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Assessment of air pollutants and pollution tolerant tree species for the development of Greenbelt at Narasapura Industrial Estate, India

Assessment of air pollutants and pollution tolerant tree species for the development of Greenbelt... GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2022.2144857 RESEARCH ARTICLE Assessment of air pollutants and pollution tolerant tree species for the development of Greenbelt at Narasapura Industrial Estate, India L. Arul Pragasan and N. Ganesan Environmental Ecology Lab, Department of Environmental Sciences, Bharathiar University, Coimbatore 641 046, India ABSTRACT ARTICLE HISTORY Received 28 January 2022 In this study, we assessed the concentration of air pollutants to understand the pollution status Revised 17 October 2022 of the Narasapura industrial area located in India. Also, we identified pollution-tolerant tree Accepted 3 November 2022 species for the development of greenbelts for NIA. Monthly air samples were collected from three sites from NIA and the samples were analysed for the determination of air pollutant KEYWORDS concentration following standard methods. Air pollutants such as PM10, PM2.5, SO , NO , Pb, 2 2 Air pollutants; PM10; PM2.5; CO, NH , and O were detected and their concentrations for the three sites ranged from 21 to APTI; spathodea 3 3 3 3 3 3 3 3 99 μg/m , 11 to 67 μg/m , 3 to 14 μg/m , 5 to 28 μg/m , 0.01 to 0.9 μg/m , 0.3 to 0.9 mg/m , 3 campanulata; greenbelts 3 3 to 17 μg/m , 6 to 25 μg/m , respectively. Twenty common tree species to NIA were selected and their air pollution tolerance potential was determined by the Air pollution tolerance index using leaf relative water content, total chlorophyll content, leaf extract pH, and ascorbic acid content. Tree species, Spathodea campanulata (9.58 ± 0.33) recorded maximum APTI value followed by Terminalia catappa, Tabebuia avellanedae, Anthocephalus cadamba, and Syzygium jambos. We conclude that the development of greenbelts is necessary for the mitigation of air pollutants. Introduction in sensitive species and the lowest intolerant ones Air pollutants are a major threat to all living beings and (Govindaraju et al., 2012). Air pollutants have an ecosystem sustainability (Hatamimanesh et al., 2021). impact on the plant system, beginning with biochem- ical alterations and progressing through structural and The earth’s atmosphere has been polluted highly before the industrial revolution. The sources of pollutants are functional changes in the leaf, all the way to the land- mostly anthropogenic activities, and scientists relate scape level (Rai, 2016). that the increase in air pollutants is attributed to uncon- An essential factor that defines a plant’s capacity to trolled industrialization, inadequate emission manage- withstand air pollution is the Air Pollution Tolerance ment, and a lack of strong environmental policies (Roy Index (APTI), and plants with a higher index value et al., 2020). Besides astonishing growth in the medical can act as natural sinks for CO sequestration. (Sahu & sector, pollutants are pulling down human health con- Sahu, 2015). Some fundamental biological factors, ditions through threats such as cardiovascular disease, such as ascorbic acid, total chlorophyll, relative water respiratory problems, lung cancer, infertility, and pre- content, and leaf extract pH, affect how sensitive and tolerant plants are to air pollution. Singh and Rao mature mortality (Hatamimanesh et al., 2021). Air qual- ity for the past two decades has deteriorated (1983) created the APTI, which is derived using dramatically, as a result of industrial emissions, trans- these four biochemical characteristics. By adding APTI values to other biological and socioeconomic portation, urbanization, and a significant decline in vegetation. Industry-related air pollution is a key source characteristics, it is possible to generate the of concern for air quality degradation, especially, in Anticipated Performance Index (API) for the plants. developing countries, where severe air pollution is (Sharma et al., 2020). The primary greenbelt compo- a big concern (Gros Valerie et al., 2007). nent, plants, serve as a sink and living filters to reduce Air pollution has the potential to have negative air pollution through absorption, adsorption, detoxi- health consequences on not only humans but also on fication, accumulation, and/or metabolization without suffering serious foliar damage or growth decline, trees and other animals in the environment (Rai & Panda, 2014). Air pollution in certain industrial areas improving air quality by supplying oxygen to the is adsorbed, collected, and integrated into the plant atmosphere (Kapoor & Chittora, 2016). Greenbelts are a natural technique to minimize system. Toxic air pollution can injure plants in a variety of ways, with the level of impact being highest pollution in the atmosphere by trapping particulates CONTACT L. Arul Pragasan arulpragasan@buc.edu Environmental Ecology Lab, Department of Environmental Sciences, Bharathiar University, Coimbatore 641 046, Tamil Nadu, India © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 L. A. PRAGASAN AND N. GANESAN and absorbing gaseous pollutants (Han et al., 2021). Commercial Vehicles Ltd. The district’s landscape is This concept was first created by Englishman Sir varied, ranging from undulating to flat terrain, with Ebenezer Howard (Howard & Osborn, 1965). red loamy, red sandy and lateritic soils. The mean However, only a few nations have implemented green- monthly maximum and minimum temperatures for belts that include the United Kingdom, Canada, Kolar over eight years (2009–2016) are 31°C and 20°C, Australia, the United States, and South Korea (Han respectively. For the same period, the average annual et al., 2021). Greenbelts not only help to reduce air rainfall and rainy days are 1212 mm and 201 days pollution, but also reduce noise pollution, prevent soil (Ganesan & Pragasan, 2017). erosion, increase water harvesting, and improve the region’s scenic value (Yang & Jinxing, 2007). Growing Assessment of air quality resistant plants in and around polluted areas is a new strategy that has been adopted in recent years (Roy Air samples were taken monthly at three sites in NIA et al., 2020), as emphasized by scientists and policy- to measure the air quality, from April 2014 to makers. The widespread planting of pollutant-tolerant March 2016. Standard methods, as per National trees in industrial sectors can make a significant con- Ambient Air Quality Standards and Central tribution to improving air quality. Pollution Control Board standards were followed to So far no study has been reported on either air determine the concentration of air pollutants such as pollutant concentration or greenbelts of Narasapura PM10, PM2.5, SO , NO , Pb, CO, NH , C H , Benzo 2 2 3 6 6 Industrial Area (NIA) located in India. Thus, the pre- (a) Pyrene (BaP), As, Ni, and O sent study was carried out to assess the concentration of air pollutants, and also to evaluate the pollution Determination of air pollution tolerance levels of tolerance levels of common tree species for the devel- tree species opment