Introduction

Global population growth has increased significantly in the past few decades, directly causing the level of pollutants to increase rapidly in the environment. The high level of pollutants has raised major concerns primarily because of the potential negative impact on human health as well as vegetation and other living organisms. The impact that could be, or is being, caused by pollutants can be mitigated to a significant degree through efficient use of appropriate techniques in environmental management.

Several studies illustrate the effectiveness of a green belt strategy in addressing air pollution through planting tolerant trees in areas that are polluted (Prajapati and Tripathi 2008; Pathak et al. 2011). Such species of trees have been found to be effective in acting as a natural filter of hazardous or harmful air gases that pollute the air; therefore, they represent a sustainable approach for removing air pollutants through mechanisms such as leaf sorption, vegetation fallout, and deposition over the leaf surface (Ram et al. 2014; Li et al. 2019; Baraldi et al. 2019). Leaves are considered as the most effective part of trees for sensitively indicating air pollution levels due to resulting leaf morphological and physiological changes, such as leaf area, color, and numbers, as well as leaf stomatal density and pore opening (Seyyednejad and Koochak 2011; Chavan and Sonwane 2012). Gostin (2009) found that air pollution released from cement factories combined with general pollutants (e.g., motor vehicles) adversely influence the anatomy and leaf structure of Lotus corniculatus L., Trifolium montanum L., T. pratense L., and T. repens L. With endless exposure to industrial and vehicular emissions, many tree species are extensively reported as bio-indicators of atmospheric pollutants and represent various levels of responses.

The selection of tree species for planting in any polluted area must consider the response of the tree species to pollutants (Kaur and Nagpal 2017). Collectively, the air pollution tolerance index (APTI) is a method applied by Singh and Rao (1983) for the classification of trees based on their response to pollutants (starting with sensitive, followed by resistant, and ending with tolerant tree species). Sensitive tree species are essential as early indicators of pollution, but because they are severely harmed by small to medium levels of pollution, tolerant tree species are needed for determining (and reducing) heavy pollution levels (Singh and Rao 1983). The role of APTI for determining tree species responses and developing an effective green space area in urban environments is widely reported (Noor et al. 2015; Sanghi et al. 2015; Kaur and Nagpal 2017).

For determining the APTI of tree species, various biochemical and physiological parameters within tree leaves, including total chlorophyll, ascorbic acid, leaf pH, and relative water contents, can be usefully determined (Pandey et al. 2015; Joshi and Swami 2009). Under environmental stress, decreased chlorophyll content resulting from reactive oxygen species in the chloroplast occurs, while simultaneously, a high concentration of ascorbic acid is released from the photosynthetic apparatus against reactive oxygen species to protect thylakoid membranes from damage by oxidative processes (Joshi and Swami 2009; Patidar et al. 2016). Panda et al. (2018) indicated that leaves of tolerant plants under air pollution stress contain large amounts of ascorbic acid. Considering APTI as one strategic tool for selecting appropriate trees, Enete et al. (2012) selected trees with high APTI values for urban heat reduction.

pt?>Tsega and Prasad (2014) and Panda et al. (2018) support another index called the anticipated performance index (API), which is useful for developing a green belt strategy as well as for selecting particular tree species in any region. Calculating the level of API utilizes the APTI value combined with other parameters of trees such as biological and socio-economic dimensions. In that regard, various species of trees exhibit differences in their manner of responses to pollutants. Many studies conducted in the past have determined that API, as well as APTI, is appropriate indicators during the evaluation of tree species seeking to determine their responses to air pollution (Pandey et al. 2015; Joshi and Swami 2009). API and APTI together can represent an effective green belt development (Prajapati and Tripathi 2008; Ogunkunle et al. 2015). Furthermore, APTI is useful to provide information for selecting tolerant species for landscaping and urban planning (Enete et al. 2013; Kaur and Nagpal 2017; Sharma et al. 2019).

