Health Status of Street Trees Along the Urban–Rural Gradient in the Garden City of Bengaluru, India

Health Status of Street Trees Along the Urban–Rural Gradient in the Garden City of Bengaluru, India | 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 Health Status of Street Trees Along the Urban–Rural Gradient in the Garden City of Bengaluru, India Baragur Neelappa Divakara, CU Nikhitha, VP Tewari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6946956/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urban forestry involves the strategic management of trees within urban landscapes to enhance ecosystem services, improve biodiversity, mitigate climate change, and reduce the urban heat island effect. In addition to their ecological value, urban trees contribute aesthetic, economic, and functional benefits—such as increasing property value and attracting tourism. This study aimed to assess the health of urban trees exposed to various anthropogenic pressures in Bengaluru city. A total of 48 one-hectare plots were established—23 in the northern and 25 in the southern transects of the city. Trees with a diameter at breast height (DBH) of ≥10 cm were selected for analysis. Based on the extent of tree cover and built-up area, plots were categorized into urban, transition, and rural zones. Common tree species from each zone were evaluated for their Air Pollution Tolerance Index (APTI) in both transects. Fresh leaf samples were collected and analyzed for four key biochemical parameters: leaf pH, ascorbic acid content, relative water content, and total chlorophyll content. The results revealed that some species exhibited greater tolerance to air pollution. In the northern transect, Mangifera indica , Psidium guajava , Ficus glomerata , and Pongamia pinnata demonstrated higher APTI values, indicating greater tolerance. In the southern transect, Mangifera indica , Ficus tinctoria , Commiphora caudata , and Alangium salviifolium were identified as more tolerant. This study underscores the adaptive capacity of specific tree species to varying pollution levels and offers valuable insights for urban greening initiatives, species selection, and sustainable city planning aimed at enhancing ecological resilience and air quality in metropolitan areas. Urban–Rural gradient Tree Health Air Pollution Tolerance Index (APTI) Tolerant species Bengaluru Figures Figure 1 Figure 2 Figure 3 Introduction Urbanization is a continuous phenomenon observed in both developing and developed nations [ 48 ]. It typically refers to the demographic shift from rural areas to urban centres, influenced by a combination of economic, political, and geographical factors. Bengaluru, a major South Indian metropolis also known as the "Silicon Valley of India," has undergone rapid urbanization characterized by unplanned and uncontrolled development, demographic expansion, heterogeneous land use, deforestation, and various anthropogenic activities [ 46 , 29 ]. Originally a modest village in the 12th century, Bengaluru has evolved into one of India's fastest-growing cities by the 21st century, now ranking as the second-fastest-growing and fifth-largest metropolis in the country [ 35 , 46 ]. However, this rapid transformation has adversely impacted the region’s biodiversity and ecosystem services, particularly through the reduction of green spaces and vegetation cover required to support the growing population [ 34 , 28 ]. Urban green spaces are critical for delivering a range of ecological, environmental, economic, and social benefits. These include filtering airborne pollutants and dust [ 24 , 6 ], mitigating the urban heat island effect and climate change [ 13 ], acting as noise barriers, alleviating urban floods, and enhancing biodiversity and ecosystem functionality [ 11 , 36 ]. Additionally, they contribute to the hydrological cycle by recharging groundwater aquifers and stabilizing the urban climate [ 31 ]. Despite increasing scientific evidence over the past two decades highlighting the importance of urban green spaces, city planners and managers often undervalue the role of trees in maintaining urban ecological integrity [ 26 , 17 ]. Effective conservation of urban biodiversity necessitates detailed data on tree species composition, including the influence of urbanization on species nativity. Notably, an increase in urban land cover is often associated with a rise in non-native species richness [ 4 , 5 ]. Urban trees in Bengaluru face a range of anthropogenic stresses such as drought, impermeable surfaces restricting root growth, pest infestations, urban heat, humidity, and mechanical damage due to infrastructure development [ 43 ]. A study revealed that only 42% of the trees are native species, with four out of five being exotic [ 27 ]. Historically, Bengaluru was celebrated for its lush greenery and was famously dubbed the “Garden City of India,” a legacy attributed to Krishnaraja Wodeyar IV, who commissioned the creation of iconic green spaces such as Lalbagh and Cubbon Park. The city once boasted around 705 parks [ 10 ] and 200 additional green spaces (e.g., roadside and avenue trees), many of which have been lost due to urban development pressures [ 18 ]. Compared to surrounding rural areas, Bengaluru exhibits high species richness and diversity, largely due to the prevalence of exotic species. These urban green spaces serve as the city's “lungs,” contributing to pollution control, biodiversity conservation, and improved ecosystem services [ 45 ]. However, infrastructure projects like road widening and encroachments have led to a significant decline in large, mature canopy trees, exacerbating the urban heat island effect [ 26 ]. Urban trees are also vulnerable to vehicular pollution. Between 2005 and 2011, air pollution levels in the city fluctuated between critical and high at various monitoring sites. The Air Pollution Tolerance Index (APTI) is a valuable tool for assessing a tree's tolerance to urban air pollutants, based on physiological and biochemical parameters such as chlorophyll content, stomatal conductance, and relative water content of leaves. APTI is especially useful in assessing tree resilience to pollutants like nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and particulate matter (PM). Major sources of SO₂, NO₂, RSPM, and PM₁₀ include vehicular emissions from fossil fuel combustion and industrial activities [ 12 ]. Previous assessments have reported that the estimated tree crown cover in Bengaluru stood at approximately 19.9%, with a per capita green space availability of around 17 m² [ 7 , 44 ]. Given these findings, vegetation cover is a vital component of urban landscapes, essential for maintaining ecological balance and supporting human well-being. This study aims to evaluate the health of street trees across various urban-to-rural zones in Bengaluru using APTI as a key metric. By analyzing APTI values across the urban-rural gradient, the study seeks to understand how different tree species respond to varying pollution levels and how air pollution affects their overall health. These insights can inform urban planning and green space management strategies to mitigate pollution impacts and improve the city’s ecological health. Methodology Bengaluru, the principal administrative capital of Karnataka, is geographically located in the southeastern part of the Indian peninsula, at coordinates 77°37'19.54" E and 12°59'09.76" N. The Greater Bengaluru area spans approximately 741 km² and falls under the jurisdiction of the Bruhat Bengaluru Mahanagara Palike (BBMP), which governs 198 wards across 8 zones. The city’s spatial extent has undergone substantial urban expansion—growing more than tenfold from 69 km² in 1949 to 741 km² in 2006. The geographical boundaries of Bengaluru extend from 12°49'5" N to 13°8'32" N and from 77°27'29" E to 77°47'2" E. Bengaluru’s population within BBMP limits witnessed a remarkable decadal growth of 47.18%, increasing from 6.53 million in 2001 to approximately 9.62 million in 2011. Consequently, population density surged from 10,732 persons/km² in 2001 to 13,392 persons/km² in 2011 [35]. Topographically, Bengaluru is situated at altitudes ranging between 740 m and 960 m above mean sea level. The city experiences an average annual rainfall of about 880 mm, typically spread over approximately 60 rainy days. Summer temperatures often exceed 38°C, whereas winter temperatures average around 25°C, contributing to the city’s overall pleasant climate year-round. Positioned on a ridge, Bengaluru is drained by three principal valley systems: Hebbal-Nagavara, Koramangala-Challaghatta (K&C), and Vrishabhavathi. These valley systems facilitate drainage into major rivers such as the east-flowing Arkavathi and Pinakini (Pennar), and the west-flowing Shimsha, a tributary of the Cauvery. Historically, embankments constructed along these valleys gave rise to a network of tanks and lakes, which served irrigation and water storage purposes. While the low-lying areas are dominated by water bodies of varying sizes, the middle, northern, and eastern regions feature undulating terrain with scrub-covered highlands. The southern part is characterized by rugged hills surrounded by forests and scrub jungles. Geologically, the Bengaluru region is predominantly underlain by granitic and gneissic formations, interspersed with dolerites, dykes, and schist rocks [40]. Sampling and Data collection The study was conducted on trees located in the northern and southern parts of Bengaluru city. A total of 48 plots, each measuring 100 m × 100 m (1 ha), were established—23 in the north and 25 in the south—along transects defined by the Indo-German research consortium (Fig. 1 & 2). Plot selection was based on a stratified random sampling approach, with two strata: “built-up” and “others.” The “built-up” stratum was characterized by areas with more than 50% impervious surface, as identified from WorldView-3 satellite imagery (Fig. 1 & 2). All selected plots are within the “built-up” stratum, as the primary focus of the study was on urbanized areas. For the study, plots delineated along the transects were categorized into urban, transition, and rural zones based on the proportion of built-up pixels derived from WorldView-3 satellite imagery. Grids with more than 50% built-up pixels were classified as urban , those with 10–50% as transition , and those with 0–10% as rural . The distance of each plot from the city center was also considered as a supplementary criterion for classification. A total of 23 plots along the northern transect and 25 plots along the southern transect were surveyed in 2017 and 2018, respectively. The transects extended toward Doddaballapura in the north and Kanakapura in the south of Bengaluru. Using the geographic coordinates of each location, one-hectare (100 m × 100 m) plots were identified in the field using GPS. Distinct natural and anthropogenic features—such as buildings, roads, and tree arrangements—were used as reference markers to verify whether individual trees fell within or outside the plot boundaries. All trees within the plots were manually numbered with enamel paint on their stems to enable future identification and monitoring. To achieve the study objectives, air quality data were obtained from monitoring stations operated by the Karnataka State Pollution Control Board (KSPCB) and the Central Pollution Control Board (CPCB). Data on particulate matter (PM₁₀ and PM₂.