of greenbelts as an air pollution management strategy for mitigation of air pollutants. Twenty tropical tree species common to the study area were chosen, and their air pollution tolerance levels were investigated to determine their potential for Materials and methods greenbelt development. The Air Pollution Tolerance Index (APTI), which is regarded as an excellent Study area method was used to estimate the air pollution toler- NIA (13.13° N and 78.13° E) is located in the Kolar ance levels for each tree species (Roy et al., 2020). district of Karnataka state, India (Figure 1). It is During 2015, matured leaf samples in triplicates were located 15 km from Kolar and is one of the city’s collected (in polythene bags and labeled) for each main industrial areas. The major enterprises in the species in the early morning hours of 6 a.m. to 9.00 study area include Honda Motor and Scooter India a.m. for the three major seasons of winter, summer, Pvt Ltd., ASK Auto, Mahindra Aerospace, and Scania and rainy. Leaf samples were transferred immediately Figure 1. Map showing the location of the sampling sites at Narasapusa industrial area in Kolar district of Karnataka state in India. GEOLOGY, ECOLOGY, AND LANDSCAPES 3 3 3 to the lab for further biochemical investigation for the 0.70 ± 0.12 mg/m , 13.17 ± 4.87 μg/m and 7.2 ± 2.89 μg/ determination of APTI for each species following m (Figure 2). Similarly, at site 2, the mean concentration Singh and Rao (1983); for PM10, PM2.5, SO , NO , Pb, CO, NH , and O were 2 2 3 3 3 3 APTI = (A (T + P) +R)/10 (Eq.1) 64.13 ± 21.33 μg/m , 25.88 ± 10.97 μg/m , 6.04 ± 1.92 μg/ 3 3 3 where, A stands for ascorbic acid, T stands for total m , 11.50 ± 5.91 μg/m , 0.17 ± 0.11 μg/m , 3 3 chlorophyll, P stands for pH and R stands for relative 0.64 ± 0.10 mg/m , 12.50 ± 4.63 μg/m and water content. Based on the APTI value, the 20 selected 6.71 ± 2.73 μg/m (Figure 3). And at site 3, the mean tree species were categorized into three levels of air concentration for PM10, PM2.5, SO , NO , Pb, CO, 2 2 pollution tolerance: tolerant (if the APTI value is >7), NH , and O were 63.67 ± 21.84 μg/m , 3 3 3 3 moderately tolerant (if the APTI value is between 6 and 31.58 ± 13.79 μg/m , 6.58 ± 2.10 μg/m , 3 3 4), and sensitive (if the APTI value is <3). Growth 12.58 ± 5.59 μg/m , 0.18 ± 0.12 μg/m , 0.61 ± 0.10 mg/ 3 3 3 parameters such as shoot length (SL), leaf length (LL) m , 12.21 ± 3.71 μg/m and 6.71 ± 2.49 μg/m (Figure 4). and leaf width (LW) were recorded once in two weeks. One-way ANOVA revealed that all the air pollutants did The plants were then collected and utilized for further not vary significantly between the three study sites, investigation. SL, LL, and LW were measured using except CO (F = 4.573, p < 0.05; Table 1). (2,69) a measuring scale, and C content was calculated using the loss on ignition method. Relationship between air pollutants The results of the correlation analysis between the air Data analysis pollutants at site 1 are provided in Table 2. It reveals that PM10 had a positive relation with PM2.5, Pb and CO. One-way ANOVA was used to check the significance PM2.5 had positive relation only with PM10. SO had level for monthly fluctuation in the concentration of a positive relation with NO , Pb and CO. NO had 2 2 air pollutants, and to compare the fluctuation in APTI a positive relation with SO and Pb. Pb had a positive values for the 20 selected species across different sea- relation with PM10, SO , NO and CO, and it had 2 2 sons. Correlation analysis was used to understand the a negative relation with NH and O . CO had a positive 3 3 relationship between the air pollutants. relation with PM10, SO and Pb. NH had a positive 2 3 relation with O and negative relation with Pb. O had 3 3 Results a positive relation with NH and negative relation with Pb. Concentration of air pollutants Table 3 provides the results of the correlation ana- In all three sites of the study area, only eight air pollutants lysis between the air pollutants at site 2. It reveals that such as PM10, PM2.5, SO , NO , Pb, CO, NH and O PM10 had a positive relation with PM2.5, SO and 2 2 3, 3 2 were detected. While, the concentrations for C H , B(a) CO. PM2.5 had positive relation only with PM10. SO 6 6 2 P, As, and Ni, were not detected in the air samples during had a positive relation with PM10, NO , Pb, and CO. the study period, which may be attributed to the absence NO had a positive relation with SO , Pb and CO. Pb 2 2 of emission sources. At site 1, the mean (± S.D.) con- had a positive relation with SO , NO , and CO, and it 2 2 centration for PM10, PM2.5, SO , NO , Pb, CO, NH had a negative relation with NH and O . CO had 2 2 3 3 3 3 3 and O were 64.75 ± 18.08 μg/m , 27.04 ± 11.12 μg/m , a positive relation with PM10, SO , NO and Pb. 3 2 2 3 3 3 6.92 ± 2.57 μg/m , 14.04 ± 6.77 μg/m , 0.21 ± 0.13 μg/m , NH had a positive relation with O and negative 3 3 120.0 100.0 80.0 Q1 60.0 Min 40.0 Median Max 20.0 Q3 0.0 PM10 PM2.5 Pb CO SO NO NH O 2 3 3 Site 1 3 3 Figure 2. Box plot showing the concentration of air pollutants at site 1. The unit for all the pollutants is μg/m except CO (mg/m ). 3 3 Concentration, µg/m or mg/m 4 L. A. PRAGASAN AND N. GANESAN 100.0 90.0 80.0 70.0 60.0 Q1 50.0 Min 40.0 Median 30.0 20.0 Max 10.0 Q3 0.0 PM10 PM2.5 Pb CO SO NO NH 2 2 3 Site 2 3 3 Figure 3. Box plot showing the concentration of air pollutants at site 2. The unit for all the pollutants is μg/m except CO (mg/m ). 120.0 100.0 80.0 Q1 60.0 Min 40.0 Median Max 20.0 Q3 0.0 PM10 PM2.5 Pb CO SO NO O NH 2 2 3 Site 3 3 3 Figure 4. Box plot showing the concentration of air pollutants at site 3. The unit for all the pollutants is μg/m except CO (mg/m ). Table 1. Analysis of variance for the air pollutants concentrations between the three sites. Air pollutant Source of Variation SS df MS F-value P-value F crit PM10 Between Groups 14.194 2 7.097 0.017 0.983 3.130 Within Groups 28,962.460 69 419.746 Total 28,976.650 71 PM2.5 Between Groups 436.583 2 218.292 1.509 0.228 3.130 Within Groups 9983.417 69 144.687 Total 10,420.000 71 SO Between Groups 9.361 2 4.681 0.954 0.390 3.130 Within Groups 338.625 69 4.908 Total 347.986 71 NO Between Groups 78.083 2 39.042 1.045 0.357 3.130 Within Groups 2576.792 69 37.345 Total 2654.875 71 Pb Between Groups 0.021 2 0.011 0.740 0.481 3.130 Within Groups 1.000 69 0.014 Total 1.021 71 CO Between Groups 0.105 2 0.053 4.573 0.014 3.130 Within Groups 0.794 69 0.012 Total 0.899 71 NH Between Groups 11.583 2 5.792 0.295 0.746 3.130 Within Groups 1355.292 69 19.642 Total 1366.875 71 O Between Groups 5.444 2 2.722 0.371 0.692 3.130 Within Groups 506.875 69 7.346 Total 512.319 71 SS-sum of squares, df-degree of freedom, MS-mean square,F crit-F critical value. 