The Kingdom of Saudi Arabia (KSA) has declared a sustainable plan for the next decade called “2030 Vision” to accomplish many internal and international goals including cultural, economic, and geostrategic aspects (Saudi Arabia Vision 2016). The expected increase of industrial projects has the potential to produce enormous environmental pollutants within the KSA. Therefore, one goal of this vision is “achieving environmental sustainability” to preserve and develop the environment of the KSA. Recently, one of the biggest urban greening projects has been established in the KSA, namely, “Green Riyadh,” to increase green space within Riyadh City by planting 7.5 million trees by 2030 (Riyadh Green 2019). A common green belt strategy includes the growth of tolerant tree species to reduce air pollution in major cities worldwide.

To ensure the success of the “Green Riyadh” project, investigation of which particular tree species should be planted is required. To our knowledge, studies for developing recommendations of tree species that efficiently perform in greening projects are limited. Therefore, the objectives of this study were (1) to assess the API and APTI of five tree species that are commonly found along roadsides, as well as around residential and industrial areas of Riyadh, and (2) to select effective and tolerant species for use in the “Green Riyadh” project.

Materials and methodology

Area of study

Riyadh is the capital city of Saudi Arabia. It is the largest metropolitan city in the Kingdom and serves as the center for political and economic activities. Longitude and latitude dimensions (physical or geographical location) are 24° 28′ N to 24° 58′ N and 46° 30′ E to 46° 56′ E. The population of the Riyadh region was estimated to be about 8.5 million in 2018 according to the General Authority for Statistics in KSA (GAStat 2018). Khan (2002) has statistically indicated that based on population growth trends in the past half-decade, the Riyadh population will exceed 10 million by 2020. Population growth and urbanization have grown exponentially in the past three decades and that trend is associated with an increase in pollutant levels (Ali et al. 2016; Verma and Singh 2006). In this study, four sites were used as the study area, with three sites (sites one, two, and three) located in Riyadh while the other site (fourth site) was selected to be a reference site and is located approximately 20 km from Riyadh. To improve the general applicability of our findings and ensure that a wide range of influential factors is taken into consideration, the sites chosen for the study were situated distantly from each other (geographically) and vary in properties (such as industrial activities, residential activities, and traffic density). Table 1 and Figure 1 show the description of the selected sites and the particular location of each site (with reference to other locations in Riyadh), respectively.

Table 1 Details of the study sites
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Fig. 1
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Map of Riyadh City (location of the four sites)

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Riyadh is mostly hot and dry in the summer while in the winter, the city is cold with minimal precipitation. During the summer, the relative humidity in Riyadh averages approximately 12% (Alharbi et al. 2015). Dust storms with the hot and arid climate in Riyadh (central region of Saudi Arabia) can place extreme stress on the atmospheric environment (Al Jassir et al. 2005; Maghrabi 2009; Alharbi et al. 2015; Modaihsh et al. 2015). Pollutants from industrial and traffic activities are released into the environment within the form of particulate matter (PM) in various liquid or solid states (Duong and Lee 2011). Many studies have been conducted in Riyadh to determine the atmospheric pollutants such as lead, sulfur, and nickel present within PM (El-Shobokshy 1985; Rushdi et al. 2013; Alharbi et al. 2014; Alharbi et al. 2015). Likewise, 20 metals averaged 21.5% of PM concentrations that were emitted over the city of Riyadh (Alharbi et al. 2015). In a comparative study, the air quality of Riyadh has been recently assessed using five monitoring network stations (Alharbi et al. 2014). This study reported PM concentrations estimated in Riyadh as high as three times over the levels of the KSA ambient air quality, and PM concentrations were 84% higher in summer compared with winter. Emissions from heavy traffic roads (King Fahad road) at the city center of Riyadh led to twofold higher PM concentrations compared with the rural site (Modaihsh et al. 2016).

Sampling and analysis

Leaf samples of five tree species, namely, Albizia lebbeck (L.) Benth, Eucalyptus camaldulensis Dehn, Ficus altissima Blume, Prosopis juliflora (Sw) Dc, and Ziziphus spina-christi (L.) Desf, were collected during July and August 2017, which ensured that temperature, as well as rain conditions, was similar at the time of collection. These five species were found in all four study sites (see Table 1 and Fig. 1 for descriptions of the study sites). Samples of fully developed (mature) leaves were randomly collected in three replicates from all sides of the individual tree species using polyethylene gloves and stainless steel scissors (Deljanin et al. 2016). During collection, leaves showing surface blemishes as well as atypical morphologies were avoided. Samples were then immediately taken to the lab using a heatproof container to assess relative leaf water content, ascorbic acid content, pH and total chlorophyll, and measurements of leaf area.