₅), sulfur oxides (SOₓ), and nitrogen oxides (NOₓ) were regularly collected from stations located in proximity to the plots. To assess pollution-induced stress on urban vegetation, the Air Pollution Tolerance Index (APTI) was calculated for tree species across different land-use categories. From the northern transect, 15 common tree species representing urban, transition, and rural plots were selected for APTI analysis. Similarly, 16 common tree species were selected from the southern transect. The APTI procedure was standardized, and leaf samples were analyzed on a day-to-day basis. Samples were collected from trees consistently exposed to vehicular emissions and other anthropogenic influences. Only mature leaves were selected for analysis, using a tree pruner to randomly sample leaves from the canopy. Samples were immediately transported to the laboratory in iceboxes to preserve their physiological integrity prior to analysis. The detailed methodology for APTI estimation is as follows: a) Measurement of Leaf Extract pH One gram of fresh leaf material was homogenized in 10 ml of distilled water using a mortar and pestle. The homogenate was then filtered, and the filtrate was diluted in a 1:1 ratio with distilled water. The resulting solution was centrifuged at 2,500 rpm for 3 minutes to remove any suspended particles [42, 21]. The pH of the clear supernatant was measured using a calibrated digital pH meter. The pH meter was calibrated using standard buffer solutions of pH 4.0 and 9.0 prior to measurement. b) Determination of Relative Water Content (RWC) Fresh leaves were initially weighed to record the fresh weight (FW). They were then immersed in distilled water for 24 hours to attain full turgidity. After immersion, the leaves were gently blotted dry with tissue paper and weighed to obtain the turgid weight (TW). Subsequently, the leaves were oven-dried at 70°C overnight to a constant weight, and the dry weight (DW) was recorded [41]. Relative Water Content was calculated using the formula: RWC (%) = [(FW − DW) / (TW − DW)] × 100 where, W = Fresh weight of leaf; TW = Turgid weight of leaf; DW = Dry weight of leaf c) Estimation of Total Chlorophyll Content (TCC) To determine the total chlorophyll content, one gram of fresh leaf tissue was homogenized in 5 ml of 80 % acetone using a mortar and pestle. The volume was then made up to 10 ml with 80 % acetone, and the mixture was left undisturbed for 15–20 minutes to ensure thorough pigment extraction. The liquid extract was decanted into a clean test tube and centrifuged at 2,500 rpm for 3 minutes. If required, centrifugation was extended for an additional 3–4 minutes to ensure clear separation. The supernatant was carefully collected, and absorbance was measured using a spectrophotometer at wavelengths of 645 nm and 663 nm [3, 1]. Total chlorophyll content was calculated using the following formula: Where, A = Absorbance of the extract at the wavelength y nm; V = Total volume of the chlorophyll solution (ml); W = Weight of the tissue extracted (g). d) Estimation of Ascorbic Acid Content (AAC) Requirements: 1. 4% (w/v) Oxalic acid : 40 g of oxalic acid in 1000 ml of distilled water. 2. Dye solution : 84 mg of sodium bicarbonate and 104 mg of 2, 6- dichlorophenol indophenol in 400 ml of distilled water. 3. Ascorbic acid stock standard : (1mg/ml): 100 mg in 100 ml of 4 % oxalic acid. 4. Ascorbic acid working standard : (100µg/ml): 10 ml of the ascorbic acid stock standard diluted to 100 ml with 4% oxalic acid. Ascorbic acid content was determined using the indophenol (DCPIP) titration method [38]. Approximately one gram of fresh leaf tissue was accurately weighed and ground in a mortar and pestle with 10 ml of 4% oxalic acid. The homogenate was further ground and then filtered through filter paper. A 5 ml aliquot of the filtrate was transferred into a standard flask and the volume was made up to 25 ml with 4% oxalic acid. For standardization, 5 ml of the ascorbic acid working standard solution (500 µg/5 ml) was mixed with 10 ml of 4% oxalic acid in a 100 ml conical flask. This solution was titrated against the standard DCPIP dye solution until a persistent pale pink color appeared; the volume of dye used was noted as V₁ . Similarly, 5 ml of the prepared test sample was titrated against the DCPIP solution, and the dye volume required was recorded as V₂ . Ascorbic acid content in the test sample was calculated as follows: where, 500 = µg of standard ascorbic acid taken for titration = 0.5 mg; V1 = Volume of dye consumed by 500µg of standard ascorbic acid; V2 = Volume of dye consumed by 5 ml of test sample; 25 = Corresponds to total volume of the extract; 1 = Weight of sample taken for extraction; 5 = Volume of the test sample taken for titration. The APTI was calculated by the formula below [42]; where, A = Ascorbic acid content; T = Total Chlorophyll Content; P = p H of the leaf sample; R = Relative Water Content. The air pollution index [47] and the plant response to pollution is mentioned below: the index range of 17 show intermediate and tolerant responses respectively. Results and Discussion Air pollution data collected from three KSPCB monitoring stations near the study plots indicated that the Air Quality Index (AQI) ranged from satisfactory levels of 51 to 100. Among the measured pollutants, particulate matter (PM₁₀) was the only parameter consistently exceeding permissible limits. Other pollutants, including sulfur oxides (SOₓ) and nitrogen oxides (NOₓ), remained within acceptable regulatory thresholds (Fig. 3 ). The results of APTI are as follows: 3.1 NORTHERN TRANSECT 3.1.1 APTI analysis The selection of plant or tree species is a critical consideration when planting along roadsides or in urban green spaces. The Air Pollution Tolerance Index (APTI) is a useful tool to identify species based on their tolerance to air pollution. This index involves measuring leaf relative water content, pH, ascorbic acid, and chlorophyll content of leaf extracts [ 20 ]. Among the 15 tree species studied, Mangifera indica , Psidium guajava , Ficus glomerata , and Pongamia pinnata were relatively more tolerant compared to the other species. Although most species were categorized under a sensitive response category based on their APTI values, only Mangifera indica exhibited an intermediate tolerance level across the pollution gradient. Overall, species from urban and transition plots demonstrated higher tolerance to pollution compared to those from rural plots (Tables 1 and 2 ). The tolerance ranking of species in urban plots, from most to least tolerant, is as follows: Mangifera indica ˃ Psidium guajava ˃ Grevillea robusta ˃ Ficus glomerata ˃ Tectona grandis ˃ Syzygium cumini ˃ Moringa oleifera ˃ Eucalyptus hybrid ˃ Murraya koenigii ˃ Pongamia pinnata ˃ Muntingia calabura ˃ Tecoma stans ˃ Artocarpus heterophyllus ˃ Azadirachta indica ˃ Samanea saman . Similarly, the tolerance ranking in transition plots, from least to most tolerant, is: Muntingia calabura ˂ Murraya koenigii ˂ Artocarpus heterophyllus ˂ Samanea saman ˂ Azadirachta indica ˂ Eucalyptus hybrid ˂ Moringa oleifera ˂ Syzygium cumini ˂ Pongamia pinnata ˂ Tectona grandis ˂ Grevillea robusta ˂ Ficus glomerata ˂ Psidium guajava ˂ Tecoma stans ˂ Mangifera indica . In rural plots, species such as Murraya koenigii , Moringa oleifera , and Muntingia calabura exhibited the lowest tolerance indices among the studied species (Tables 1 and 2 ). Table 1 Parameters of Air Pollution Tolerance Index (APTI) along the domains of northern transect Urban Transition Rural Sl. No. List of species p H RWC Ascorbic Acid Total Chlorophyll p H RWC Ascorbic Acid Total Chlorophyll p H RWC Ascorbic Acid Total Chlorophyll 1 Mangifera indica 5.32 88.81 2.50 0.68 5.37 92.78 2.32 0.74 5.08 90.48 2.05 0.68 2 Artocarpus heterophyllus 6.06 61.45 0.63 0.70 6.03 70.33 0.09 0.70 6.11 61.38 0.36 0.71 3 Pongamia pinnata 6.91 70.77 0.09 0.67 6.16 82.86 0.09 0.69 6.25 71.43 0.09 0.69 4 Murraya koenigii 5.95 70.59 0.18 0.56 6.12 65.00 0.27 0.69 5.98 62.03 0.09 0.68 5 Azadirachta indica 6.18 54.82 0.96 0.66 6.32 69.51 0.88 0.63 6.38 77.78 0.76 0.69 6 Tectona grandis 6.83 84.23 0.18 0.69 6.59 86.95 0.09 0.70 5.80 75.78 0.18 0.57 7 Grevillea robusta 5.78 89.66 0.76 0.57 5.98 83.33 0.80 0.65 5.53 84.21 0.45 0.68 8 Muntingia calabura 7.52 62.50 0.98 0.23 5.83 64.29 0.31 0.70 5.85 39.26 0.58 0.71 9 Syzygium cumini 7.07 82.99 0.18 0.48 6.57 77.45 0.32 0.53 4.55 71.50 0.58 0.68 10 Samanea saman 5.90 50.00 1.07 0.70 5.95 71.43 0.58 0.70 5.63 74.64 0.58 0.71 11 Ficus glomerata 6.69 84.09 0.22 0.68 7.78 91.89 0.18 0.69 7.51 87.50 0.09 0.69 12 Tecoma stans 7.34 66.85 0.18 0.67 6.08 96.59 0.27 0.66 5.57 83.33 0.18 0.69 13 Psidium guajava 4.85 91.67 0.89 0.66 5.16 93.18 0.89 0.66 5.98 91.78 0.18 0.49 14 Moringa oleifera 5.61 80.00 0.63 0.70 5.44 75.00 0.63 0.69 5.27 42.86 0.18 0.67 15 Eucalyptus hybrid 4.97 69.85 0.82 0.59 5.29 72.68 0.74 0.67 4.85 78.90 0.67 0.67 Table 2 APTI values of northern transect Species Urban Transition Rural Mangifera indica 10.38 10.70 10.23 Artocarpus heterophyllus 6.57 7.09 6.38 Pongamia pinnata 7.14 8.35 7.20 Murraya koenigii 7.18 6.68 6.26 Azadirachta indica 6.14 7.57 8.31 Tectona grandis 8.56 8.76 7.69 Grevillea robusta 9.45 8.87 8.70 Muntingia calabura 7.01 6.63 4.31 Syzygium cumini 8.43 7.97 7.45 Samanea saman 5.71 7.53 7.83 Ficus glomerata 8.57 9.34 8.82 Tecoma stans 6.83 9.84 8.45 Psidium guajava 9.66 9.84 9.29 Moringa oleifera 8.39 7.88 4.39 Eucalyptus hybrid 7.44 7.71 8.26 3.2 SOUTHERN TRANSECT 3.2.1 APTI analysis Among the 16 tree species evaluated, Mangifera indica , Ficus tinctoria , Commiphora caudata , and Alangium salviifolium demonstrated relatively higher tolerance to air pollution compared to the others. Based on the Air Pollution Tolerance Index (APTI), all species were categorized under a sensitive response, except for Mangifera indica , which exhibited an intermediate response in both transition and rural plots. Overall, tree species in urban and rural plots showed comparatively higher tolerance than those in transition zones. The tolerance index in rural plots, arranged in decreasing order, is as follows: Mangifera indica > Commiphora caudata > Ficus tinctoria > Psidium guajava > Azadirachta indica > Alangium salviifolium > Grevillea robusta > Pongamia pinnata > Morus alba > Muntingia calabura > Tectona grandis > Murraya koenigii > Moringa oleifera > Phyllanthus acidus > Artocarpus heterophyllus > Leucaena