3 3 3 3 Concentration, µg/m or mg/m Concentration, µg/m or mg/m GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Table 2. Correlation table for the air pollutants at Site 1. Air pollutant PM10 PM2.5 SO NO Pb CO NH O 2 2 3 3 PM10 Pearson coefficient 1 0.495* 0.205 0.176 0.506* 0.721** 0.001 0.171 P (2-tailed) 0.014 0.336 0.411 0.012 0.000 0.996 0.425 N 24 24 24 24 24 24 24 24 PM2.5 Pearson coefficient 0.495* 1 0.137 −0.154 −0.127 0.356 0.144 0.299 P (2-tailed) 0.014 0.523 0.473 0.555 0.088 0.501 0.155 N 24 24 24 24 24 24 24 24 SO Pearson coefficient 0.205 0.137 1 0.803** 0.536** 0.552** −0.180 −0.131 P (2-tailed) 0.336 0.523 0.000 0.007 0.005 0.401 0.542 N 24 24 24 24 24 24 24 24 NO Pearson coefficient 0.176 −0.154 0.803** 1 0.658** 0.343 −0.285 −0.289 P (2-tailed) 0.411 0.473 0.000 0.000 0.100 0.177 0.171 N 24 24 24 24 24 24 24 24 Pb Pearson coefficient 0.506* −0.127 0.536** 0.658** 1 0.621** −0.625** −0.564** P (2-tailed) 0.012 0.555 .007 0.000 0.001 0.001 0.004 N 24 24 24 24 24 24 24 24 CO Pearson coefficient 0.721** 0.356 0.552** 0.343 0.621** 1 −0.009 0.034 P (2-tailed) 0.000 0.088 0.005 0.100 0.001 0.968 0.875 N 24 24 24 24 24 24 24 24 NH Pearson 0.001 0.144 −0.180 −0.285 −0.625** −0.009 1 0.909** coefficient P (2-tailed) 0.996 0.501 0.401 0.177 0.001 0.968 0.000 N 24 24 24 24 24 24 24 24 O Pearson coefficient 0.171 0.299 −0.131 −0.289 −0.564** 0.034 0.909** 1 P (2-tailed) 0.425 0.155 0.542 0.171 0.004 0.875 0.000 N 24 24 24 24 24 24 24 24 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Table 3. Correlation table for the air pollutants at Site 2. Air pollutant PM10 PM2.5 SO NO Pb CO NH O 2 2 3 3 PM10 Pearson coefficient 1 0.423* 0.469* 0.360 0.176 0.540** 0.392 0.395 P (2-tailed) 0.039 0.021 0.084 0.412 0.006 0.058 0.056 N 24 24 24 24 24 24 24 24 PM2.5 Pearson coefficient 0.423* 1 0.209 0.244 0.051 0.309 0.188 0.241 P (2-tailed) 0.039 0.328 0.251 0.814 0.142 0.380 0.256 N 24 24 24 24 24 24 24 24 SO Pearson coefficient 0.469* 0.209 1 0.828** 0.709** 0.680** −0.066 0.011 P (2-tailed) 0.021 0.328 0.000 0.000 0.000 0.760 0.960 N 24 24 24 24 24 24 24 24 NO Pearson coefficient 0.360 0.244 0.828** 1 0.764** 0.600** −0.167 −0.109 P (2-tailed) 0.084 0.251 0.000 0.000 0.002 0.437 0.611 N 24 24 24 24 24 24 24 24 Pb Pearson coefficient 0.176 0.051 0.709** 0.764** 1 0.601** −0.528** −0.468* P (2-tailed) 0.412 0.814 0.000 0.000 0.002 0.008 0.021 N 24 24 24 24 24 24 24 24 CO Pearson coefficient 0.540** 0.309 0.680** 0.600** 0.601** 1 0.111 0.218 P (2-tailed) 0.006 0.142 0.000 0.002 0.002 0.607 0.306 N 24 24 24 24 24 24 24 24 NH Pearson 0.392 0.188 −0.066 −0.167 −0.528** 0.111 1 0.972** coefficient P (2-tailed) 0.058 0.380 0.760 0.437 0.008 0.607 0.000 N 24 24 24 24 24 24 24 24 O Pearson coefficient 0.395 0.241 0.011 −0.109 −0.468* 0.218 0.972** 1 P (2-tailed) 0.056 0.256 0.960 0.611 0.021 0.306 0.000 N 24 24 24 24 24 24 24 24 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). relation with Pb. O had a positive relation with NH O had a positive relation with NH3 and negative rela- 3 3 3 and negative relation with Pb. tion with Pb. The results of the correlation analysis between the air pollutants at site 3 are provided in Table 4. It reveals that APTI value of tree species PM10 had positive relation only with CO. PM2.5 had a positive relation with PM10 and PM2.5. SO had The air pollution tolerance levels of the 20 tree species a positive relation with PM2.5, NO , Pb, and CO. NO investigated in this study are provided along with their 2 2 had a positive relation with SO , Pb and CO. Pb had APTI values for the three seasons in Table 5. Tree a positive relation with SO , NO , and CO, and negative species, Spathodea campanulata (9.58 ± 0.33) scored 2 2 relation with NH and O . CO had a positive relation the highest mean APTI value followed by Terminalia 3 3 with PM10, PM2.5, SO , NO , and Pb. NH had catappa, Tabebuia avellanedae, Anthocephalus 2 2 3 a positive relation with O and negative relation with Pb. cadamba, Syzygium jambos, and Ficus benghalensis. 3 6 L. A. PRAGASAN AND N. GANESAN Table 4. Correlation table for the air pollutants at Site 3. Air pollutant PM10 PM2.5 SO NO Pb CO NH O 2 2 3 3 PM10 Pearson coefficient 1 0.302 0.309 0.268 0.396 0.650** 0.168 0.185 P (2-tailed) 0.151 0.142 0.206 0.056 0.001 0.433 0.387 N 24 24 24 24 24 24 24 24 PM2.5 Pearson coefficient 0.302 1 0.466* 0.274 0.258 0.440* −0.042 0.049 P (2-tailed) 0.151 0.022 0.195 0.223 0.032 0.844 0.819 N 24 24 24 24 24 24 24 24 SO Pearson coefficient 0.309 0.466* 1 0.716** 0.489* 0.755** 0.067 0.150 P (2-tailed) 0.142 0.022 0.000 0.015 0.000 0.754 0.485 N 24 24 24 24 24 24 24 24 NO Pearson coefficient 0.268 0.274 0.716** 1 0.628** 0.503* −0.249 −0.218 P (2-tailed) 0.206 0.195 0.000 0.001 0.012 0.240 0.306 N 24 24 24 24 24 24 24 24 Pb Pearson coefficient 0.396 0.258 0.489* 0.628** 1 0.483* −0.591** −0.526** P (2-tailed) 0.056 0.223 0.015 0.001 0.017 0.002 0.008 N 24 24 24 24 24 24 24 24 CO Pearson coefficient 0.650** 0.440* 0.755** 0.503* 0.483* 1 0.229 0.279 P (2-tailed) 0.001 0.032 0.000 0.012 0.017 0.281 0.187 N 24 24 24 24 24 24 24 24 NH Pearson 0.168 −0.042 0.067 −0.249 −0.591** 0.229 1 0.948** coefficient P (2-tailed) 0.433 0.844 0.754 0.240 0.002 0.281 0.000 N 24 24 24 24 24 24 24 24 O Pearson coefficient 0.185 0.049 0.150 −0.218 −0.526** 0.279 0.948** 1 P (2-tailed) 0.387 0.819 0.485 0.306 0.008 0.187 0.000 N 24 24 24 24 24 24 24 24 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). All the species fell under the tolerant category except development and economic impacts, aesthetic appeal Azadirachta indica, Pongamia pinnata, and Samanea and biodiversity, psychological relaxation and stress saman which fell under the intermediate category. relief, offices with greenery that increase productivity, One-way ANOVA revealed that the APTI value did stimulate social cohesion and interaction, support not vary significantly among the tree species across physical fitness and activities, and provide healthier, three seasons (F = 1.603, p > 0.05). more tranquil outdoor living spaces (Jabbar et al., (2,57) 2021). Data on air quality must be evaluated in a variety of Discussion ways to record and research the effects. Huang et al. Green spaces reduce the creation of photochemical (2015) stated emphatically about the monitoring data ozone, which helps to reduce air pollution by cooling in Xi’an, China, that if simply the mean values from down cities and minimizing the urban heat island raw data were examined, several facts and impacts effect. Urban trees’ shade also lowers energy use, would be overlooked, such as some significant which indirectly improves air quality (Akbari et al., monthly or seasonal abrupt changes. In the present 2001). The benefits of urban green spaces include study, air pollutants were analysed on monthly basis to better environmental conditions, sustainable assess the significance of variation in the Table 5. Air pollution tolerance levels of the twenty selected tree species in NIA based on APTI value. APTI value Name of the tree species Winter Summer Rainy Mean (± S.D.) TL Anthocephalus cadamba Miq. 9.63 8.36 9.85 9.28 0.80 T Azadirachta indica A.Juss. 6.67 5.01 8.02 6.57 1.51 IM Bauhinia variegata L. 8.53 9.09 9.34 8.99 0.41 T Dalbergia melanoxylon Guill. & Perr. 8.93 7.19 8.99 8.37 1.02 T Ficus benghalensis L. 9.09 9.02 9.27 9.13 0.13 T Grevillea robusta A.Cunn. 8.67 9.17 9.41 9.08 0.38 T Markhamia platycalyx Sprague 8.21 7.7 7.94 7.95 0.26 T Millingtonia hortensis L.f. 8.28 8.27 8.45 8.33 0.10 T Morus nigra L. 8.32 7.84 8.03 8.06 0.24 T Muntingia calabura L. 6.7 7.14 7.38 7.07 0.34 T Peltophorum pterocarpum Backer ex K.Heyne 8.88 8.27 8.65 8.60 0.31 T Polyalthia longifolia (Sonn.) Hook.f. & Thomson 8.66 8.01 9.91 8.86 0.97 T Pongamia pinnata (L.) Merr. 5.52 5.26 6.36 5.71 0.57 IM Samanea saman (Jacq.) Merr. 6.51 5.14 6.35 6.00 0.75 IM Spathodea campanulata Buch.