Leaf area

Only mature, fully exposed fresh leaves were used for leaf area measurements. Prior to measurement, leaves were delicately cleansed with moist tissue paper in the laboratory. The approximate leaf area (cm2) of ten leaves per tree was measured in triplicates using leaf area meter following the method described by Laghari and Zaidi (2013).

Ascorbic acid content (A)

We used HPLC (high-performance liquid chromatography) for determining ascorbic acid in the leaf samples (Bozan et al. 1998). HPLC analysis involved the following equipment: a Shimadzu (Agilent1100, USA) 20A series HPLC, fitted with LC-solution software, 2 LC-20A pumps, diode array detector (SPD-M20A), and NUCLEODUR®100-5 C18ec separation column. The dimensions of the separation column were 150 by 4.6 mm, with a 25 °C column temperature. The pH of the eluent was adjusted to 3 using HCl. Isocratic elution was used for separating sample components, and the ultraviolet detector wavelength was set at 245 nm. A mobile phase consisting of acetonitrile 95:5 v/v and 20 mM NaHH2PO4 was used for detecting ascorbic acid. The flow rate was maintained at 1.9 mL/min and the injection volume was 10 μL.

In extracting ascorbic acid, 10 g of fresh leaves was extracted in 25 mL of 5% MPA (metaphosphoric acid). The extract was shaken for approximately 6 h, and a 0.45-μm Supor® membrane filter was used to filter the extracted samples. The extracts were then incubated in the dark at 4 °C. After the two phases (extraction and filtering) were completed, 4 standard solutions (0.1, 0.3, 0.5, and 0.8 mg L−1) were prepared for performing HPLC quantitative analysis. Next, the peak areas from the chromatograms were compared with an external standard for quantification (see Figs. 2 and 3). A linear correlation of response was obtained (for ascorbic acid over the standard solutions), and the correlation coefficient (R2) was found to be 0.994.

Fig. 2
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Calibration curve of ascorbic acid (A) standard solutions

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Fig. 3
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Peak area of chromatography for A (245 nm)

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pH of leaf extracts

Five grams of fresh samples of leaves was homogenized using 10 mL of deionized water. The extracted leaves were then filtered. A pH buffer solution with an acid-base ratio of 4:9 was used to calibrate the pH meter for pH determinations of the leaf samples (Prasad and Rao 1982).

Total chlorophyll content (T)

Chlorophyll content was quantified using a technique described by Arnon (1949). Fresh leaf samples (3 g) were blended, followed by extraction using a solution of 80% acetone (10 mL). The extract was then incubated for 15 min. The supernatant was decanted and centrifuged (at 2500 rpm) for 3 min. The supernatant absorbance was then measured (at wavelengths of 645 nm and 663 nm), using a spectrometer. The following formulae were then utilized in making calculations.

$$ \mathrm{Chlorophyll}\ \mathrm{a}\ \left(\mathrm{mg}\ {\mathrm{g}}^{-1}\right)=12.7\times {A}_{663}-2.69\times {A}_{645} $$
(1)
$$ \mathrm{Chlorophyll}\ \mathrm{b}\ \left(\mathrm{mg}\ {\mathrm{g}}^{-1}\right)=22.9\times {A}_{645}-4.68\times {A}_{663} $$
(2)
$$ \mathrm{Total}\ \mathrm{content}\ \mathrm{of}\ \mathrm{chlorophyll}\ \left(\mathrm{mg}\ {\mathrm{g}}^{-1}\right)=20.2\times {A}_{645}+8.02\times {A}_{663} $$
(3)

Relative water content (R)

The technique explained by Henson et al. (1981) was used to calculate the relative water content (variable R) based on methods by Singh (1977) using the formula given below:

$$ \mathrm{R}=\left[\left(\mathrm{FW}-\mathrm{DW}\right)/\left(\mathrm{TW}-\mathrm{DW}\right)\right]\times 100 $$
(4)

where FW represents the fresh weight; DW represents the dry weight; and TW represents the turgid weight.