leucocephala . In contrast, the tolerance index in transition plots, presented in increasing order, is: Artocarpus heterophyllus < Murraya koenigii < Muntingia calabura < Psidium guajava < Grevillea robusta < Leucaena leucocephala < Moringa oleifera < Tectona grandis < Phyllanthus acidus < Alangium salviifolium < Azadirachta indica < Morus alba < Commiphora caudata < Pongamia pinnata < Ficus tinctoria < Mangifera indica . Species such as Phyllanthus acidus , Leucaena leucocephala , Artocarpus heterophyllus , and Moringa oleifera recorded the lowest tolerance indices among all. Global climate change is expected to intensify pollutant emissions and greenhouse gas release in tropical urban environments. Therefore, the selection of suitable tree species is crucial for urban greening initiatives, especially along roadsides and in green spaces. Based on this study, species categorized as sensitive can also serve as effective bio-indicators for environmental monitoring. These findings are consistent with earlier research [ 16 , 14 ], which also reported similar physiological responses in tree species under polluted conditions. Notably, a shift toward acidic pH in urban plots and reductions in total chlorophyll content and relative water content suggest physiological stress in trees due to pollution [ 14 ]. Table 3 Parameters of Air Pollution Tolerance Index (APTI) along the domains of southern transect Urban Transition Rural Sl. No. List of species P H RWC Ascorbic Acid Total Chlorophyll P H RWC Ascorbic Acid Total Chlorophyll P H RWC Ascorbic Acid Total Chlorophyll 1 Mangifera indica 4.88 85.71 1.38 0.65 5.11 92.59 1.63 0.69 5.91 88.98 1.88 0.73 2 Artocarpus heterophyllus 5.75 65.66 0.25 0.64 5.89 56.23 0.50 0.67 6.40 60.70 0.13 0.73 3 Pongamia pinnata 6.12 82.50 0.13 0.51 6.60 84.88 0.13 0.68 6.51 79.59 0.13 0.70 4 Murraya koenigii 5.66 70.00 0.25 0.64 6.07 57.89 0.25 0.57 5.87 71.43 0.13 0.60 5 Azadirachta indica 6.03 71.43 0.13 0.64 6.70 76.92 0.75 0.68 6.47 82.61 0.81 0.72 6 Tectona grandis 6.86 77.56 0.25 0.64 6.32 77.96 0.13 0.68 6.26 73.45 0.13 0.69 7 Alangium salviifolium 5.50 85.19 1.25 0.64 6.23 78.95 0.38 0.66 6.36 80.95 0.50 0.47 8 Grevillea robusta 5.61 80.00 0.13 0.66 5.73 64.00 0.13 0.74 6.23 78.95 0.25 0.71 9 Muntingia calabura 5.38 73.68 0.13 0.61 6.27 60.00 0.25 0.66 5.97 75.86 0.69 0.68 10 Phyllanthus acidus 5.78 69.23 0.25 0.58 5.98 80.00 0.13 0.58 6.32 66.67 0.13 0.73 11 Psidium guajava 5.17 85.37 0.13 0.51 5.96 62.50 0.25 0.54 5.96 86.36 0.50 0.51 12 Moringa oleifera 5.48 60.00 0.38 0.58 5.43 75.00 0.38 0.69 5.73 66.67 0.50 0.55 13 Morus alba 6.21 71.67 0.25 0.65 6.24 82.43 0.25 0.66 6.82 79.55 0.13 0.67 14 Leucaena leucocephala 6.16 66.67 0.13 0.61 6.43 66.67 0.50 0.68 6.44 50.00 0.13 0.72 15 Ficus tinctoria 5.68 86.03 0.25 0.62 6.19 89.07 0.13 0.65 5.82 87.67 0.38 0.65 16 Commiphora caudata 4.49 80.00 0.13 0.58 4.78 84.00 0.13 0.57 5.52 90.48 0.13 0.56 Table 4 APTI values of southern transect Species Urban Transition Rural Mangifera indica 9.33 10.20 10.14 Artocarpus heterophyllus 6.73 5.95 6.16 Pongamia pinnata 8.33 8.58 8.05 Murraya koenigii 7.16 5.96 7.22 Azadirachta indica 7.23 8.25 8.85 Tectona grandis 7.94 7.88 7.43 Alangium salviifolium 9.29 8.15 8.44 Grevillea robusta 8.08 6.48 8.07 Muntingia calabura 7.44 6.17 8.04 Phyllanthus acidus 7.08 8.08 6.75 Psidium guajava 8.61 6.41 8.96 Moringa oleifera 6.23 7.73 6.98 Morus alba 7.34 8.42 8.05 Leucaena leucocephala 6.75 7.02 5.09 Ficus tinctoria 8.76 8.99 9.01 Commiphora caudata 8.06 8.47 9.12 The Air Pollution Tolerance Index (APTI) has been widely employed across different geographic regions to evaluate the tolerance of plant species to atmospheric pollutants. Several studies across industrial and urban landscapes have yielded insightful comparisons of plant species in relation to their pollution response mechanisms. In Beijing, China, a study near a steel industry identified Broussonetia papyrifera , Robinia pseudoacacia , and Ailanthus altissima as species with moderate to high tolerance to industrial emissions [ 22 ]. Similarly, in Delta State, Nigeria, species such as Emilia samtifolia , Manihot esculenta , and Elaeis guineensis exhibited higher APTI values near the Otorogun Gas Plant, indicating their resilience in a polluted environment [ 1 ]. At the Erhoike-Kokori oil drilling site in the same region, Mangifera indica and Chromolaena odorata topped the tolerance rankings, while Psidium guajava and Elaeis guineensis showed lower tolerance [ 2 ]. In Enugu, Nigeria, APTI analysis in both urban and industrial zones revealed that tree species generally outperformed ornamental shrubs in pollution tolerance. Anacardium occidentale , Pinus spp. , and Catalpa bungei recorded higher APTI values than shrubs, suggesting their suitability for urban landscaping to combat heat island effects [ 15 ]. Furthermore, in the Ama industrial area of Enugu, Delonix regia (APTI: 5.308 ± 0.090) showed significantly higher tolerance compared to Anacardium occidentale (APTI: 3.470 ± 0.001) [ 32 ]. In Pulau Pinang, Malaysia, comparative evaluation between Ficus sp. and Bougainvillea sp. along major roads showed that Ficus exhibited greater APTI values, indicating higher tolerance, while Bougainvillea was more sensitive and suitable as a biological indicator [ 25 ]. In Côte d'Ivoire, Ficus benjamina recorded the highest APTI among ornamental plants (up to 17.15), followed by Jatropha integerrima and Barleria prionitis . Cassia surattensis was the most sensitive species based on APTI scores [ 47 ]. Studies from Ahvaz, Iran, showed notable spatial variation in APTI values across polluted and non-polluted sites. Myrtus exhibited the highest tolerance in both areas, while Prosopis showed vulnerability to pollution, with its APTI dropping from 4.97 (unpolluted) to 4.57 (polluted) [ 16 ]. In the Babylon region of Mesopotamia, Conocarpus lancifolius was found tolerant, while Dodonaea viscosa was more sensitive, as influenced by prevailing wind directions and proximity to pollution sources [ 39 ]. A notable observation from several studies is the positive correlation between higher ascorbic acid content and SO₂ tolerance, supporting the use of physiological parameters in APTI computation [ 49 ]. In Indian studies, Mangifera indica , Alstonia scholaris , and Eupatorium odoratum emerged as effective biomonitors of vehicular pollution [ 19 ]. In Bangalore, India, field comparisons showed that Bougainvillea spectabilis and Ageratum conyzoides had significant drops in APTI values from control to polluted sites, indicating high sensitivity, whereas Peltophorum pterocarpum and Portulaca oleraceae retained relatively stable scores, marking them as pollution-tolerant species [ 14 ]. A broader study assessing APTI and Anticipated Performance Index (API) for 29 species showed that Ficus benghalensis , Cassia fistula , and Ficus religiosa had the highest tolerance scores, while Mangifera indica showed lower tolerance in comparison. In marble industrial zones of Potwar, species such as Polyalthia longifolia , Ficus glomerata , and Ailanthus indicus were noted for their resilience [ 33 ]. In Navsari, India, Cassia fistula outperformed other species like Saraca asoca and Syzygium cumini in terms of APTI, affirming its tolerance in urban polluted environments. In contrast, Tectona grandis and Terminalia catappa showed moderate sensitivity [ 30 ]. Lastly, in Dehradun, Eucalyptus globulus exhibited the highest APTI values, followed by Ficus religiosa and Mangifera indica , while lower APTI was observed in species like Lantana camara [ 23 ]. Conclusion The APTI values indicated that several tree species found along the northern and southern transects exhibit relatively higher tolerance to air pollution. In the northern transect, species such as Mangifera indica , Psidium guajava , Ficus glomerata , and Pongamia pinnata were identified as pollution-tolerant. Similarly, in the southern transect, Mangifera indica , Ficus tinctoria , Commiphora caudata , and Alangium salviifolium demonstrated higher tolerance levels. These findings underscore the importance of selecting tree species based on the local air quality index (AQI). In urban environments—where pollution levels are often elevated—prioritizing species with high APTI values is critical for the success and sustainability of urban forestry and landscape planning initiatives. Pollution-tolerant species not only have a higher likelihood of survival in degraded environments, but also contribute significantly to mitigating air pollution, reducing urban heat island effects, and enhancing biodiversity. Therefore, incorporating APTI as a guiding criterion in species selection can improve the resilience and ecological performance of urban green spaces. This approach ensures that planted trees are better adapted to prevailing environmental stresses, thereby maximizing the long-term benefits of urban forests. In turn, this supports the development of healthier, greener, and more sustainable cities. Declarations Acknowledgements: The authors express their sincere gratitude to the Director, Institute of Wood Science and Technology (IWST), for providing the necessary facilities and institutional support to carry out this study. The research was funded by the Department of Biotechnology (DBT), Government of India, under the Indo-German collaborative program, which is gratefully acknowledged. The authors also extend their appreciation to the German collaborators, Dr. Christoph Kleinn and Dr. Nils Nölke from Georg-August-University, Göttingen, Germany, for their valuable contributions to the Indo-German research consortium. Their support in providing WorldView-3 satellite imagery was instrumental in the layout and selection of plots along the urban–rural gradient across the northern and southern transects of Bengaluru. Funding: The study was funded by the Department of Biotechnology (DBT), Government of India, New Delhi , under the Indo-German collaborative research programme. Author contributions: All authors have approved the final version of the manuscript. B.N.D. conceptualized, planned, and executed the fieldwork, and led the drafting of the manuscript. C.U.N. assisted in data collection, compilation, and contributed to the manuscript drafting. V.P.T. supported the execution of the study, reviewed the manuscript, and provided valuable suggestions for its improvement. Competing interests: The authors declare that they have received no financial support for the preparation of this manuscript and have no competing interests to disclose. Ethics approval and Consent to participate: The collection of leaf samples in this study was carried out in accordance with applicable local and national guidelines. All samples were obtained from cultivated plants, and no endangered or protected species were involved. Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Consent to Publish: All authors have read and approved the final manuscript and consent to its publication. Clinical trial number: Not Applicable References Agbaire PO, Esiefarienrhe E. Air Pollution Tolerance Indices (APTI) of some plants around Otorogun Gas Plant in Delta State, Nigeria. J Appl Sci Environ Manage. 2009;13(1):11–4. Agbaire PO. Air pollution tolerance indices (APTI) of some plants around Erhoike-Kokori oil exploration site of Delta State, Nigeria. Int J Phys Sci. 2009;4(6):366–8. Arnon DI. Copper Enzymes in Isolated Chloroplasts Polyphenol Oxidase in Beta vulgaris. Plant physiol. 1949;24:1–15. Aronson MF, La Sorte FA, Nilon CH, Katti M, Goddard MA, Lepczyk CA, Warren PS, Williams NS, Cilliers S, Clarkson B, Dobbs C, Dolan R, Hedblom M, Klotz S, Kooijmans JL, Kühn I, Macgregor-Fors I, McDonnell M, Mörtberg U, Pysek P, Siebert S, Sushinsky J, Werner P, Winter M. 2014. 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Mesopotemia Environ J. 2017;3(2):11–7. Sekhar M, Mohan Kumar MS. 2009. Geo-hydrological studies along the metro rail alignment in Bangalore. Technical report, Department of Civil Engineering, Indian Institute of Science, Bangalore, India. http://bmrc.co.in/pdf/news/iisc-report.pdf Singh A. Practical Plant physiology. New Delhi: Kalyari; 1977. Singh SK, Rao DN. 1983. Evaluation of plants for their tolerance to air pollution. In Proc. Symp. on Air pollution control, IIT, Delhi, pp 218–224. Stagoll K, Lindenmayer DB, Knight E, Fischer J, Manning AD. Large trees are keystone structures in urban parks. Conserv Lett. 2012;5:115–22. Sudha P, Ravindranath NH. A study of Bangalore urban forest. Landsc Urban Plann. 2000;47(1–2):47–63. Sudhira HS, Nagendra H. Local Assessment of Bangalore: Graying and Greening in Bangalore - Impacts of Urbanization on Ecosystems, Ecosystem Services and Biodiversity. In: Elmqvist T, et al. editors. Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities. Dordrecht: Springer; 2013. pp. 75–91. https://doi.org/10.1007/978-94-007-7088-1_7 . Sudhira HS, Ramachandra TV, Subrahmanya MHB. City profile Bangalore Cities. 2007;24:379–90. Tra Bi ZF, Angaman DM, Barima YSS, Dongui BK. Evaluation of air pollution tolerance indices of four ornamental plants arranged along roadsides in Abidjan (Côte d'Ivoire). Int J Environ Monit Anal. 2015;3(1):22–7. 10.11648/j.ijema.20150301.14 . United Nations. Urbanization and Development - Emerging Futures (World Cities report) [United. Nations Human Settlements Programme (UN-Habitat)]; 2016. Varshney SRK, Varshney CK. Effects of SO2 on ascorbic acid in crop plants. Environ Pollution Ser Ecol Biol. 1984;35(4):285–90. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6946956","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483378315,"identity":"00ff3dfb-0e2b-47e9-ad8c-1d3af082477a","order_by":0,"name":"Baragur Neelappa Divakara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACAxBKYGCQA3EOPCBFizFYSwLRWoAgsQFEEqXFXLp584eHe+zS54cdfgi0xU5Ot4GAFss5x8okEp4l5268nWYA1JJsbHaAkMNu5JgxJBxgzt04OwGk5UDiNiK0GH9IOFCfbjg7/QPRWgwkEg4cTpCXziHSFssZaUC/HDhuuEE6p+BAggERfjGXSN788ceBann52embP3yosJMjqAXhQrBKA2KVg4B8AymqR8EoGAWjYEQBAEdiSNHWbiTaAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Wood Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Baragur","middleName":"Neelappa","lastName":"Divakara","suffix":""},{"id":483378316,"identity":"c3b6d8df-71de-4b16-8588-de8487c532ce","order_by":1,"name":"CU Nikhitha","email":"","orcid":"","institution":"Institute of Wood Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"CU","middleName":"","lastName":"Nikhitha","suffix":""},{"id":483378317,"identity":"3357ea11-aca1-4592-bb58-a22521e9832a","order_by":2,"name":"VP Tewari","email":"","orcid":"","institution":"Institute of Wood Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"VP","middleName":"","lastName":"Tewari","suffix":""}],"badges":[],"createdAt":"2025-06-21 22:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6946956/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6946956/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86507510,"identity":"17becbaf-1cf8-4f76-b7c6-2941715debad","added_by":"auto","created_at":"2025-07-11 12:17:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of the study area in the northern and southern part of Bengaluru\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6946956/v1/dcec20db7e933d9afa38a877.png"},{"id":86506079,"identity":"59550584-a388-40fe-b740-89e8f65dd56e","added_by":"auto","created_at":"2025-07-11 12:01:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":591653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy plots identified along the northern and southern part of Bengaluru\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6946956/v1/a0cff48c4b5d87e84334190e.png"},{"id":86506080,"identity":"52f9c09b-0e4c-44c8-a854-ef7554f28ebd","added_by":"auto","created_at":"2025-07-11 12:01:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAir quality parameters from the Karnataka State Pollution Control Board (KSPCB) monitoring stations\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6946956/v1/5c4c434cbe6434ad1aab66aa.png"},{"id":93051251,"identity":"892695ee-df4b-4f61-93e2-51a055102259","added_by":"auto","created_at":"2025-10-08 14:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2723096,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6946956/v1/1a0ee27e-ef05-4a07-b405-1a05077c2bd1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Status of Street Trees Along the Urban–Rural Gradient in the Garden City of Bengaluru, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrbanization is a continuous phenomenon observed in both developing and developed nations [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. It typically refers to the demographic shift from rural areas to urban centres, influenced by a combination of economic, political, and geographical factors. Bengaluru, a major South Indian metropolis also known as the \"Silicon Valley of India,\" has undergone rapid urbanization characterized by unplanned and uncontrolled development, demographic expansion, heterogeneous land use, deforestation, and various anthropogenic activities [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Originally a modest village in the 12th century, Bengaluru has evolved into one of India's fastest-growing cities by the 21st century, now ranking as the second-fastest-growing and fifth-largest metropolis in the country [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, this rapid transformation has adversely impacted the region\u0026rsquo;s biodiversity and ecosystem services, particularly through the reduction of green spaces and vegetation cover required to support the growing population [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUrban green spaces are critical for delivering a range of ecological, environmental, economic, and social benefits. These include filtering airborne pollutants and dust [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], mitigating the urban heat island effect and climate change [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], acting as noise barriers, alleviating urban floods, and enhancing biodiversity and ecosystem functionality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, they contribute to the hydrological cycle by recharging groundwater aquifers and stabilizing the urban climate [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Despite increasing scientific evidence over the past two decades highlighting the importance of urban green spaces, city planners and managers often undervalue the role of trees in maintaining urban ecological integrity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Effective conservation of urban biodiversity necessitates detailed data on tree species composition, including the influence of urbanization on species nativity. Notably, an increase in urban land cover is often associated with a rise in non-native species richness [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Urban trees in Bengaluru face a range of anthropogenic stresses such as drought, impermeable surfaces restricting root growth, pest infestations, urban heat, humidity, and mechanical damage due to infrastructure development [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A study revealed that only 42% of the trees are native species, with four out of five being exotic [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHistorically, Bengaluru was celebrated for its lush greenery and was famously dubbed the \u0026ldquo;Garden City of India,\u0026rdquo; a legacy attributed to Krishnaraja Wodeyar IV, who commissioned the creation of iconic green spaces such as Lalbagh and Cubbon Park. The city once boasted around 705 parks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and 200 additional green spaces (e.g., roadside and avenue trees), many of which have been lost due to urban development pressures [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Compared to surrounding rural areas, Bengaluru exhibits high species richness and diversity, largely due to the prevalence of exotic species. These urban green spaces serve as the city's \u0026ldquo;lungs,\u0026rdquo; contributing to pollution control, biodiversity conservation, and improved ecosystem services [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, infrastructure projects like road widening and encroachments have led to a significant decline in large, mature canopy trees, exacerbating the urban heat island effect [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUrban trees are also vulnerable to vehicular pollution. Between 2005 and 2011, air pollution levels in the city fluctuated between critical and high at various monitoring sites. The Air Pollution Tolerance Index (APTI) is a valuable tool for assessing a tree's tolerance to urban air pollutants, based on physiological and biochemical parameters such as chlorophyll content, stomatal conductance, and relative water content of leaves. APTI is especially useful in assessing tree resilience to pollutants like nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and particulate matter (PM). Major sources of SO₂, NO₂, RSPM, and PM₁₀ include vehicular emissions from fossil fuel combustion and industrial activities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous assessments have reported that the estimated tree crown cover in Bengaluru stood at approximately 19.9%, with a per capita green space availability of around 17 m\u0026sup2; [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven these findings, vegetation cover is a vital component of urban landscapes, essential for maintaining ecological balance and supporting human well-being. This study aims to evaluate the health of street trees across various urban-to-rural zones in Bengaluru using APTI as a key metric. By analyzing APTI values across the urban-rural gradient, the study seeks to understand how different tree species respond to varying pollution levels and how air pollution affects their overall health. These insights can inform urban planning and green space management strategies to mitigate pollution impacts and improve the city\u0026rsquo;s ecological health.