-Ham. ex DC. 9.95 9.3 9.5 9.58 0.33 T Swietenia macrophylla King 8.84 8.98 9.23 9.02 0.20 T Syzygium jambos (L.) Alston 8.86 9.22 9.52 9.20 0.33 T Tabebuia avellanedae Lorentz ex Griseb. 9.05 9.46 9.55 9.35 0.27 T Terminalia catappa L. 8.69 9.63 9.87 9.40 0.62 T Thespesia populnea Sol. ex Correa 8.56 8.04 8.29 8.30 0.26 T TL- air pollution tolerance levels, T- tolerant, IM-intermediate. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 concentration of each air pollutant. The concentra- APTI tions of air pollutants PM10, PM2.5, SO , NO , Pb, 2 2 The selection of air pollution tolerant tree species for CO, NH , and O detected in study area ranged from 3 3 greenbelts development is mostly carried out using the 3 3 3 3 3 26 μg/m to 98 μg/m , 11 μg/m to 60 μg/m , 3 μg/m APTI method (Ghafari et al., 2021). Similarly, in the 3 3 3 3 to 13 μg/m , 5 μg/m to 50 μg/m , 0.01 μg/m to present study APTI method was followed to understand 3 3 3 3 0.4 μg/m , 0.5 mg/m to 0.9 mg/m , 7 μg/m to the air pollution tolerance levels of 20 selected tree 3 3 3 25 μg/m , 4 μg/m to 14 μg/m , respectively. When species. All the species fell under the tolerant category compared, the mean annual concentrations of all the except Azadirachta indica, Pongamia pinnata and air pollutants detected in this study are within the Samanea saman which fell under the intermediate cate- CPCB limits except PM10 (Table 6). The mean annual gory. Spathodea campanulata scored the highest APTI concentrations for the second year (2015–2016) are value followed by Terminalia catappa, Tabebuia avella- greater than the first year (2014–2015) for PM10, SO , nedae, Anthocephalus cadamba, Syzygium jambos, and NO , Pb and CO, revealing an increasing trend. While, Ficus benghalensis. While, in our earlier report from for PM2.5, NH and O the values of the second year 3 3 Tamaka industrial site, Terminalia catappa scored the are lesser than the first year, revealing the decreasing highest APTI value followed by Polyalthia longifolia, trend for these pollutants. This variation may be Anthocephalus cadamba, Grevillea robusta and attributed to the annual variation in the rate of pro- Peltophorum pterocarpum (Ganesan & Pragasan, duction and release of air pollutants from the 2017). Whereas, Pandey et al. (2015) reported that enterprises. Ficus benghalensis scored the highest APTI value fol- Rapid industrial expansion in the last two dec- lowed by Cassia fistula, Ficus religiosa, Polyalthia long- ades has undoubtedly enhanced the level of living ifolia, Drypetes roxburghii, and Zizyphus jujuba out of of citizens in India, as evidenced by the growing 29 tree species studied at Varanasi city. This is clear that number of vehicles on the highways. However, our the APTI value of any species varies with the study environmental health condition deteriorates as we region with varied environmental conditions particu- inhale the poisoned air due to this development. larly air pollution levels and climatic regimes. Similarly, Approximately 7 million people die every year due Noor et al. (2015) stated that the air pollution tolerance to particulate air pollution worldwide, and 12.5% of levels of plant species vary with site and depend on the all deaths in India are due to air pollution type and level of pollution. (Lokhandwala & Gautam, 2020). Mitigation mea- In the present study, the APTI value of tree species sures are a high need for the sustainable develop- did not vary significantly across three seasons ment of the nation. (p > 0.05). Similarly, Das and Prasad (2010) reported Goel et al. (2021) were concerned about Indian no significant seasonal variation in APTI values for cities, phase 1 of the lockdown (the first 21 days) had plant species. However, we have an earlier report that the greatest health advantages because PM 2.5 con- APTI values varied significantly across the different centrations were at their lowest during this time. In seasons in Tamaka industrial site (Ganesan & comparison to 2019, the average pollution decrease Pragasan, 2017). Further research on the biochemical was 44.6% in Uttar Pradesh and roughly 58.5% in changes of plant species during different seasons is Delhi-NCR. required to better understand the reason for seasonal According to World Health Organization (WHO, variation in APTI values of plant species. 2016) estimations, 10 of the top 20 most populous India has severe pollution issues due to its urban cities on earth are located in India. India was listed population increase of 31.8% and total population by the World Health Organization (WHO, 2019) as growth of 17.6% between 2001 and 2011. The energy the fifth most polluted nation based on PM2.5 emis- requirements have increased due to the country’s growth sion concentrations, with 21 of the top 30 most pol- boom and 35 cities that are close to or have surpassed luted cities being in India. The Indian cities, on 1 million inhabitants. A wide range of initiatives has been average, were 500% alarmingly above the WHO implemented by the national government, several standard. Table 6. Comparison of mean annual concentrations of air pollutants of the study area with CPCB limits. Air pollutant PM10 PM2.5 SO NO Pb CO NH O 2 2 3 3 3 3 3 3 3 3 3 3 Units μg/m μg/m μg/m μg/m μg/m mg/m μg/m μg/m First year 61.22 30.47 5.61 8.72 0.11 0.62 14.00 7.86 Second year 67.14 25.86 7.42 16.69 0.26 0.68 11.25 5.94 CPCB limit 60 40 50 40 0.5 2 100 100 8 L. A. PRAGASAN AND N. GANESAN localities, and other organizations to reduce pollution Funding and increase green space (Imam & Banerjee, 2016). The authors have no funding to report. Everyone is at risk for health problems caused by air pollution, while the sick, elderly, young children, and the destitute are more at risk than others. Several vari- ORCID ables increase the need for pollution control and L. Arul Pragasan http://orcid.org/0000-0002-8543-4267 emphasize the negative effects of inactivity, including urbanization, growing industrialization, global warm- ing, and increasing knowledge of the harm caused by air Author contributions pollution. Fortunately, lowering air pollution may have Dr. L. Arul Pragasan, Assistant Professor, the corresponding an immediate and significant positive impact on your author contributed by conceptualization, supervision, health. It is more difficult to quantify but may result in drafted and final approval of the paper. Mr. N. Ganesan is instant rewards when one is protected personally and a research scholar who contributed by carrying out the from short-term dangers. The education of policy- work, data collection, and laboratory analysis. makers, the medical profession, and the general public is a crucial component in air pollution mitigation References (Schraufnagel et al., 2019). Akbari, H., Pomerantz, M., & Taha, H. (2001). 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Assessment of air pollutants and pollution tolerant tree species for the development of Greenbelt at Narasapura Industrial Estate, India

Pragasan, L. Arul; Ganesan, N.