Immediately after transport to the lab, the fresh leaves were weighed to determine the fresh weight of each sample. Then, the leaves were immersed in water overnight. The leaves were then blotted dry, and to obtain the turgid weights, leaves were again weighed. The final step was drying the leaves overnight using an oven set at 70 °C and reweighing the leaves to obtain the dry weights of each sample.

APTI

APTI was calculated by determining the concentrations of chlorophyll, ascorbic acid, pH, and the relative water content in the collected leaf samples. The equation developed by Singh and Rao (1983) was utilized to calculate APTI by the following formula:

$$ \mathrm{APTI}=\left[A\ \left(T+P\right)+R\right]/10 $$

where A represents ascorbic acid (mg g−1); T represents total chlorophyll (mg g−1); P represents pH; and R represents the relative content of water (%).

API

API was calculated based on the APTI values in addition to other factors such as the plants’ socio-economic values and morphological characters (including canopy structure, plant habit, and plant type). Grades were then assigned (ranging from − to +) for the individual characteristics based on a technique (or criteria) described by Gupta et al. (2011), Pandey et al. (2015), and Kaur and Nagpal (2017), and the results were tabulated (Table 2). The maximum grade that a tree species could manifest was up to 16 points. The value of API in a specific species was determined using a percentage score that the tree species can obtain according to its grade points. In calculating the percentage score, the following formula was used:

$$ \mathrm{Score}\ \left(\%\right)=\left(\mathrm{obtained}\ \mathrm{plant}\ \mathrm{species}\ \mathrm{grade}/\mathrm{highest}\ \mathrm{possible}\ \mathrm{grade}\ \mathrm{for}\ \mathrm{any}\ \mathrm{plant}\ \mathrm{species}\right)\times 100 $$
(5)
Table 2 Use of API and APTI parameters to grade and assess (+ or −) the species
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Using the percentage score, each tree species was assigned an API value and that value was utilized in assigning the plant species to one of several performance categories (ranging from not recommended to best) as described by Prajapati and Tripathi (2008) (Table 3).

Table 3 API score, grade, and category for plant species
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Statistical analysis

A general randomized complete design (RCD) with three replications was applied for analyzing the interaction between five tree species and four study sites. Data were statistically analyzed using the mixed procedure in SAS software version 9.4 (SAS Institute 2013). Tukey’s statistical method was used for the pairwise comparison of the interaction levels exploring the least significant difference (p ≤ 0.05). Variations between tree species at each site separately and for each tree across all study sites were statistically analyzed. To determine the correlation matrix of APTI and biochemical parameters (pH, relative water content, total chlorophyll, and ascorbic acid contents in the investigated tree leaves), regression analyses were performed using MS Excel software. In addition, variation of leaf area for the tree species across all study sites was determined by MS Excel software.

Results and discussion

Wolch et al. (2014) explained that the air quality in an urban area is improved by the presence of urban trees due to the substantial volumes of oxygen that such plants release into the immediate environment. Jim and Chen (2008) and Roy et al. (2012) added that green plants might be perceived as living filters that lessen air pollution by adsorbing, absorbing, accumulating, and detoxifying pollutants while remaining in a healthy condition.

Leaf area

To observe if atmospheric pollution affected leaf area (cm2) in the urban environments of Riyadh, the reduction change (%) of leaf areas for the selected trees in the study sites was measured (Table 4). The reduction of leaf area was found to be variable among all tree species at all study sites. Leaves from urban sites (S1, S2, and S3) were significantly smaller than those collected from the reference site (S4) (see description of the study sites in Area of study). Across the study sites, leaves collected from trees grown around the cement factory (S3) showed the highest reduction in leaf area. These results indicate that air pollutants at the industrial site significantly influence leaf growth. Among all tree species, the smallest reduction of leaf areas was reported for F. altissima (11.6%) while the highest reduction was observed for P. juliflora (34.8%). Similar findings, namely, reduction of leaf area, have been significantly recorded in contaminated areas for Eucalyptus sp., Platanus acerifolia, Guaiacum officinale, and Ficus benghalensis trees (Ninova et al. 1983; Jahan and Iqbal 1992). These findings suggest that the differences in the reduction in leaf areas between tree species at different locations may be due to the availability of leaf surface area and the capacity of leaves for capturing air pollutants (Weerakkody et al. 2017).

