp\u003eThe Fe content at polluted sites was inconsistent across species. \u003cem\u003eAlbizia lebbeck\u003c/em\u003e and \u003cem\u003eKhaya senegalensis\u003c/em\u003e experienced reductions of 21.11% and 56.17%, respectively, whereas \u003cem\u003eAzadirachta indica\u003c/em\u003e and \u003cem\u003eSenna siamea\u003c/em\u003e exhibited increases of 42.69% and 51.77%, respectively. These results indicate that Fe uptake may depend on species-specific physiological mechanisms. Similar findings were reported by Gupta [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], who noted that Fe availability and accumulation in polluted environments could be influenced by the tree's tolerance and the metal's speciation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLeave Mn content and variation in selected tree species\u003c/h2\u003e \u003cp\u003eGenerally, the concentration of Mn was higher at polluted sites for all species, with increases ranging from 23.29% in \u003cem\u003eAzadirachta indica\u003c/em\u003e to 97.89% in \u003cem\u003eKhaya senegalensis\u003c/em\u003e. This indicates a consistent trend of Mn accumulation in polluted environments. According to literature, Mn accumulation in tree species at polluted sites may be attributed to the availability of Mn in vehicular emissions and industrial pollutants, making these species effective indicators of Mn contamination [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLeave Ni content and variation in selected tree species\u003c/h2\u003e \u003cp\u003eNi accumulation showed significant increases in all species at polluted sites, with the highest increase observed in \u003cem\u003eSenna siamea\u003c/em\u003e (50%). Although Ni concentrations were low across all species, its significant variation between polluted and control sites emphasizes the potential of these species to act as bioindicators for Ni pollution. Literature by Kumar, Kumar [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] corroborates the bioaccumulation of Ni in tree leaves exposed to vehicular and industrial pollutants.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMetal Accumulation Index\u003c/h2\u003e \u003cp\u003eThe Metal Accumulation Index (MAI) was highest in \u003cem\u003eAlbizia lebbeck\u003c/em\u003e at the control site (4.23), while the lowest MAI at the polluted site was recorded in \u003cem\u003eSenna siamea\u003c/em\u003e (2.71). This trend suggests that \u003cem\u003eAlbizia lebbeck\u003c/em\u003e has a strong baseline accumulation capacity across sites, but its effectiveness in polluted environments diminishes. On the other hand, \u003cem\u003eKhaya senegalensis\u003c/em\u003e and \u003cem\u003eAzadirachta indica\u003c/em\u003e exhibited increases in MAI, indicating their adaptability to polluted conditions. Several studies have emphasized the significance of MAI in assessing species' overall metal uptake efficiency in polluted versus control environments [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The observed variations in heavy metal concentration and MAI across the species emphasize the importance of tree species in monitoring environmental pollution. Species like \u003cem\u003eKhaya senegalensis\u003c/em\u003e and \u003cem\u003eAzadirachta indica\u003c/em\u003e showed potential as bioindicators for Cr, Cu, and Mn.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContent of heavy metals (mg/kg) and metal accumulation index (MAI) in leaf samples of selected trees\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTree species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e \u003cp\u003eHeavy metals (mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMAI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSampling\u003c/p\u003e \u003cp\u003esites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003elebbeck\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.426**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e360.00\u0026thinsp;\u0026plusmn;\u0026thinsp;246.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.707**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84.96\u0026thinsp;\u0026plusmn;\u0026thinsp;34.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.068**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.016*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e456.33\u0026thinsp;\u0026plusmn;\u0026thinsp;218.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e111.13\u0026thinsp;\u0026plusmn;\u0026thinsp;11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-21.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-23.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. indica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.906**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e645.00\u0026thinsp;\u0026plusmn;\u0026thinsp;430.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.441**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.43\u0026thinsp;\u0026plusmn;\u0026thinsp;25.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.247**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.074**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e452.00\u0026thinsp;\u0026plusmn;\u0026thinsp;210.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.18\u0026thinsp;\u0026plusmn;\u0026thinsp;51.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e33.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. senegalensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.679**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e392.67\u0026thinsp;\u0026plusmn;\u0026thinsp;149.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.097**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.68\u0026thinsp;\u0026plusmn;\u0026thinsp;36.