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eBengaluru, the principal administrative capital of Karnataka, is geographically located in the southeastern part of the Indian peninsula, at coordinates 77\u0026deg;37\u0026apos;19.54\u0026quot; E and 12\u0026deg;59\u0026apos;09.76\u0026quot; N. The Greater Bengaluru area spans approximately 741 km\u0026sup2; and falls under the jurisdiction of the Bruhat Bengaluru Mahanagara Palike (BBMP), which governs 198 wards across 8 zones. The city\u0026rsquo;s spatial extent has undergone substantial urban expansion\u0026mdash;growing more than tenfold from 69 km\u0026sup2; in 1949 to 741 km\u0026sup2; in 2006. The geographical boundaries of Bengaluru extend from 12\u0026deg;49\u0026apos;5\u0026quot; N to 13\u0026deg;8\u0026apos;32\u0026quot; N and from 77\u0026deg;27\u0026apos;29\u0026quot; E to 77\u0026deg;47\u0026apos;2\u0026quot; E. Bengaluru\u0026rsquo;s population within BBMP limits witnessed a remarkable decadal growth of 47.18%, increasing from 6.53 million in 2001 to approximately 9.62 million in 2011. Consequently, population density surged from 10,732 persons/km\u0026sup2; in 2001 to 13,392 persons/km\u0026sup2; in 2011 [35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTopographically, Bengaluru is situated at altitudes ranging between 740 m and 960 m above mean sea level. The city experiences an average annual rainfall of about 880 mm, typically spread over approximately 60 rainy days. Summer temperatures often exceed 38\u0026deg;C, whereas winter temperatures average around 25\u0026deg;C, contributing to the city\u0026rsquo;s overall pleasant climate year-round. Positioned on a ridge, Bengaluru is drained by three principal valley systems: Hebbal-Nagavara, Koramangala-Challaghatta (K\u0026amp;C), and Vrishabhavathi. These valley systems facilitate drainage into major rivers such as the east-flowing Arkavathi and Pinakini (Pennar), and the west-flowing Shimsha, a tributary of the Cauvery. Historically, embankments constructed along these valleys gave rise to a network of tanks and lakes, which served irrigation and water storage purposes. While the low-lying areas are dominated by water bodies of varying sizes, the middle, northern, and eastern regions feature undulating terrain with scrub-covered highlands. The southern part is characterized by rugged hills surrounded by forests and scrub jungles. Geologically, the Bengaluru region is predominantly underlain by granitic and gneissic formations, interspersed with dolerites, dykes, and schist rocks [40].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling and Data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted on trees located in the northern and southern parts of Bengaluru city. A total of 48 plots, each measuring 100 m \u0026times; 100 m (1 ha), were established\u0026mdash;23 in the north and 25 in the south\u0026mdash;along transects defined by the Indo-German research consortium (Fig. 1 \u0026amp; 2). Plot selection was based on a stratified random sampling approach, with two strata: \u0026ldquo;built-up\u0026rdquo; and \u0026ldquo;others.\u0026rdquo; The \u0026ldquo;built-up\u0026rdquo; stratum was characterized by areas with more than 50% impervious surface, as identified from WorldView-3 satellite imagery (Fig. 1 \u0026amp; 2). All selected plots are within the \u0026ldquo;built-up\u0026rdquo; stratum, as the primary focus of the study was on urbanized areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the study, plots delineated along the transects were categorized into urban, transition, and rural zones based on the proportion of built-up pixels derived from WorldView-3 satellite imagery. Grids with more than 50% built-up pixels were classified as \u003cem\u003eurban\u003c/em\u003e, those with 10\u0026ndash;50% as \u003cem\u003etransition\u003c/em\u003e, and those with 0\u0026ndash;10% as \u003cem\u003erural\u003c/em\u003e. The distance of each plot from the city center was also considered as a supplementary criterion for classification.\u003c/p\u003e\n\u003cp\u003eA total of 23 plots along the northern transect and 25 plots along the southern transect were surveyed in 2017 and 2018, respectively. The transects extended toward Doddaballapura in the north and Kanakapura in the south of Bengaluru. Using the geographic coordinates of each location, one-hectare (100 m \u0026times; 100 m) plots were identified in the field using GPS. Distinct natural and anthropogenic features\u0026mdash;such as buildings, roads, and tree arrangements\u0026mdash;were used as reference markers to verify whether individual trees fell within or outside the plot boundaries. All trees within the plots were manually numbered with enamel paint on their stems to enable future identification and monitoring.\u003c/p\u003e\n\u003cp\u003eTo achieve the study objectives, air quality data were obtained from monitoring stations operated by the Karnataka State Pollution Control Board (KSPCB) and the Central Pollution Control Board (CPCB). Data on particulate matter (PM₁₀ and PM₂.₅), sulfur oxides (SOₓ), and nitrogen oxides (NOₓ) were regularly collected from stations located in proximity to the plots.\u003c/p\u003e\n\u003cp\u003eTo assess pollution-induced stress on urban vegetation, the Air Pollution Tolerance Index (APTI) was calculated for tree species across different land-use categories. From the northern transect, 15 common tree species representing urban, transition, and rural plots were selected for APTI analysis. Similarly, 16 common tree species were selected from the southern transect.\u003c/p\u003e\n\u003cp\u003eThe APTI procedure was standardized, and leaf samples were analyzed on a day-to-day basis. Samples were collected from trees consistently exposed to vehicular emissions and other anthropogenic influences. Only mature leaves were selected for analysis, using a tree pruner to randomly sample leaves from the canopy. Samples were immediately transported to the laboratory in iceboxes to preserve their physiological integrity prior to analysis. The detailed methodology for APTI estimation is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea)\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMeasurement of Leaf Extract pH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne gram of fresh leaf material was homogenized in 10 ml of distilled water using a mortar and pestle. The homogenate was then filtered, and the filtrate was diluted in a 1:1 ratio with distilled water. The resulting solution was centrifuged at 2,500 rpm for 3 minutes to remove any suspended particles [42, 21]. The pH of the clear supernatant was measured using a calibrated digital pH meter. The pH meter was calibrated using standard buffer solutions of pH 4.0 and 9.0 prior to measurement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb)\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDetermination of Relative Water Content (RWC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh leaves were initially weighed to record the fresh weight (FW). They were then immersed in distilled water for 24 hours to attain full turgidity. After immersion, the leaves were gently blotted dry with tissue paper and weighed to obtain the turgid weight (TW). Subsequently, the leaves were oven-dried at 70\u0026deg;C overnight to a constant weight, and the dry weight (DW) was recorded [41]. Relative Water Content was calculated using the formula:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRWC (%) = [(FW \u0026minus; DW) / (TW \u0026minus; DW)] \u0026times; 100\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewhere, W = Fresh weight of leaf; TW = Turgid weight of leaf; DW = Dry weight of leaf\u003c/p\u003e\n\u003cp\u003ec) \u003cstrong\u003eEstimation of Total Chlorophyll Content (TCC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the total chlorophyll content, one gram of fresh leaf tissue was homogenized in 5 ml of 80 % acetone using a mortar and pestle. The volume was then made up to 10 ml with 80 % acetone, and the mixture was left undisturbed for 15\u0026ndash;20 minutes to ensure thorough pigment extraction. The liquid extract was decanted into a clean test tube and centrifuged at 2,500 rpm for 3 minutes. If required, centrifugation was extended for an additional 3\u0026ndash;4 minutes to ensure clear separation. The supernatant was carefully collected, and absorbance was measured using a spectrophotometer at wavelengths of 645 nm and 663 nm [3, 1]. Total chlorophyll content was calculated using the following formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 495px; height: 189.344px;\" width=\"495\" height=\"189.344\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere, A = Absorbance of the extract at the wavelength y nm; V = Total volume of the chlorophyll solution (ml); W = Weight of the tissue extracted (g).\u003c/p\u003e\n\u003cp\u003ed) \u003cstrong\u003eEstimation of Ascorbic Acid Content (AAC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRequirements:\u003c/p\u003e\n\u003cp\u003e1. \u003cu\u003e4% (w/v) Oxalic acid\u003c/u\u003e: 40 g of oxalic acid in 1000 ml of distilled water.\u003c/p\u003e\n\u003cp\u003e2. \u003cu\u003eDye solution\u003c/u\u003e: 84 mg of sodium bicarbonate and 104 mg of 2, 6- dichlorophenol indophenol in 400 ml of distilled water.\u003c/p\u003e\n\u003cp\u003e3. \u003cu\u003eAscorbic acid stock standard\u003c/u\u003e: (1mg/ml): 100 mg in 100 ml of 4 % oxalic acid.\u003c/p\u003e\n\u003cp\u003e4. \u003cu\u003eAscorbic acid working standard\u003c/u\u003e: (100\u0026micro;g/ml): 10 ml of the ascorbic acid stock standard diluted to 100 ml with 4% oxalic acid.\u003c/p\u003e\n\u003cp\u003eAscorbic acid content was determined using the \u003cstrong\u003eindophenol (DCPIP) titration method\u003c/strong\u003e [38]. Approximately one gram of fresh leaf tissue was accurately weighed and ground in a mortar and pestle with 10 ml of 4% oxalic acid. The homogenate was further ground and then filtered through filter paper. A 5 ml aliquot of the filtrate was transferred into a standard flask and the volume was made up to 25 ml with 4% oxalic acid. For standardization, 5 ml of the ascorbic acid working standard solution (500 \u0026micro;g/5 ml) was mixed with 10 ml of 4% oxalic acid in a 100 ml conical flask. This solution was titrated against the standard DCPIP dye solution until a persistent pale pink color appeared; the volume of dye used was noted as \u003cstrong\u003eV₁\u003c/strong\u003e. Similarly, 5 ml of the prepared test sample was titrated against the DCPIP solution, and the dye volume required was recorded as \u003cstrong\u003eV₂\u003c/strong\u003e. Ascorbic acid content in the test sample was calculated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 455px; height: 64.6355px;\" width=\"455\" height=\"64.6355\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere, 500 = \u0026micro;g of standard ascorbic acid taken for titration = 0.5 mg; V1 = Volume of dye consumed by 500\u0026micro;g of standard ascorbic acid; V2 = Volume of dye consumed by 5 ml of test sample; 25 = Corresponds to total volume of the extract; 1 = Weight of sample taken for extraction; 5 = Volume of the test sample taken for titration.