Geology Ecology and Landscapes , Volume OnlineFirst: 9 – Nov 21, 2022
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GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2022.2144857 RESEARCH ARTICLE Assessment of air pollutants and pollution tolerant tree species for the development of Greenbelt at Narasapura Industrial Estate, India L. Arul Pragasan and N. Ganesan Environmental Ecology Lab, Department of Environmental Sciences, Bharathiar University, Coimbatore 641 046, India ABSTRACT ARTICLE HISTORY Received 28 January 2022 In this study, we assessed the concentration of air pollutants to understand the pollution status Revised 17 October 2022 of the Narasapura industrial area located in India. Also, we identified pollution-tolerant tree Accepted 3 November 2022 species for the development of greenbelts for NIA. Monthly air samples were collected from three sites from NIA and the samples were analysed for the determination of air pollutant KEYWORDS concentration following standard methods. Air pollutants such as PM10, PM2.5, SO , NO , Pb, 2 2 Air pollutants; PM10; PM2.5; CO, NH , and O were detected and their concentrations for the three sites ranged from 21 to APTI; spathodea 3 3 3 3 3 3 3 3 99 μg/m , 11 to 67 μg/m , 3 to 14 μg/m , 5 to 28 μg/m , 0.01 to 0.9 μg/m , 0.3 to 0.9 mg/m , 3 campanulata; greenbelts 3 3 to 17 μg/m , 6 to 25 μg/m , respectively. Twenty common tree species to NIA were selected and their air pollution tolerance potential was determined by the Air pollution tolerance index using leaf relative water content, total chlorophyll content, leaf extract pH, and ascorbic acid content. Tree species, Spathodea campanulata (9.58 ± 0.33) recorded maximum APTI value followed by Terminalia catappa, Tabebuia avellanedae, Anthocephalus cadamba, and Syzygium jambos. We conclude that the development of greenbelts is necessary for the mitigation of air pollutants. Introduction in sensitive species and the lowest intolerant ones Air pollutants are a major threat to all living beings and (Govindaraju et al., 2012). Air pollutants have an ecosystem sustainability (Hatamimanesh et al., 2021). impact on the plant system, beginning with biochem- ical alterations and progressing through structural and The earth’s atmosphere has been polluted highly before the industrial revolution. The sources of pollutants are functional changes in the leaf, all the way to the land- mostly anthropogenic activities, and scientists relate scape level (Rai, 2016). that the increase in air pollutants is attributed to uncon- An essential factor that defines a plant’s capacity to trolled industrialization, inadequate emission manage- withstand air pollution is the Air Pollution Tolerance ment, and a lack of strong environmental policies (Roy Index (APTI), and plants with a higher index value et al., 2020). Besides astonishing growth in the medical can act as natural sinks for CO sequestration. (Sahu & sector, pollutants are pulling down human health con- Sahu, 2015). Some fundamental biological factors, ditions through threats such as cardiovascular disease, such as ascorbic acid, total chlorophyll, relative water respiratory problems, lung cancer, infertility, and pre- content, and leaf extract pH, affect how sensitive and tolerant plants are to air pollution. Singh and Rao mature mortality (Hatamimanesh et al., 2021). Air qual- ity for the past two decades has deteriorated (1983) created the APTI, which is derived using dramatically, as a result of industrial emissions, trans- these four biochemical characteristics. By adding APTI values to other biological and socioeconomic portation, urbanization, and a significant decline in vegetation. Industry-related air pollution is a key source characteristics, it is possible to generate the of concern for air quality degradation, especially, in Anticipated Performance Index (API) for the plants. developing countries, where severe air pollution is (Sharma et al., 2020). The primary greenbelt compo- a big concern (Gros Valerie et al., 2007). nent, plants, serve as a sink and living filters to reduce Air pollution has the potential to have negative air pollution through absorption, adsorption, detoxi- health consequences on not only humans but also on fication, accumulation, and/or metabolization without suffering serious foliar damage or growth decline, trees and other animals in the environment (Rai & Panda, 2014). Air pollution in certain industrial areas improving air quality by supplying oxygen to the is adsorbed, collected, and integrated into the plant atmosphere (Kapoor & Chittora, 2016). Greenbelts are a natural technique to minimize system. Toxic air pollution can injure plants in a variety of ways, with the level of impact being highest pollution in the atmosphere by trapping particulates CONTACT L. Arul Pragasan arulpragasan@buc.edu Environmental Ecology Lab, Department of Environmental Sciences, Bharathiar University, Coimbatore 641 046, Tamil Nadu, India © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 L. A. PRAGASAN AND N. GANESAN and absorbing gaseous pollutants (Han et al., 2021). Commercial Vehicles Ltd. The district’s landscape is This concept was first created by Englishman Sir varied, ranging from undulating to flat terrain, with Ebenezer Howard (Howard & Osborn, 1965). red loamy, red sandy and lateritic soils. The mean However, only a few nations have implemented green- monthly maximum and minimum temperatures for belts that include the United Kingdom, Canada, Kolar over eight years (2009–2016) are 31°C and 20°C, Australia, the United States, and South Korea (Han respectively. For the same period, the average annual et al., 2021). Greenbelts not only help to reduce air rainfall and rainy days are 1212 mm and 201 days pollution, but also reduce noise pollution, prevent soil (Ganesan & Pragasan, 2017). erosion, increase water harvesting, and improve the region’s scenic value (Yang & Jinxing, 2007). Growing Assessment of air quality resistant plants in and around polluted areas is a new strategy that has been adopted in recent years (Roy Air samples were taken monthly at three sites in NIA et al., 2020), as emphasized by scientists and policy- to measure the air quality, from April 2014 to makers. The widespread planting of pollutant-tolerant March 2016. Standard methods, as per National trees in industrial sectors can make a significant con- Ambient Air Quality Standards and Central tribution to improving air quality. Pollution Control Board standards were followed to So far no study has been reported on either air determine the concentration of air pollutants such as pollutant concentration or greenbelts of Narasapura PM10, PM2.5, SO , NO , Pb, CO, NH , C H , Benzo 2 2 3 6 6 Industrial Area (NIA) located in India. Thus, the pre- (a) Pyrene (BaP), As, Ni, and O sent study was carried out to assess the concentration of air pollutants, and also to evaluate the pollution Determination of air pollution tolerance levels of tolerance levels of common tree species for the devel- tree species opment of greenbelts as an air pollution management strategy for mitigation of air pollutants. Twenty tropical tree species common to the study