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.350**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.057**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e895.83\u0026thinsp;\u0026plusmn;\u0026thinsp;792.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.62\u0026thinsp;\u0026plusmn;\u0026thinsp;23.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-56.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003esiamea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.682**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e430.00\u0026thinsp;\u0026plusmn;\u0026thinsp;215.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.580**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.34\u0026thinsp;\u0026plusmn;\u0026thinsp;29.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.139**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.842**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e283.33\u0026thinsp;\u0026plusmn;\u0026thinsp;127.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.80\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e* = significantly different, ** = not significantly different, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;probability value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), % \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;percentage variation; (\u0026minus;)\u0026thinsp;=\u0026thinsp;Values indicate that value of control site is higher than polluted site\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation between photosynthetic pigments and heavy metal contents\u003c/h2\u003e \u003cp\u003eThe analysis of metal\u0026ndash;metal and metal\u0026ndash;photosynthetic pigment correlations in the leaves of tree species exposed to vehicular pollution is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. There were differences in the correlation across the studied metals and pigments, possibly reflecting the differences in uptake, accumulation, and biochemical impact of metals on the photosynthetic pigments of tree species.\u003c/p\u003e \u003cp\u003eThe Chromium (Cr) content in the leaves of \u003cem\u003eAlbizia lebbeck\u003c/em\u003e, exhibited a significant positive correlation with total chlorophyll (r\u0026thinsp;=\u0026thinsp;1.000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and carotenoids (r\u0026thinsp;=\u0026thinsp;0.964), suggesting that Cr accumulation may not directly impair the photosynthetic pigment levels in this species. This observation aligns with findings by Sharma, Sodhi [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], who reported that certain tree species can tolerate moderate Cr levels due to the activation of detoxification pathways [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Conversely, negative correlations were observed between iron (Fe) and both total chlorophyll (r = -0.893) and carotenoids (r = -0.975), implying that Fe accumulation may adversely affect photosynthetic pigment synthesis, as reported by Zahra, Hafeez [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], where Fe toxicity was shown to disrupt chlorophyll biosynthesis by inducing oxidative stress.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eAzadirachta indica\u003c/em\u003e, Cr displayed a strong negative correlation with both total chlorophyll (r = -0.953) and carotenoids (r = -0.959), indicating potential Cr-induced phytotoxicity. This is consistent with research by Wakeel, Xu [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], who observed that excessive Cr concentrations can impair chloroplast structure and function, thereby reducing pigment levels [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Interestingly, Fe exhibited a perfect positive correlation with nickel (Ni) (r\u0026thinsp;=\u0026thinsp;1.000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting co-accumulation, which has been noted in plants growing in metal-polluted environments [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith regard to \u003cem\u003eKhaya senegalensis\u003c/em\u003e, Fe showed a strong positive correlation with total chlorophyll (r\u0026thinsp;=\u0026thinsp;0.991, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and manganese (Mn) exhibited a very high positive correlation with total chlorophyll (r\u0026thinsp;=\u0026thinsp;0.942). These findings suggest that moderate levels of Fe and Mn could enhance chlorophyll production, potentially due to their roles as cofactors in chlorophyll biosynthesis, as highlighted by Tripathi and Nema [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. However, Ni exhibited negative correlations with total chlorophyll (r = -0.747) and carotenoids (r = -0.565), consistent with reports that Ni toxicity can impair photosynthetic efficiency by inhibiting pigment synthesis [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study revealed that in \u003cem\u003eSenna siamea\u003c/em\u003e, Mn displayed a highly significant positive correlation with carotenoids (r\u0026thinsp;=\u0026thinsp;1.000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that Mn might play a protective role against oxidative stress by enhancing carotenoid content, as previously observed by Reichman [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] and Srivastava and Dubey [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. However, Cr showed a strong negative correlation with both total chlorophyll (r = -0.941) and carotenoids (r = -0.969), further highlighting its detrimental effects on pigment stability. Similarly, Ni exhibited negative correlations with both pigments, indicating that Ni toxicity might exacerbate the effects of Cr on