\u003c/p\u003e\n\u003cp\u003eThe APTI was calculated by the formula below [42];\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 218px; height: 69.6885px;\" width=\"218\" height=\"69.6885\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere, A = Ascorbic acid content; T = Total Chlorophyll Content; P = p\u003csup\u003eH\u0026nbsp;\u003c/sup\u003eof the leaf sample; \u0026nbsp; R = Relative Water Content.\u003c/p\u003e\n\u003cp\u003eThe air pollution index [47] and the plant response to pollution is mentioned below: the index range of \u0026lt;10 shows sensitive response, besides the index range of 10 - 16 and \u0026gt;17 show intermediate and tolerant responses respectively.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eAir pollution data collected from three KSPCB monitoring stations near the study plots indicated that the Air Quality Index (AQI) ranged from satisfactory levels of 51 to 100. Among the measured pollutants, particulate matter (PM₁₀) was the only parameter consistently exceeding permissible limits. Other pollutants, including sulfur oxides (SOₓ) and nitrogen oxides (NOₓ), remained within acceptable regulatory thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results of APTI are as follows:\u003c/p\u003e\n\u003ch3\u003e3.1 NORTHERN TRANSECT\u003c/h3\u003e\n\u003cp\u003e3.1.1 APTI analysis\u003c/p\u003e\u003cp\u003eThe selection of plant or tree species is a critical consideration when planting along roadsides or in urban green spaces. The Air Pollution Tolerance Index (APTI) is a useful tool to identify species based on their tolerance to air pollution. This index involves measuring leaf relative water content, pH, ascorbic acid, and chlorophyll content of leaf extracts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among the 15 tree species studied, \u003cem\u003eMangifera indica\u003c/em\u003e, \u003cem\u003ePsidium guajava\u003c/em\u003e, \u003cem\u003eFicus glomerata\u003c/em\u003e, and \u003cem\u003ePongamia pinnata\u003c/em\u003e were relatively more tolerant compared to the other species. Although most species were categorized under a sensitive response category based on their APTI values, only \u003cem\u003eMangifera indica\u003c/em\u003e exhibited an intermediate tolerance level across the pollution gradient.\u003c/p\u003e\u003cp\u003eOverall, species from urban and transition plots demonstrated higher tolerance to pollution compared to those from rural plots (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The tolerance ranking of species in urban plots, from most to least tolerant, is as follows:\u003c/p\u003e\u003cp\u003e\u003cem\u003eMangifera indica\u003c/em\u003e ˃ \u003cem\u003ePsidium guajava\u003c/em\u003e ˃ \u003cem\u003eGrevillea robusta\u003c/em\u003e ˃ \u003cem\u003eFicus glomerata\u003c/em\u003e ˃ \u003cem\u003eTectona grandis\u003c/em\u003e ˃ \u003cem\u003eSyzygium cumini\u003c/em\u003e ˃ \u003cem\u003eMoringa oleifera\u003c/em\u003e ˃ \u003cem\u003eEucalyptus\u003c/em\u003e hybrid ˃ \u003cem\u003eMurraya koenigii\u003c/em\u003e ˃ \u003cem\u003ePongamia pinnata\u003c/em\u003e ˃ \u003cem\u003eMuntingia calabura\u003c/em\u003e ˃ \u003cem\u003eTecoma stans\u003c/em\u003e ˃ \u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e ˃ \u003cem\u003eAzadirachta indica\u003c/em\u003e ˃ \u003cem\u003eSamanea saman\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eSimilarly, the tolerance ranking in transition plots, from least to most tolerant, is: \u003cem\u003eMuntingia calabura\u003c/em\u003e ˂ \u003cem\u003eMurraya koenigii\u003c/em\u003e ˂ \u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e ˂ \u003cem\u003eSamanea saman\u003c/em\u003e ˂ \u003cem\u003eAzadirachta indica\u003c/em\u003e ˂ \u003cem\u003eEucalyptus\u003c/em\u003e hybrid ˂ \u003cem\u003eMoringa oleifera\u003c/em\u003e ˂ \u003cem\u003eSyzygium cumini\u003c/em\u003e ˂ \u003cem\u003ePongamia pinnata\u003c/em\u003e ˂ \u003cem\u003eTectona grandis\u003c/em\u003e ˂ \u003cem\u003eGrevillea robusta\u003c/em\u003e ˂ \u003cem\u003eFicus glomerata\u003c/em\u003e ˂ \u003cem\u003ePsidium guajava\u003c/em\u003e ˂ \u003cem\u003eTecoma stans\u003c/em\u003e ˂ \u003cem\u003eMangifera indica\u003c/em\u003e. In rural plots, species such as \u003cem\u003eMurraya koenigii\u003c/em\u003e, \u003cem\u003eMoringa oleifera\u003c/em\u003e, and \u003cem\u003eMuntingia calabura\u003c/em\u003e exhibited the lowest tolerance indices among the studied species (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\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\u003eParameters of Air Pollution Tolerance Index (APTI) along the domains of northern transect\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eTransition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSl. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eList of species\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRWC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAscorbic Acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal Chlorophyll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRWC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAscorbic Acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTotal Chlorophyll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ep\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eRWC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eAscorbic Acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eTotal Chlorophyll\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMangifera indica\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e92.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e90.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eArtocarpus heterophyllus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e70.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e61.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePongamia pinnata\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e71.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMurraya koenigii\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e65.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e62.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAzadirachta indica\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e69.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e77.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTectona grandis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e86.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e75.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGrevillea robusta\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e83.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMuntingia calabura\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e64.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e39.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSyzygium cumini\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e77.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e71.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSamanea saman\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e71.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e74.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFicus glomerata\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e91.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e87.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTecoma stans\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e96.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e83.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePsidium guajava\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e93.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e91.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMoringa oleifera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e75.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e42.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEucalyptus hybrid\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e72.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e78.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.67\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\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\u003eAPTI values of northern transect\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTransition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRural\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\u003eMangifera indica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePongamia pinnata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMurraya koenigii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAzadirachta indica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTectona grandis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrevillea robusta\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMuntingia calabura\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSyzygium cumini\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSamanea saman\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFicus glomerata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTecoma stans\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePsidium guajava\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMoringa oleifera\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEucalyptus hybrid\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 SOUTHERN TRANSECT\u003c/h2\u003e\u003cp\u003e3.2.1 APTI analysis\u003c/p\u003e\u003cp\u003eAmong the 16 tree species evaluated, \u003cem\u003eMangifera indica\u003c/em\u003e, \u003cem\u003eFicus tinctoria\u003c/em\u003e, \u003cem\u003eCommiphora caudata\u003c/em\u003e, and \u003cem\u003eAlangium salviifolium\u003c/em\u003e demonstrated relatively higher tolerance to air pollution compared to the others. Based on the Air Pollution Tolerance Index (APTI), all species were categorized under a sensitive response, except for \u003cem\u003eMangifera indica\u003c/em\u003e, which exhibited an intermediate response in both transition and rural plots. Overall, tree species in urban and rural plots showed comparatively higher tolerance than those in transition zones.