Table 4 Leaf area change (%) in urban sites (sites 1, 2, and 3) compared with the reference site (site 4)
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Pollutants significantly impact leaf growth in sensitive species (Tiwari et al. 2006). A study by Honour et al. (2009) reported that heavy traffic in urban environments was related to decreased leaf senescence, photosynthetic rate, stomatal conductance, and chlorophyll content. Decreased leaf area resulted from a reduction of absorbed radiation and ultimately a reduction in photosynthetic activity (Tiwari et al. 2006). In this study, the reduction of leaf area measured in the contaminated sites agrees with studies by Dineva (2004) and Tiwari et al. (2006).

Ascorbic acid content (A)

As shown in Table 5, the ascorbic acid content varied significantly for the five tree species across the four sites. High ascorbic acid contents were observed in the leaf samples from all tree species from site 3, with 84.6 mg g−1 for Z. spina-christi, 47 mg g−1 for A. lebbeck, 29.1 for E. camaldulensis, 24.8 for F. altissima, and 16 mg g−1 for P. juliflora. The highest ascorbic acid value was found at site 3 for Z. spina-christi (84.6 mg g−1) while the lowest value was recorded at site 4 (reference site) for P. juliflora (10.4 mg g−1). Leaves collected from site 3 (cement factory area) showed the highest ascorbic acid content followed by site 2 (roadside area), site 1 (residential and university campus area), and site 4 (reference site). According to suggestions raised by Achakzai et al. (2017), Kaur and Nagpal (2017), and Sharma et al. (2019), the increased ascorbic acid contents observed in leaves collected from polluted areas represented the tolerance response of tree species against the elevated air pollution levels of their growing environment.

Table 5 Mean values of biochemical parameters with APTI of investigated between trees at each study location as well as for each tree across all sites
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Likewise, a study conducted in Baroda city of Gujarat, India, showed that high ascorbic acid content was associated with responses to environmental pollution stress such as traffic stress in several plant species including Acacia arabica, Azadirachta indica, Ficus benghalensis, Peltophorum pterocarpum, and Polyalthia longifolia. (Bhattacharya et al. 2013). Adamsab et al. (2011) determined that traffic stress caused plants to increase ascorbic acid content, as manifested in the following 14 plant species that were collected from a city in India (Shivamogga city in Karnataka): Azadirachta indica; Mangifera indica; Carica papaya; Ricinus communis; Polyalthia longifolia; Calotropis gigantean; Eucalyptusmysoresins; Nerium indicum; Psidium guajava; Parthenium hysterophorus; Bougainvillea glabra; Mutangia calabra; Terminalia catappa; and Tamarindus indica. Numerous other studies report similar findings regarding the ascorbic acid contents in plant leaves (or trees), as illustrated in Cheng et al. (2007) and Kaur and Nayyar (2014), among other studies.

Jyothi and Jaya (2010), Palit et al. (2013), and Sanghi et al. (2015) demonstrated that ascorbic acid is a powerful antioxidant and upholds the stability of cell division as well as cell membranes in plants when conditions are strenuous (difficult or tough). Antioxidants achieve this objective by scavenging cytotoxic free radicals as well as reactive oxygen species that are created by the photo-oxidation of SO2 to SO3 (Aguiar-Silva et al. 2016; Aghajanzadeh et al. 2016). Additionally, ascorbic acid has the potential to thwart harmful effects resulting from air pollution; hence, it plays a major role in developing plant tolerance to air pollution. The findings of the present study align with previous studies (Laghari et al. 2015; Achakzai et al. 2017).

pH

Variations in pH values were observed among the different species (Table 5). This finding suggests that the variation in extracted leaf pH values can be attributed to the differences in responses between each tree species to the same pollutant stress from similar sources of pollution (Kaur and Nagpal 2017). PH values of all leaf samples across all sites did not exceed 6.3. Similarly, a study focused on roadside trees determined that leaf samples from Cassia angustifolia, Mangifera indica, Khaya senegalensis, Azadirachta indica, Eucalyptus spp., and Anacardium occidentale yield pH values ranging between 4.6 and 6.7 (Aji et al. 2015). This study was conducted with a particular focus on whether the leaf samples were exposed to high, moderate, or low levels of pollution.