photosynthetic pigments [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe metal\u0026ndash;metal correlations revealed interesting co-accumulation patterns. In \u003cem\u003eA. indica\u003c/em\u003e, \u003cem\u003eK. senegalensis\u003c/em\u003e, and \u003cem\u003eS. siamea\u003c/em\u003e, Fe and Cr were positively correlated, suggesting shared uptake pathways or common environmental sources of these metals, such as vehicular emissions [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Additionally, the positive correlation between Mn and Cu in \u003cem\u003eK. senegalensis\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.959) and between Mn and Fe (r\u0026thinsp;=\u0026thinsp;0.978) highlights potential synergistic interactions that facilitate metal accumulation. These findings align with earlier studies by Gupta, Yadav [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], which suggested that certain trace metals may enhance each other's uptake and accumulation under polluted conditions.\u003c/p\u003e \u003cp\u003eOverall, the differential correlation patterns across species emphasize the species-specific responses of trees to metal pollution. The strong negative correlations between heavy metals (for instance, Cr, Ni) and photosynthetic pigments highlight their potential toxicity, while the positive correlations for Fe and Mn suggest that these metals may support certain physiological functions when present at moderate levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between metal contents and photosynthetic pigments in the leaves of tree species exposed to vehicular pollution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eA. lebbeck\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eA. indica\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eK. senegalensis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eS. siamea\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal\u0026ndash;metal correlations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu x Cr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe x Cr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe x Cu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn x Cr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn x Cu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn x Fe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi x Cr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi x Cu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi x Fe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi x Mn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal\u0026ndash;photosynthetic pigments correlations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr x Total chlorophyll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu x Total chlorophyll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe x Total chlorophyll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn x Total chlorophyll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi x Total chlorophyll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr x Carotenoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu x Carotenoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe x Carotenoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn x Carotenoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi x Carotenoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e \u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed significant reductions in photosynthetic pigments, particularly chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e, and carotenoids, in leaf samples from polluted road, indicating the detrimental impact of vehicular emissions on plant health. Among the species, \u003cem\u003eA. lebbeck\u003c/em\u003e exhibited the highest reduction in total chlorophyll content, while \u003cem\u003eS. siamea\u003c/em\u003e displayed resilience to pollution-induced stress. Heavy metal accumulation was higher in leaves from polluted road, with significant variations observed across species. The Metal Accumulation Index (MAI) highlighted \u003cem\u003eK. senegalensis\u003c/em\u003e and \u003cem\u003eA. indica\u003c/em\u003e as effective accumulators of chromium (Cr) and copper (Cu), respectively, making them potential bioindicators for these pollutants. Additionally, the correlation analysis revealed complex interactions between heavy metals and photosynthetic pigments, with some metals like Cr and Ni negatively affecting pigment levels, while others like Fe and Mn showed potential positive associations. Overall, the findings emphasize the critical role of urban trees in mitigating vehicular pollution by serving as sinks for heavy metals and bioindicators for environmental pollution. The study highlights the importance of species selection for urban greening initiatives, as pollution-tolerant species like \u003cem\u003eS. siamea\u003c/em\u003e can enhance the resilience of urban ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge Environ Research Consult Limited, Accra, Ghana for making available their laboratory facilities. We would like to thank the anonymous reviewers for critically reading the manuscript, comments and suggestions for the improved publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: FKN, EJDB and JNH; Methodology: FKN, EJDB and JNH, AKA, SEA Formal analysis: FKN; Investigation: FKN; Resources: FKN; Data curation: FKN, EJDB, JNH and AKA; Writing—original draft preparation FKN; Writing—review and editing: EJDB and JNH; Supervision: EJDB, JNH, and AKA. All authors have read and approved the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve humans or animals as subjects, there was no harm anticipated to human or animal life. Ethical issues (Including plagiarism, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003e(Not applicable)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003e(Not applicable)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data would be made available upon request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eManisalidis, I., et al., \u003cem\u003eEnvironmental and health impacts of air pollution: a review\u003c/em\u003e. 