area were chosen, and their air pollution tolerance levels were investigated to determine their potential for Materials and methods greenbelt development. The Air Pollution Tolerance Index (APTI), which is regarded as an excellent Study area method was used to estimate the air pollution toler- NIA (13.13° N and 78.13° E) is located in the Kolar ance levels for each tree species (Roy et al., 2020). district of Karnataka state, India (Figure 1). It is During 2015, matured leaf samples in triplicates were located 15 km from Kolar and is one of the city’s collected (in polythene bags and labeled) for each main industrial areas. The major enterprises in the species in the early morning hours of 6 a.m. to 9.00 study area include Honda Motor and Scooter India a.m. for the three major seasons of winter, summer, Pvt Ltd., ASK Auto, Mahindra Aerospace, and Scania and rainy. Leaf samples were transferred immediately Figure 1. Map showing the location of the sampling sites at Narasapusa industrial area in Kolar district of Karnataka state in India. GEOLOGY, ECOLOGY, AND LANDSCAPES 3 3 3 to the lab for further biochemical investigation for the 0.70 ± 0.12 mg/m , 13.17 ± 4.87 μg/m and 7.2 ± 2.89 μg/ determination of APTI for each species following m (Figure 2). Similarly, at site 2, the mean concentration Singh and Rao (1983); for PM10, PM2.5, SO , NO , Pb, CO, NH , and O were 2 2 3 3 3 3 APTI = (A (T + P) +R)/10 (Eq.1) 64.13 ± 21.33 μg/m , 25.88 ± 10.97 μg/m , 6.04 ± 1.92 μg/ 3 3 3 where, A stands for ascorbic acid, T stands for total m , 11.50 ± 5.91 μg/m , 0.17 ± 0.11 μg/m , 3 3 chlorophyll, P stands for pH and R stands for relative 0.64 ± 0.10 mg/m , 12.50 ± 4.63 μg/m and water content. Based on the APTI value, the 20 selected 6.71 ± 2.73 μg/m (Figure 3). And at site 3, the mean tree species were categorized into three levels of air concentration for PM10, PM2.5, SO , NO , Pb, CO, 2 2 pollution tolerance: tolerant (if the APTI value is >7), NH , and O were 63.67 ± 21.84 μg/m , 3 3 3 3 moderately tolerant (if the APTI value is between 6 and 31.58 ± 13.79 μg/m , 6.58 ± 2.10 μg/m , 3 3 4), and sensitive (if the APTI value is <3). Growth 12.58 ± 5.59 μg/m , 0.18 ± 0.12 μg/m , 0.61 ± 0.10 mg/ 3 3 3 parameters such as shoot length (SL), leaf length (LL) m , 12.21 ± 3.71 μg/m and 6.71 ± 2.49 μg/m (Figure 4). and leaf width (LW) were recorded once in two weeks. One-way ANOVA revealed that all the air pollutants did The plants were then collected and utilized for further not vary significantly between the three study sites, investigation. SL, LL, and LW were measured using except CO (F = 4.573, p < 0.05; Table 1). (2,69) a measuring scale, and C content was calculated using the loss on ignition method. Relationship between air pollutants The results of the correlation analysis between the air Data analysis pollutants at site 1 are provided in Table 2. It reveals that PM10 had a positive relation with PM2.5, Pb and CO. One-way ANOVA was used to check the significance PM2.5 had positive relation only with PM10. SO had level for monthly fluctuation in the concentration of a positive relation with NO , Pb and CO. NO had 2 2 air pollutants, and to compare the fluctuation in APTI a positive relation with SO and Pb. Pb had a positive values for the 20 selected species across different sea- relation with PM10, SO , NO and CO, and it had 2 2 sons. Correlation analysis was used to understand the a negative relation with NH and O . CO had a positive 3 3 relationship between the air pollutants. relation with PM10, SO and Pb. NH had a positive 2 3 relation with O and negative relation with Pb. O had 3 3 Results a positive relation with NH and negative relation with Pb. Concentration of air pollutants Table 3 provides the results of the correlation ana- In all three sites of the study area, only eight air pollutants lysis between the air pollutants at site 2. It reveals that such as PM10, PM2.5, SO , NO , Pb, CO, NH and O PM10 had a positive relation with PM2.5, SO and 2 2 3, 3 2 were detected. While, the concentrations for C H , B(a) CO. PM2.5 had positive relation only with PM10. SO 6 6 2 P, As, and Ni, were not detected in the air samples during had a positive relation with PM10, NO , Pb, and CO. the study period, which may be attributed to the absence NO had a positive relation with SO , Pb and CO. Pb 2 2 of emission sources. At site 1, the mean (± S.D.) con- had a positive relation with SO , NO , and CO, and it 2 2 centration for PM10, PM2.5, SO , NO , Pb, CO, NH had a negative relation with NH and O . CO had 2 2 3 3 3 3 3 and O were 64.75 ± 18.08 μg/m , 27.04 ± 11.12 μg/m , a positive relation with PM10, SO , NO and Pb. 3 2 2 3 3 3 6.92 ± 2.57 μg/m , 14.04 ± 6.77 μg/m , 0.21 ± 0.13 μg/m , NH had a positive relation with O and negative 3 3 120.0 100.0 80.0 Q1 60.0 Min 40.0 Median Max 20.0 Q3 0.0 PM10 PM2.5 Pb CO SO NO NH O 2 3 3 Site 1 3 3 Figure 2. Box plot showing the concentration of air pollutants at site 1. The unit for all the pollutants is μg/m except CO (mg/m ). 3 3 Concentration, µg/m or mg/m 4 L. A. PRAGASAN AND N. GANESAN 100.0 90.0 80.0 70.0 60.0 Q1 50.0 Min 40.0 Median 30.0 20.0 Max 10.0 Q3 0.0 PM10 PM2.5 Pb CO SO NO NH 2 2 3 Site 2 3 3 Figure 3. Box plot showing the concentration of air pollutants at site 2. The unit for all the pollutants is μg/m except CO (mg/m ). 120.0 100.0 80.0 Q1 60.0 Min 40.0 Median Max 20.0 Q3 0.0 PM10 PM2.5 Pb CO SO NO O NH 2 2 3 Site 3 3 3 Figure 4. Box plot showing the concentration of air pollutants at site 3. The unit for all the pollutants is μg/m except CO (mg/m ). Table 1. Analysis of variance for the air pollutants concentrations between the three sites. Air pollutant Source of Variation SS df MS F-value P-value F crit PM10 Between Groups 14.194 2 7.097 0.017 0.983 3.130 Within Groups 28,962.460 69 419.746 Total 28,976.650 71 PM2.5 Between Groups 436.583 2 218.292 1.509 0.228 3.130 Within Groups 9983.417 69 144.687 Total 10,420.000 71 SO Between Groups 9.361 2 4.681 0.954 0.390 3.130 Within Groups 338.625 69 4.908 Total 347.986 71 NO Between Groups 78.083 2 39.042 1.045 0.357 3.130 Within Groups 2576.792 69 37.345 Total 2654.875 71 Pb Between Groups 0.021 2 0.011 0.740 0.481 3.130 Within Groups 1.000 69 0.014 Total 1.021 71 CO Between Groups 0.105 2 0.053 4.573 0.014 3.130 Within Groups 0.794 69 0.012 Total 0.899 71 NH Between Groups 11.583 2 5.792 0.295 0.746 3.130 Within Groups 1355.292 69 19.642 Total 1366.875 71 O Between Groups 5.444 2 2.722 0.371 0.692 3.130 Within Groups 506.875 69 7.346 Total 512.319 71 SS-sum of squares, df-degree of freedom, MS-mean square,F crit-F critical value. 