Across all tree species, the lowest pH value (4.95) was observed for the sensitive species (P. juliflora) according to APTI grade, as shown in Tables 5 and 6. Other studies including Dhankhar et al. (2015), Mondal et al. (2011), Shannigrahi et al. (2004), Pathak et al. (2011), Kulkarni and Ingawale (2014), and Prajapati and Tripathi (2008) match the findings of this study. The role of pH is that it participates in the regulation of the sensitivity of a plant to pollution (Das and Prasad 2010). Researchers like Swami et al. (2004) share the perspective that when gaseous air pollutants in the cell sap, such as SO2, NO2, and CO2 diffuse, they convert into acidic radicals that explain the acidic pH observed in the present study. Low pH values may offer the inference that a challenge exists (or a decline will be observed) with regard to hexose sugar conversion within leaves of sensitive species (Jyothi and Jaya 2010). Verma and Singh (2006) indicated that variations in leaf pH might be due to the impact of air pollution on stomatal sensitivity. Collectively, leaves, with lower pH values are more influenced by atmospheric pollutants, while those with higher pH are more tolerant to pollutants (Govindaraju et al. 2012).

Table 6 APTI value and API grade of tree species investigated in this study as well as previously conducted research
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Total chlorophyll content (T)

The findings made at the four sites illustrate the varying levels of chlorophyll contents in leaves as follows: A. lebbeck (0.74–1.78 mg g−1); E. camaldulensis (0.67–0.28 mg g−1); F. altissima (0.41–0.89 mg g−1); P. juliflora (0.41–0.91 mg g−1); and Z. spina-christi (0.54–1.08 mg g−1). From the results above, it is evident that the total content of chlorophyll varies significantly for the different species of trees that were included in the study and that were collected from the four different sites. The chlorophyll contents of the leaf samples collected from site 4 were comparatively higher than samples from the other sites and should be considered in light of the findings shown in Table 1. The results of this study are in agreement with a recent study conducted by Achakzai et al. (2017), where they observed that the T contents of nine plant species decreased when the source of pollution increased. High T in the collected samples from our study may be associated with low air pollution volumes or a lack of accumulation of many dust particles on the surface of the leaves (Shyam et al. 2006). One of the key constituents found in plants is chlorophyll, and it is expected to enable plants to maintain a green exterior in addition to helping the plants to trap sunlight and convert it to chemical energy.

Bakiyaraj and Ayyappan (2014) and other studies such as Noor et al. (2015) established that the low T identified in some leaf samples that were collected at other sites might be due to factors such as stomatal pore blockage, high moisture content, and high pollutant loads. When the volume of air pollutants is high, chlorophyll molecules may become degraded to pheophytin as two hydrogen atoms replace Mg++ ions (Rahmawati et al. 2014). Chloroplast efficiency becomes reduced by air pollution; hence, the rate of photosynthesis and the conductance of stomata are affected. Additionally, leaves fall prematurely, and productivity is observed to decline (Tripathi and Gautam 2007; Bora and Joshi 2014; Giri et al. 2013). The chlorophyll content may differ between varying species and with levels of pollution, and the abiotic and biotic circumstances that are inherent in the particular region (Rai and Panda, 2015). Our study identifies the same trend as it demonstrates that species that were collected from highly polluted urban sites have low T.

Relative water content (R)

The data collected showed that the value of R (as a percentage) varies across different species of trees and different sites (Table 5). The results from this study demonstrated that the highest value of R of all species was found at site 4 (the reference site) followed by sites 1 and 2, while the lowest R was noticed at site 3 (cement industry site). Among all species, R across all study sites were observed, with the highest and the lowest R found for A. lebbeck at site 4 and P. juliflora at site 3, respectively.