\u003c/p\u003e\u003cp\u003eThe tolerance index in rural plots, arranged in decreasing order, is as follows: \u003cem\u003eMangifera indica\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eCommiphora caudata\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eFicus tinctoria\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003ePsidium guajava\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eAzadirachta indica\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eAlangium salviifolium\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eGrevillea robusta\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003ePongamia pinnata\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eMorus alba\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eMuntingia calabura\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eTectona grandis\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eMurraya koenigii\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eMoringa oleifera\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003ePhyllanthus acidus\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eLeucaena leucocephala\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn contrast, the tolerance index in transition plots, presented in increasing order, is: \u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eMurraya koenigii\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eMuntingia calabura\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ePsidium guajava\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eGrevillea robusta\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eLeucaena leucocephala\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eMoringa oleifera\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eTectona grandis\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ePhyllanthus acidus\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eAlangium salviifolium\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eAzadirachta indica\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eMorus alba\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eCommiphora caudata\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ePongamia pinnata\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eFicus tinctoria\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eMangifera indica\u003c/em\u003e. Species such as \u003cem\u003ePhyllanthus acidus\u003c/em\u003e, \u003cem\u003eLeucaena leucocephala\u003c/em\u003e, \u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e, and \u003cem\u003eMoringa oleifera\u003c/em\u003e recorded the lowest tolerance indices among all.\u003c/p\u003e\u003cp\u003eGlobal climate change is expected to intensify pollutant emissions and greenhouse gas release in tropical urban environments. Therefore, the selection of suitable tree species is crucial for urban greening initiatives, especially along roadsides and in green spaces. Based on this study, species categorized as sensitive can also serve as effective bio-indicators for environmental monitoring. These findings are consistent with earlier research [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which also reported similar physiological responses in tree species under polluted conditions. Notably, a shift toward acidic pH in urban plots and reductions in total chlorophyll content and relative water content suggest physiological stress in trees due to pollution [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\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\u003eParameters of Air Pollution Tolerance Index (APTI) along the domains of southern transect\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\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\u003cdiv align=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003eTransition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSl. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eList of species\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRWC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAscorbic Acid\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\u003eP\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRWC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAscorbic Acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eTotal Chlorophyll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eP\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eRWC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eAscorbic Acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eTotal Chlorophyll\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMangifera indica\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e92.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e88.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eArtocarpus heterophyllus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e56.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e60.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePongamia pinnata\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e84.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e79.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMurraya koenigii\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e57.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e71.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAzadirachta indica\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e76.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e82.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTectona grandis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e77.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e73.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAlangium salviifolium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e80.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGrevillea robusta\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e64.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMuntingia calabura\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePhyllanthus acidus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e66.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePsidium guajava\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e62.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e86.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMoringa oleifera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e75.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e66.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMorus alba\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e82.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e79.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLeucaena leucocephala\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e66.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e50.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFicus tinctoria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e89.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e87.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCommiphora caudata\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e84.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e90.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.56\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\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\u003eAPTI values of southern transect\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTransition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRural\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\u003eMangifera indica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePongamia pinnata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMurraya koenigii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAzadirachta indica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTectona grandis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAlangium salviifolium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrevillea robusta\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMuntingia calabura\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePhyllanthus acidus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePsidium guajava\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMoringa oleifera\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMorus alba\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeucaena leucocephala\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFicus tinctoria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCommiphora caudata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.12\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\u003eThe Air Pollution Tolerance Index (APTI) has been widely employed across different geographic regions to evaluate the tolerance of plant species to atmospheric pollutants. Several studies across industrial and urban landscapes have yielded insightful comparisons of plant species in relation to their pollution response mechanisms.\u003c/p\u003e\u003cp\u003eIn Beijing, China, a study near a steel industry identified \u003cem\u003eBroussonetia papyrifera\u003c/em\u003e, \u003cem\u003eRobinia pseudoacacia\u003c/em\u003e, and \u003cem\u003eAilanthus altissima\u003c/em\u003e as species with moderate to high tolerance to industrial emissions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, in Delta State, Nigeria, species such as \u003cem\u003eEmilia samtifolia\u003c/em\u003e, \u003cem\u003eManihot esculenta\u003c/em\u003e, and \u003cem\u003eElaeis guineensis\u003c/em\u003e exhibited higher APTI values near the Otorogun Gas Plant, indicating their resilience in a polluted environment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. At the Erhoike-Kokori oil drilling site in the same region, \u003cem\u003eMangifera indica\u003c/em\u003e and \u003cem\u003eChromolaena odorata\u003c/em\u003e topped the tolerance rankings, while \u003cem\u003ePsidium guajava\u003c/em\u003e and \u003cem\u003eElaeis guineensis\u003c/em\u003e showed lower tolerance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Enugu, Nigeria, APTI analysis in both urban and industrial zones revealed that tree species generally outperformed ornamental shrubs in pollution tolerance. \u003cem\u003eAnacardium occidentale\u003c/em\u003e, \u003cem\u003ePinus spp.\u003c/em\u003e, and \u003cem\u003eCatalpa bungei\u003c/em\u003e recorded higher APTI values than shrubs, suggesting their suitability for urban landscaping to combat heat island effects [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, in the Ama industrial area of Enugu, \u003cem\u003eDelonix regia\u003c/em\u003e (APTI: 5.308\u0026thinsp;\u0026plusmn;\u0026thinsp;0.090) showed significantly higher tolerance compared to \u003cem\u003eAnacardium occidentale\u003c/em\u003e (APTI: 3.470\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Pulau Pinang, Malaysia, comparative evaluation between \u003cem\u003eFicus sp.\u003c/em\u003e and \u003cem\u003eBougainvillea sp.