3 3 3 3 Concentration, µg/m or mg/m Concentration, µg/m or mg/m GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Table 2. Correlation table for the air pollutants at Site 1. Air pollutant PM10 PM2.5 SO NO Pb CO NH O 2 2 3 3 PM10 Pearson coefficient 1 0.495* 0.205 0.176 0.506* 0.721** 0.001 0.171 P (2-tailed) 0.014 0.336 0.411 0.012 0.000 0.996 0.425 N 24 24 24 24 24 24 24 24 PM2.5 Pearson coefficient 0.495* 1 0.137 −0.154 −0.127 0.356 0.144 0.299 P (2-tailed) 0.014 0.523 0.473 0.555 0.088 0.501 0.155 N 24 24 24 24 24 24 24 24 SO Pearson coefficient 0.205 0.137 1 0.803** 0.536** 0.552** −0.180 −0.131 P (2-tailed) 0.336 0.523 0.000 0.007 0.005 0.401 0.542 N 24 24 24 24 24 24 24 24 NO Pearson coefficient 0.176 −0.154 0.803** 1 0.658** 0.343 −0.285 −0.289 P (2-tailed) 0.411 0.473 0.000 0.000 0.100 0.177 0.171 N 24 24 24 24 24 24 24 24 Pb Pearson coefficient 0.506* −0.127 0.536** 0.658** 1 0.621** −0.625** −0.564** P (2-tailed) 0.012 0.555 .007 0.000 0.001 0.001 0.004 N 24 24 24 24 24 24 24 24 CO Pearson coefficient 0.721** 0.356 0.552** 0.343 0.621** 1 −0.009 0.034 P (2-tailed) 0.000 0.088 0.005 0.100 0.001 0.968 0.875 N 24 24 24 24 24 24 24 24 NH Pearson 0.001 0.144 −0.180 −0.285 −0.625** −0.009 1 0.909** coefficient P (2-tailed) 0.996 0.501 0.401 0.177 0.001 0.968 0.000 N 24 24 24 24 24 24 24 24 O Pearson coefficient 0.171 0.299 −0.131 −0.289 −0.564** 0.034 0.909** 1 P (2-tailed) 0.425 0.155 0.542 0.171 0.004 0.875 0.000 N 24 24 24 24 24 24 24 24 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Table 3. Correlation table for the air pollutants at Site 2. Air pollutant PM10 PM2.5 SO NO Pb CO NH O 2 2 3 3 PM10 Pearson coefficient 1 0.423* 0.469* 0.360 0.176 0.540** 0.392 0.395 P (2-tailed) 0.039 0.021 0.084 0.412 0.006 0.058 0.056 N 24 24 24 24 24 24 24 24 PM2.5 Pearson coefficient 0.423* 1 0.209 0.244 0.051 0.309 0.188 0.241 P (2-tailed) 0.039 0.328 0.251 0.814 0.142 0.380 0.256 N 24 24 24 24 24 24 24 24 SO Pearson coefficient 0.469* 0.209 1 0.828** 0.709** 0.680** −0.066 0.011 P (2-tailed) 0.021 0.328 0.000 0.000 0.000 0.760 0.960 N 24 24 24 24 24 24 24 24 NO Pearson coefficient 0.360 0.244 0.828** 1 0.764** 0.600** −0.167 −0.109 P (2-tailed) 0.084 0.251 0.000 0.000 0.002 0.437 0.611 N 24 24 24 24 24 24 24 24 Pb Pearson coefficient 0.176 0.051 0.709** 0.764** 1 0.601** −0.528** −0.468* P (2-tailed) 0.412 0.814 0.000 0.000 0.002 0.008 0.021 N 24 24 24 24 24 24 24 24 CO Pearson coefficient 0.540** 0.309 0.680** 0.600** 0.601** 1 0.111 0.218 P (2-tailed) 0.006 0.142 0.000 0.002 0.002 0.607 0.306 N 24 24 24 24 24 24 24 24 NH Pearson 0.392 0.188 −0.066 −0.167 −0.528** 0.111 1 0.972** coefficient P (2-tailed) 0.058 0.380 0.760 0.437 0.008 0.607 0.000 N 24 24 24 24 24 24 24 24 O Pearson coefficient 0.395 0.241 0.011 −0.109 −0.468* 0.218 0.972** 1 P (2-tailed) 0.056 0.256 0.960 0.611 0.021 0.306 0.000 N 24 24 24 24 24 24 24 24 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). relation with Pb. O had a positive relation with NH O had a positive relation with NH3 and negative rela- 3 3 3 and negative relation with Pb. tion with Pb. The results of the correlation analysis between the air pollutants at site 3 are provided in Table 4. It reveals that APTI value of tree species PM10 had positive relation only with CO. PM2.5 had a positive relation with PM10 and PM2.5. SO had The air pollution tolerance levels of the 20 tree species a positive relation with PM2.5, NO , Pb, and CO. NO investigated in this study are provided along with their 2 2 had a positive relation with SO , Pb and CO. Pb had APTI values for the three seasons in Table 5. Tree a positive relation with SO , NO , and CO, and negative species, Spathodea campanulata (9.58 ± 0.33) scored 2 2 relation with NH and O . CO had a positive relation the highest mean APTI value followed by Terminalia 3 3 with PM10, PM2.5, SO , NO , and Pb. NH had catappa, Tabebuia avellanedae, Anthocephalus 2 2 3 a positive relation with O and negative relation with Pb. cadamba, Syzygium jambos, and Ficus benghalensis. 3 6 L. A. PRAGASAN AND N. GANESAN Table 4. Correlation table for the air pollutants at Site 3. Air pollutant PM10 PM2.5 SO NO Pb CO NH O 2 2 3 3 PM10 Pearson coefficient 1 0.302 0.309 0.268 0.396 0.650** 0.168 0.185 P (2-tailed) 0.151 0.142 0.206 0.056 0.001 0.433 0.387 N 24 24 24 24 24 24 24 24 PM2.5 Pearson coefficient 0.302 1 0.466* 0.274 0.258 0.440* −0.042 0.049 P (2-tailed) 0.151 0.022 0.195 0.223 0.032 0.844 0.819 N 24 24 24 24 24 24 24 24 SO Pearson coefficient 0.309 0.466* 1 0.716** 0.489* 0.755** 0.067 0.150 P (2-tailed) 0.142 0.022 0.000 0.015 0.000 0.754 0.485 N 24 24 24 24 24 24 24 24 NO Pearson coefficient 0.268 0.274 0.716** 1 0.628** 0.503* −0.249 −0.218 P (2-tailed) 0.206 0.195 0.000 0.001 0.012 0.240 0.306 N 24 24 24 24 24 24 24 24 Pb Pearson coefficient 0.396 0.258 0.489* 0.628** 1 0.483* −0.591** −0.526** P (2-tailed) 0.056 0.223 0.015 0.001 0.017 0.002 0.008 N 24 24 24 24 24 24 24 24 CO Pearson coefficient 0.650** 0.440* 0.755** 0.503* 0.483* 1 0.229 0.279 P (2-tailed) 0.001 0.032 0.000 0.012 0.017 0.281 0.187 N 24 24 24 24 24 24 24 24 NH Pearson 0.168 −0.042 0.067 −0.249 −0.591** 0.229 1 0.948** coefficient P (2-tailed) 0.433 0.844 0.754 0.240 0.002 0.281 0.000 N 24 24 24 24 24 24 24 24 O Pearson coefficient 0.185 0.049 0.150 −0.218 −0.526** 0.279 0.948** 1 P (2-tailed) 0.387 0.819 0.485 0.306 0.008 0.187 0.000 N 24 24 24 24 24 24 24 24 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). All the species fell under the tolerant category except development and economic impacts, aesthetic appeal Azadirachta indica, Pongamia pinnata, and Samanea and biodiversity, psychological relaxation and stress saman which fell under the intermediate category. relief, offices with greenery that increase productivity, One-way ANOVA revealed that the APTI value did stimulate social cohesion and interaction, support not vary significantly among the tree species across physical fitness and activities, and provide healthier, three seasons (F = 1.603, p > 0.05). more tranquil outdoor living spaces (Jabbar et al., (2,57) 2021). Data on air quality must be evaluated in a variety of Discussion ways to record and research the effects. Huang et al. Green spaces reduce the creation of photochemical (2015) stated emphatically about the monitoring data ozone, which helps to reduce air pollution by cooling in Xi’an, China, that if simply the mean values from down cities and minimizing the urban heat island raw data were examined, several facts and impacts effect. Urban trees’ shade also lowers energy use, would be overlooked, such as some significant which indirectly improves air quality (Akbari et al., monthly or seasonal abrupt changes. In the present 2001). The benefits of urban green spaces include study, air pollutants were analysed on monthly basis to better environmental conditions, sustainable assess the significance of variation in the Table 5. Air pollution tolerance levels of the twenty selected tree species in NIA based on APTI value. APTI value Name of the tree species Winter Summer Rainy Mean (± S.D.) TL Anthocephalus cadamba Miq. 9.63 8.36 9.85 9.28 0.80 T Azadirachta indica A.Juss. 6.67 5.01 8.02 6.57 1.51 IM Bauhinia variegata L. 8.53 9.09 9.34 8.99 0.41 T Dalbergia melanoxylon Guill. & Perr. 8.93 7.19 8.99 8.37 1.02 T Ficus benghalensis L. 9.09 9.02 9.27 9.13 0.13 T Grevillea robusta A.Cunn. 8.67 9.17 9.41 9.08 0.38 T Markhamia platycalyx Sprague 8.21 7.7 7.94 7.95 0.26 T Millingtonia hortensis L.f. 8.28 8.27 8.45 8.33 0.10 T Morus nigra L. 8.32 7.84 8.03 8.06 0.24 T Muntingia calabura L. 6.7 7.14 7.38 7.07 0.34 T Peltophorum pterocarpum Backer ex K.Heyne 8.88 8.27 8.65 8.60 0.31 T Polyalthia longifolia (Sonn.) Hook.f. & Thomson 8.66 8.01 9.91 8.86 0.97 T Pongamia pinnata (L.) Merr. 5.52 5.26 6.36 5.71 0.57 IM Samanea saman (Jacq.) Merr. 6.51 5.14 6.35 6.00 0.75 IM Spathodea campanulata Buch.