Water is a critical element that facilitates the maintenance of plant physiological balance when conditions are strenuous (difficult or stressful) due to air pollution. When R is high, it represents an advantage, especially for plants that are resistant to drought (or dry conditions) (Geravandia et al. 2011). High water content in plants enables the plant to dilute acidity that may occur in the cytoplasm of leaf cells (Palit et al. 2013). The status of leaf water in plants is directly linked to numerous physiological conditions such as respiration, transpiration, and growth (Dhankhar et al. 2015). The authors found a direct relationship between protoplasmic permeability and the relative water content. When the relative water content is high, the plant becomes more tolerant of air pollution under tough conditions (Jyothi and Jaya 2010; Singh et al. 1991). The findings made in this study regarding R are in agreement with previous findings including those of Seyyednejad et al. (2011), Dwivadi and Tripathi (2007), and Chandawat et al. (2011).

APTI

APTI was calculated by determining the content of chlorophyll, the relative water content, the ascorbic acid content, and the pH of each sample. Table 5 shows the results from analyzing these biochemical parameters as well as the calculated APTI of each leaf sample. As the statistical analyses of these results were performed for each separate site as well as for each specific tree, significant variations between the biochemical parameters and APTI values among the five tree species and across the study sites were observed.

The correlation of biochemical parameters with APTI and leaf area

Figure 4 shows the impact that ascorbic acid content (R2 = 0.9726) has on APTI is positive, as well as significantly strong (based on linear regression). Linear regression analysis illustrates that pH (R2 = 0.0445), R values (R2 = 0.0182), and T values (R2 = 0.0155) have no significant impact on APTI. The same trend or outcome was determined by Kaur and Nagpal (2017) regarding the relationship between APTI and ascorbic acid content, the total content of chlorophyll, the relative water content, and pH.

Fig. 4
figure 4

Correlation of biochemical parameters across study sites with APTI values

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Correlation analyses were also performed between leaf area (cm2) and biochemical parameters (ascorbic acid (A), pH (P), total chlorophyll (T), and relative water content (R)) (Fig. 5). While A, T, and R did not show a significant correlation with leaf area of tree species across study locations, leaf pH was positively correlated with leaf area (r = 0.718). These results are in agreement with the findings of a previous study conducted by Boutraa (2013). In conflict, Mishra et al. (2018) documented that there was no significant correlation between leaf area and chlorophyll and leaf water content. Conversely, Cornelissen et al. (2006) suggested that the positive correlation between leaf area and leaf pH can stem from a plant phenotype under variable environmental stress conditions. Relevant studies have considered that leaf pH is an effective biochemical indicator of leaf growth in more than 90 plant species (Cornelissen et al. 2006; Cornelissen et al. 2011). An early study undertaken by Van Volkenburgh and Boyer (1985) indicated that the impact of drought environments on the pH of apoplastic inside leaves ultimately affected leaf growth. During dry weather, a significant relationship between apoplastic pH and leaf expansion rate can be exhibited (Bacon et al. 1998). This suggests that leaf pH can influence leaf physiological changes.

Fig. 5
figure 5

Correlation of biochemical parameters across study sites with leaf area

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Tolerant categories of tree species

APTI is applicable as a criterion that guides the selection of plants that are tolerant to air pollution (Singh et al. 1991). Table 5 presents detailed information about the individual tree species’ APTI. Increased APTI values were recorded for each tree collected from sites 3, 2, and 1 respectively while their lower APTI’s were found in the reference site (site 4). Tolerance of Z. spina-christi to air pollution was found to be high for each of the four sites, and similarly, A. lebbeck exhibited high air pollution tolerance for the sites except in site 4 (where tolerance was intermediate). An alternative explanation may be that in sites 2 (roadside) and 3 (industrial area), the collected leaf samples showed high tolerances for air pollution in comparison with site 4 because of the high content of ascorbic acid that the leaf samples contained.