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Assoc., 2020. 19(1): p. 56\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafo-Adu, G., et al., \u003cem\u003eAssessment of Chemical Composition and Health Implications Associated with PM10 Exposure: A Comparative Study of an Urban Road Intersection and a Wood-Burning Vicinity.\u003c/em\u003e Environmental Forensics, 2024: p. 1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel, K., M. Chaurasia, and K.S. Rao, \u003cem\u003eHeavy metal accumulation in leaves of selected plant species in urban areas of Delhi\u003c/em\u003e. Environmental Science and Pollution Research, 2023. 30(10): p. 27622\u0026ndash;27635.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaurasia, M., et al., \u003cem\u003eImpact of dust accumulation on the physiological functioning of selected herbaceous plants of Delhi, India\u003c/em\u003e. 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Science of the Total Environment, 2014. 476: p. 522\u0026ndash;531.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta, N., et al., \u003cem\u003eTrace elements in soil-vegetables interface: translocation, bioaccumulation, toxicity and amelioration-a review\u003c/em\u003e. Science of the Total Environment, 2019. 651: p. 2927\u0026ndash;2942.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bioindicator, Heavy metals, Photosynthetic pigments, Urban trees, Vehicular air pollution","lastPublishedDoi":"10.21203/rs.3.rs-6085946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6085946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the impact of vehicular pollution on photosynthetic pigments and heavy metal accumulation in four dominant roadside tree species, \u003cem\u003eAlbizia lebbeck\u003c/em\u003e, \u003cem\u003eAzadirachta indica\u003c/em\u003e, \u003cem\u003eKhaya senegalensis\u003c/em\u003e, and \u003cem\u003eSenna siamea\u003c/em\u003e. Leaf samples were collected along a major arterial road with heavy traffic and compared with those from a low-traffic control road. Photosynthetic pigments (chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e, and carotenoids) were quantified using spectrophotometry, while heavy metal concentrations (Cr, Cu, Fe, Mn, and Ni) were analyzed using inductively coupled plasma-optical emission spectrometry (ICP-OES). The results indicated significant reductions in photosynthetic pigments in leaves under pollution, with \u003cem\u003eA. lebbeck\u003c/em\u003e showing the highest reduction in total chlorophyll (91.95%), while \u003cem\u003eS. siamea\u003c/em\u003e exhibited minimal reductions (4.73%), indicating species-specific differences in pollution tolerance. Heavy metal concentrations were significantly higher in leaves from polluted road, with \u003cem\u003eK. senegalensis\u003c/em\u003e showing the highest chromium uptake (85.71%). Correlation analysis revealed negative associations between heavy metal concentrations and photosynthetic pigments in most species, suggesting oxidative stress-induced pigment degradation. The Metal Accumulation Index (MAI) identified \u003cem\u003eK. senegalensis\u003c/em\u003e and \u003cem\u003eA. indica\u003c/em\u003e as effective bioindicators for Chromium and Copper pollution, respectively. These findings emphasize the role of urban trees in mitigating vehicular pollution by acting as bioindicators and sinks for heavy metals. The study highlights the importance of selecting pollution-tolerant species for urban greening and phytoremediation efforts, particularly in rapidly urbanizing regions.\u003c/p\u003e","manuscriptTitle":"Photosynthetic Pigments and Heavy Metal Accumulation in Urban Tree Species as Bioindicators of Vehicular Pollution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-03 18:13:07","doi":"10.21203/rs.3.rs-6085946/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-17T12:34:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-15T18:43:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-12T20:00:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-11T18:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147197752762707538150062391402341640330","date":"2025-03-05T05:38:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203541605258840070449613717837586887221","date":"2025-03-04T23:21:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68876412252344790328291701610496418101","date":"2025-03-03T08:57:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99869835016582529967375970998453657547","date":"2025-03-03T07:23:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-03T05:19:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-28T05:41:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-27T10:48:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2025-02-22T13:45:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d31c537-5dd1-4424-a60c-282b17622660","owner":[],"postedDate":"March 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-05T11:08:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-03 18:13:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6085946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6085946","identity":"rs-6085946","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}