\u003c/em\u003e along major roads showed that \u003cem\u003eFicus\u003c/em\u003e exhibited greater APTI values, indicating higher tolerance, while \u003cem\u003eBougainvillea\u003c/em\u003e was more sensitive and suitable as a biological indicator [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn C\u0026ocirc;te d'Ivoire, \u003cem\u003eFicus benjamina\u003c/em\u003e recorded the highest APTI among ornamental plants (up to 17.15), followed by \u003cem\u003eJatropha integerrima\u003c/em\u003e and \u003cem\u003eBarleria prionitis\u003c/em\u003e. \u003cem\u003eCassia surattensis\u003c/em\u003e was the most sensitive species based on APTI scores [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Studies from Ahvaz, Iran, showed notable spatial variation in APTI values across polluted and non-polluted sites. \u003cem\u003eMyrtus\u003c/em\u003e exhibited the highest tolerance in both areas, while \u003cem\u003eProsopis\u003c/em\u003e showed vulnerability to pollution, with its APTI dropping from 4.97 (unpolluted) to 4.57 (polluted) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the Babylon region of Mesopotamia, \u003cem\u003eConocarpus lancifolius\u003c/em\u003e was found tolerant, while \u003cem\u003eDodonaea viscosa\u003c/em\u003e was more sensitive, as influenced by prevailing wind directions and proximity to pollution sources [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA notable observation from several studies is the positive correlation between higher ascorbic acid content and SO₂ tolerance, supporting the use of physiological parameters in APTI computation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In Indian studies, \u003cem\u003eMangifera indica\u003c/em\u003e, \u003cem\u003eAlstonia scholaris\u003c/em\u003e, and \u003cem\u003eEupatorium odoratum\u003c/em\u003e emerged as effective biomonitors of vehicular pollution [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Bangalore, India, field comparisons showed that \u003cem\u003eBougainvillea spectabilis\u003c/em\u003e and \u003cem\u003eAgeratum conyzoides\u003c/em\u003e had significant drops in APTI values from control to polluted sites, indicating high sensitivity, whereas \u003cem\u003ePeltophorum pterocarpum\u003c/em\u003e and \u003cem\u003ePortulaca oleraceae\u003c/em\u003e retained relatively stable scores, marking them as pollution-tolerant species [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA broader study assessing APTI and Anticipated Performance Index (API) for 29 species showed that \u003cem\u003eFicus benghalensis\u003c/em\u003e, \u003cem\u003eCassia fistula\u003c/em\u003e, and \u003cem\u003eFicus religiosa\u003c/em\u003e had the highest tolerance scores, while \u003cem\u003eMangifera indica\u003c/em\u003e showed lower tolerance in comparison. In marble industrial zones of Potwar, species such as \u003cem\u003ePolyalthia longifolia\u003c/em\u003e, \u003cem\u003eFicus glomerata\u003c/em\u003e, and \u003cem\u003eAilanthus indicus\u003c/em\u003e were noted for their resilience [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Navsari, India, \u003cem\u003eCassia fistula\u003c/em\u003e outperformed other species like \u003cem\u003eSaraca asoca\u003c/em\u003e and \u003cem\u003eSyzygium cumini\u003c/em\u003e in terms of APTI, affirming its tolerance in urban polluted environments. In contrast, \u003cem\u003eTectona grandis\u003c/em\u003e and \u003cem\u003eTerminalia catappa\u003c/em\u003e showed moderate sensitivity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Lastly, in Dehradun, \u003cem\u003eEucalyptus globulus\u003c/em\u003e exhibited the highest APTI values, followed by \u003cem\u003eFicus religiosa\u003c/em\u003e and \u003cem\u003eMangifera indica\u003c/em\u003e, while lower APTI was observed in species like \u003cem\u003eLantana camara\u003c/em\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe APTI values indicated that several tree species found along the northern and southern transects exhibit relatively higher tolerance to air pollution. In the northern transect, species such as \u003cem\u003eMangifera indica\u003c/em\u003e, \u003cem\u003ePsidium guajava\u003c/em\u003e, \u003cem\u003eFicus glomerata\u003c/em\u003e, and \u003cem\u003ePongamia pinnata\u003c/em\u003e were identified as pollution-tolerant. Similarly, in the southern transect, \u003cem\u003eMangifera indica\u003c/em\u003e, \u003cem\u003eFicus tinctoria\u003c/em\u003e, \u003cem\u003eCommiphora caudata\u003c/em\u003e, and \u003cem\u003eAlangium salviifolium\u003c/em\u003e demonstrated higher tolerance levels. These findings underscore the importance of selecting tree species based on the local air quality index (AQI). In urban environments\u0026mdash;where pollution levels are often elevated\u0026mdash;prioritizing species with high APTI values is critical for the success and sustainability of urban forestry and landscape planning initiatives. Pollution-tolerant species not only have a higher likelihood of survival in degraded environments, but also contribute significantly to mitigating air pollution, reducing urban heat island effects, and enhancing biodiversity. Therefore, incorporating APTI as a guiding criterion in species selection can improve the resilience and ecological performance of urban green spaces. This approach ensures that planted trees are better adapted to prevailing environmental stresses, thereby maximizing the long-term benefits of urban forests. In turn, this supports the development of healthier, greener, and more sustainable cities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors express their sincere gratitude to the Director, Institute of Wood Science and Technology (IWST), for providing the necessary facilities and institutional support to carry out this study. The research was funded by the Department of Biotechnology (DBT), Government of India, under the Indo-German collaborative program, which is gratefully acknowledged. The authors also extend their appreciation to the German collaborators, Dr. Christoph Kleinn and Dr. Nils Nölke from Georg-August-University, Göttingen, Germany, for their valuable contributions to the Indo-German research consortium. Their support in providing WorldView-3 satellite imagery was instrumental in the layout and selection of plots along the urban–rural gradient across the northern and southern transects of Bengaluru.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe study was funded by the \u003cstrong\u003eDepartment of Biotechnology (DBT), Government of India, New Delhi\u003c/strong\u003e, under the \u003cstrong\u003eIndo-German collaborative research programme.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eAll authors have approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.N.D.\u003c/strong\u003e conceptualized, planned, and executed the fieldwork, and led the drafting of the manuscript.\u003cbr\u003e\u003cstrong\u003eC.U.N.\u003c/strong\u003e assisted in data collection, compilation, and contributed to the manuscript drafting.\u003cbr\u003e\u003cstrong\u003eV.P.T.\u003c/strong\u003e supported the execution of the study, reviewed the manuscript, and provided valuable suggestions for its improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have received no financial support for the preparation of this manuscript and have no competing interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to participate:\u0026nbsp;\u003c/strong\u003eThe collection of leaf samples in this study was carried out in accordance with applicable local and national guidelines. All samples were obtained from cultivated plants, and no endangered or protected species were involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u0026nbsp;\u003c/strong\u003eAll authors have read and approved the final manuscript and consent to its publication.\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgbaire PO, Esiefarienrhe E. Air Pollution Tolerance Indices (APTI) of some plants around Otorogun Gas Plant in Delta State, Nigeria. J Appl Sci Environ Manage. 2009;13(1):11\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgbaire PO. 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Environ Pollution Ser Ecol Biol. 1984;35(4):285\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urban–Rural gradient, Tree Health, Air Pollution Tolerance Index (APTI), Tolerant species, Bengaluru","lastPublishedDoi":"10.21203/rs.3.rs-6946956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6946956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban forestry involves the strategic management of trees within urban landscapes to enhance ecosystem services, improve biodiversity, mitigate climate change, and reduce the urban heat island effect. In addition to their ecological value, urban trees contribute aesthetic, economic, and functional benefits—such as increasing property value and attracting tourism. This study aimed to assess the health of urban trees exposed to various anthropogenic pressures in Bengaluru city. A total of 48 one-hectare plots were established—23 in the northern and 25 in the southern transects of the city. Trees with a diameter at breast height (DBH) of ≥10 cm were selected for analysis. Based on the extent of tree cover and built-up area, plots were categorized into urban, transition, and rural zones. Common tree species from each zone were evaluated for their Air Pollution Tolerance Index (APTI) in both transects. Fresh leaf samples were collected and analyzed for four key biochemical parameters: leaf pH, ascorbic acid content, relative water content, and total chlorophyll content. The results revealed that some species exhibited greater tolerance to air pollution. In the northern transect, \u003cem\u003eMangifera indica\u003c/em\u003e, \u003cem\u003ePsidium guajava\u003c/em\u003e, \u003cem\u003eFicus glomerata\u003c/em\u003e, and \u003cem\u003ePongamia pinnata\u003c/em\u003e demonstrated higher APTI values, indicating greater tolerance. In the southern transect, \u003cem\u003eMangifera indica\u003c/em\u003e, \u003cem\u003eFicus tinctoria\u003c/em\u003e, \u003cem\u003eCommiphora caudata\u003c/em\u003e, and \u003cem\u003eAlangium salviifolium\u003c/em\u003e were identified as more tolerant. This study underscores the adaptive capacity of specific tree species to varying pollution levels and offers valuable insights for urban greening initiatives, species selection, and sustainable city planning aimed at enhancing ecological resilience and air quality in metropolitan areas.\u003c/p\u003e","manuscriptTitle":"Health Status of Street Trees Along the Urban–Rural Gradient in the Garden City of Bengaluru, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 12:01:51","doi":"10.21203/rs.3.rs-6946956/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63f33bd9-3637-4b35-95e7-e947148328f0","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T14:08:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 12:01:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6946956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6946956","identity":"rs-6946956","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}