-Ham. ex DC. 9.95 9.3 9.5 9.58 0.33 T Swietenia macrophylla King 8.84 8.98 9.23 9.02 0.20 T Syzygium jambos (L.) Alston 8.86 9.22 9.52 9.20 0.33 T Tabebuia avellanedae Lorentz ex Griseb. 9.05 9.46 9.55 9.35 0.27 T Terminalia catappa L. 8.69 9.63 9.87 9.40 0.62 T Thespesia populnea Sol. ex Correa 8.56 8.04 8.29 8.30 0.26 T TL- air pollution tolerance levels, T- tolerant, IM-intermediate. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 concentration of each air pollutant. The concentra- APTI tions of air pollutants PM10, PM2.5, SO , NO , Pb, 2 2 The selection of air pollution tolerant tree species for CO, NH , and O detected in study area ranged from 3 3 greenbelts development is mostly carried out using the 3 3 3 3 3 26 μg/m to 98 μg/m , 11 μg/m to 60 μg/m , 3 μg/m APTI method (Ghafari et al., 2021). Similarly, in the 3 3 3 3 to 13 μg/m , 5 μg/m to 50 μg/m , 0.01 μg/m to present study APTI method was followed to understand 3 3 3 3 0.4 μg/m , 0.5 mg/m to 0.9 mg/m , 7 μg/m to the air pollution tolerance levels of 20 selected tree 3 3 3 25 μg/m , 4 μg/m to 14 μg/m , respectively. When species. All the species fell under the tolerant category compared, the mean annual concentrations of all the except Azadirachta indica, Pongamia pinnata and air pollutants detected in this study are within the Samanea saman which fell under the intermediate cate- CPCB limits except PM10 (Table 6). The mean annual gory. Spathodea campanulata scored the highest APTI concentrations for the second year (2015–2016) are value followed by Terminalia catappa, Tabebuia avella- greater than the first year (2014–2015) for PM10, SO , nedae, Anthocephalus cadamba, Syzygium jambos, and NO , Pb and CO, revealing an increasing trend. While, Ficus benghalensis. While, in our earlier report from for PM2.5, NH and O the values of the second year 3 3 Tamaka industrial site, Terminalia catappa scored the are lesser than the first year, revealing the decreasing highest APTI value followed by Polyalthia longifolia, trend for these pollutants. This variation may be Anthocephalus cadamba, Grevillea robusta and attributed to the annual variation in the rate of pro- Peltophorum pterocarpum (Ganesan & Pragasan, duction and release of air pollutants from the 2017). Whereas, Pandey et al. (2015) reported that enterprises. Ficus benghalensis scored the highest APTI value fol- Rapid industrial expansion in the last two dec- lowed by Cassia fistula, Ficus religiosa, Polyalthia long- ades has undoubtedly enhanced the level of living ifolia, Drypetes roxburghii, and Zizyphus jujuba out of of citizens in India, as evidenced by the growing 29 tree species studied at Varanasi city. This is clear that number of vehicles on the highways. However, our the APTI value of any species varies with the study environmental health condition deteriorates as we region with varied environmental conditions particu- inhale the poisoned air due to this development. larly air pollution levels and climatic regimes. Similarly, Approximately 7 million people die every year due Noor et al. (2015) stated that the air pollution tolerance to particulate air pollution worldwide, and 12.5% of levels of plant species vary with site and depend on the all deaths in India are due to air pollution type and level of pollution. (Lokhandwala & Gautam, 2020). Mitigation mea- In the present study, the APTI value of tree species sures are a high need for the sustainable develop- did not vary significantly across three seasons ment of the nation. (p > 0.05). Similarly, Das and Prasad (2010) reported Goel et al. (2021) were concerned about Indian no significant seasonal variation in APTI values for cities, phase 1 of the lockdown (the first 21 days) had plant species. However, we have an earlier report that the greatest health advantages because PM 2.5 con- APTI values varied significantly across the different centrations were at their lowest during this time. In seasons in Tamaka industrial site (Ganesan & comparison to 2019, the average pollution decrease Pragasan, 2017). Further research on the biochemical was 44.6% in Uttar Pradesh and roughly 58.5% in changes of plant species during different seasons is Delhi-NCR. required to better understand the reason for seasonal According to World Health Organization (WHO, variation in APTI values of plant species. 2016) estimations, 10 of the top 20 most populous India has severe pollution issues due to its urban cities on earth are located in India. India was listed population increase of 31.8% and total population by the World Health Organization (WHO, 2019) as growth of 17.6% between 2001 and 2011. The energy the fifth most polluted nation based on PM2.5 emis- requirements have increased due to the country’s growth sion concentrations, with 21 of the top 30 most pol- boom and 35 cities that are close to or have surpassed luted cities being in India. The Indian cities, on 1 million inhabitants. A wide range of initiatives has been average, were 500% alarmingly above the WHO implemented by the national government, several standard. Table 6. Comparison of mean annual concentrations of air pollutants of the study area with CPCB limits. Air pollutant PM10 PM2.5 SO NO Pb CO NH O 2 2 3 3 3 3 3 3 3 3 3 3 Units μg/m μg/m μg/m μg/m μg/m mg/m μg/m μg/m First year 61.22 30.47 5.61 8.72 0.11 0.62 14.00 7.86 Second year 67.14 25.86 7.42 16.69 0.26 0.68 11.25 5.94 CPCB limit 60 40 50 40 0.5 2 100 100 8 L. A. PRAGASAN AND N. GANESAN localities, and other organizations to reduce pollution Funding and increase green space (Imam & Banerjee, 2016). The authors have no funding to report. Everyone is at risk for health problems caused by air pollution, while the sick, elderly, young children, and the destitute are more at risk than others. Several vari- ORCID ables increase the need for pollution control and L. Arul Pragasan http://orcid.org/0000-0002-8543-4267 emphasize the negative effects of inactivity, including urbanization, growing industrialization, global warm- ing, and increasing knowledge of the harm caused by air Author contributions pollution. Fortunately, lowering air pollution may have Dr. L. Arul Pragasan, Assistant Professor, the corresponding an immediate and significant positive impact on your author contributed by conceptualization, supervision, health. It is more difficult to quantify but may result in drafted and final approval of the paper. Mr. N. Ganesan is instant rewards when one is protected personally and a research scholar who contributed by carrying out the from short-term dangers. The education of policy- work, data collection, and laboratory analysis. makers, the medical profession, and the general public is a crucial component in air pollution mitigation References (Schraufnagel et al., 2019). Akbari, H., Pomerantz, M., & Taha, H. (2001). 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Journal

Geology Ecology and Landscapes – Taylor & Francis

Published: Nov 21, 2022

Keywords: Air pollutants; PM10; PM2.5; APTI; spathodea campanulata; greenbelts

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