Previous studies have made similar findings as is evident in the study by Pandey et al. (2015) whereby three species (Ficus religiosa, P. longifolia, and F. benghalensis) that were obtained from urban areas in India (specifically, Varanasi City) showed high APTI scores. In the study by Enete et al. (2013), eight species of trees (Catalpa bungei, Thuja pine, Yellow Ficus, Pinus sp., Masquerade pine, Yellow bush, and Anacardium occidentale) collected from major roads within the region (in the Enugu urban area) were found to have high APTI scores. Plants that show high APTI values are air pollution tolerant and may act as filters in reducing air pollution. On the other hand, plants that have low APTI values are considered to be sensitive and may be appropriate bio-indicators. In our study, the leaf samples that we collected from two sites (roadsides and industrial areas) showed varying degrees of sensitivity and tolerance. P. juliflora exhibited sensitivity while intermediate tolerance was observed in E. camaldulensis and F. altissima. Tripathi and Gautam (2007), Karthiyayini et al. (2005), and Chauhan (2010) are among researchers who conducted similar studies and determined that plants exhibited changes in the accumulation of particular metabolites because of the impact that particular pollutants have.

Investigations of API and APTI have been widely applied to evaluate the responses of several trees in various polluted areas (Table 6). Previously, published work has been conducted for measuring the appropriateness of many tree species for use in a green belt strategy. The APTI value and API grade of tree species investigated in this study, along with other recently conducted studies, are shown in Table 6. This study is considered as the first work investigating API and APTI of five tree species grown in the city of Riyadh.

API

The tree species were allocated varying grades (+ or −) in relation to APTI and the parameters (morphological and socio-economic), as shown in Tables 2 and 3. Table 7 shows several relevant parameters (including API grades, the total plus (+), and percentage scores).

Table 7 Use of API as well as APTI parameter to grades tree species
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The choice species found in this study was F. altissima due to its excellent performance on all parameters. Species such as Z. spina-christi, E. lebbeck, and E. camaldulensis also exhibited good performance. A species that was not recommended according to the findings of this study is P. juliflora. Based on an environmental perspective, planting F. altissima and other three recommended species (Z. spina-christi, A. lebbeck, and E. camaldulensis) in areas in which air pollution is high (such as industrial areas, urban areas, and heavy roadsides) would result in reduced air pollution loads. A study by Gupta et al. (2011) shared a similar perspective, and the species that they recommended for planting in industrial areas which are characterized by pollution linked with various heavy metals is A. scholaris. Their justification is similar to the one made in this study—high tolerance to air pollution. The recommendation of A. scholaris as the best candidate or tree species to be planted in such areas is slightly contested by Pandey et al. (2015), as well as Mondal et al. (2011), who argue that A. scholaris has a good to moderate performance and does not represent the best candidate. The study conducted by Dhankhar et al. (2015) completely contest the recommendations by explaining that their findings (based on leaf samples from the university campus of Rohtak, Haryana) show that A. scholaris has poor performance and N. oleander has a moderate performance. Presently (and with reference to current information or research available), there has been no other study conducted in Riyadh that has addressed this subject—except for the one we have conducted. Consequently, it would be appropriate to conduct repeated studies to obtain more data on the plant species, their varying degree of tolerance and sensitivity, and their application in mitigating air pollution and its effects.

Conclusions

As demonstrated in this study, different tree species react in various ways to air pollution. According to the results of our study, contaminated urban locations resulted in a reduction of leaf area, total chlorophyll content, and leaf relative water content. As implied in the findings, the sorting of candidate tree species should be conducted based on their tolerance relative to pollution load and using APTI as well as API indicators. Thus, locations that experience higher pollution can grow plants that exhibit higher APTI and API values. Therefore, the current study is relevant in determining the suitability of plants for different locations based on their APTI and API values. This finding implies determining what plants to grow, where to grow them, and why. For instance, plants that exhibit higher APTI and higher API values can be applied as part of bio-resistance techniques for pollution that comes from automobiles and can be planted in urban, commercial, and industrial areas where there is more air pollution as a result of vehicles and other polluting machinery. From the values of the plants examined in the study, the three plant species recommended for Riyadh are F. altissima, Z. spina-christi, A. lebbeck, and E. camaldulensis. Significantly, these trees can be incorporated into the green belt design of Riyadh to enhance long-term atmospheric pollution mitigation practices. For identifying effective atmospheric-pollutant-tolerant trees and reducing pollution levels all over the city in an appropriate manner, further investigations on the numerous plant species growing around various pollution sources such as industries and vehicular emissions are required.