The impact of Heavy dust pollution reduces biodiversity by altering the metabolism and biochemical characteristics of Fagonia indica

The impact of Heavy dust pollution reduces biodiversity by altering the metabolism and biochemical characteristics of Fagonia indica | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The impact of Heavy dust pollution reduces biodiversity by altering the metabolism and biochemical characteristics of Fagonia indica Muhammad Asim Sultan, Iftikhar Ahmad, Toqeer Abbas, Anis Ali Shah, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4369086/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Present study was conducted to explore the population dynamics in vegetation of Kirana Hills, Sargodha growing under extreme dust pollution of stone crushing industry. Through extensive survey study sites were selected and floristic composition of the area was also completed. Heavy metal analysis of the dust revealed that all heavy metals were higher at extreme dust sites particularly in winter. The soil at each site and at each season varies based on the soil analysis. Vegetation data was collected by using quadrate method. Density, frequency, coverage and importance value of vegetation was significantly decreased at extreme dust sites specifically in winter. Fagonia indica was collected throughout the study sites and evaluated for morpho-anatomical, biochemical, and physiological characteristics. Metabolic and morpho-anatomical features of all plants were severely affected at extreme dust sites, however high metabolic rate, high sclerification in leaf, root and stem along with presence of large aerenchyma cells in roots were also noticed at extreme dust sites, and these modifications help to survive in such harsh dust polluted environment. In biochemical parameters reactive oxygen species (H 2 O 2 ) production was increased at extreme dust sites, furthermore activity of enzymatic antioxidants, non-enzymatic antioxidants and osmoprotectant were increased at extreme dust sites in F. indica . Metabolic rate and concentration of heavy metals in selected ecotype also increased at extreme dust sites. Biological sciences/Physiology Biological sciences/Plant sciences Biological sciences/Structural biology Earth and environmental sciences/Environmental sciences Biodiversity chlorophyll Dust Pollution heavy metals H2O2 metabolism sclerification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Key findings Using physiological and metabolic results in the deposition of dust in the soil, plants its transfer to metabolic pathways, the activity of enzymatic antioxidants increased. On-going monitoring of dust pollution and treatment is essential to mitigate the health hazards linked to metal toxicity in soil and plants. Introduction Plant diversity increases biomass production and soil organic matter (Zhou et al., 2022 ). Plant diversity plays an essential role in pest control and nutrient cycling (Cappelli et al., 2022 ). Plant diversity has different levels like ecosystem diversity, genetic diversity species diversity (Wan et al., 2022 ). Plant diversity maintains ecosystems and serves mankind by climate regulation, clean air, disease control, food and fiber. Diversity of plants increases genetic variability provides resilience, aesthetic enjoyment, cultural services, inspiration, education, recreation, and tourism (Paudel and States, 2023 ). Plant diversity is under serious threats and is facing rapid decline day by day all over the world. Preserving species richness is primarily important about plant diversity. Urbanization is affecting abundance of biodiversity (Parisha et al. , 2022). Environmental pollution, climate change, habitat destruction, and invasive species are particular threats to biodiversity (Kolavoli and Iyiola, 2023). Anthropogenic exploitation of plant diversity is leading to decline in kind and number of important species (Prakash and Verma, 2022 ). Global warming is also effect plant diversity gradually (Shao et al., 2022 ). Invasive plant species threaten the existence of other plant species (Duell et al. , 2023). Dust blocks the stomata, which affects the gaseous exchange of plants, which may eventually affect biochemical reactions such as photosynthesis and respiration. Leaf morphological attributes may also be disturbed, such as a reduction in leaf area take, which weakens the plants and causes them to die, disrupting the ecosystem (Nawaz et al. , 2022). Effects of dust on plants also depend upon their dust capturing abilities mainly depending on their leaf morphology (Tan et al. , 2022). Dust carrying toxic substances badly affect all the plant attributes (Colzi et al , 2022). Dust also reduces the yield of plants (Zarei et al., 2022 ). Plant organs growing under dusty stressed conditions exhibit altered anatomical characteristics as adaptive responses to their habitat ecology (Iqbal et al., 2023 ). Plants often tolerate stresses by developing abilities to withstand them. When the stress crosses the normal tolerance level, plants gather variable quantities of organic osmolytes, osmoprotectants (Gycinebetaine, proline etc.) and antioxidant enzymes (superoxide dismutase, catalase etc.) which help the plants to tolerate stresses and help them in their acclimatization towards maintaining their growth and development (Bandurska, 2022 ). Increase in leaf thickness under dust pollution is also an adaptation to dust pollution in various plants (Soheili et al., 2023 ). Plants show high sclerification to tolerate dust pollution (Delian and Savulescu, 2022). Aerenchyma formation on roots of plants growing in dust-polluted environments is also an anatomical adaptation to survive (Jan et al., 2022 ). Formation of large parenchyma cells also helps the plant to cope with dust pollution (De Micco et al., 2023 ). The study was conducted on riparian vegetation in industrial contaminated area of Faisalabad and Most of the morphoanatomical parameters notably attained a decrease in E. alba (Abbas et al., 2023 ). Present project was planned to explore the flora of the area, which gave knowledge about the plants present in the area which can be used in different ways to benefit humanity. Second purpose was to explore the morpho-anatomical, biochemical and physiological modifications along with heavy metal analysis in the commonly growing plants in the area. The aim was to investigate the effects and plant adaptations and their mechanism of survival under severe dust pollution caused by stone crushing industry of Kirana hills. Materials and Methods Sargodha Hills For this purpose extensive survey of the area was conducted during which to study sites were selected. For exploring seasonal variations in the plant community structure and the factors responsible for these variations dust, soil (Table 1 ), and vegetation data was collect from all study sites during all seasons. Geographical aspects of the sites selected were recorded. Plant community structure in the sites selected was observed. Table 1 Metal analysis of soil at Kirana Hills Sargodha, Dust polluted area October Site 1 Site 2 Site 3 Site 4 Site 5 Dust (g/m 2 ) 1035 2642 9142 7785 4678 Zn (mg/kg) 0.2635 0.2555 0.0387 0.2404 0.2502 Fe (mg/kg) 1.4325 1.0907 0.0843 0.9267 1.0173 Pb (mg/kg) 0.0224 0.0912 0.1567 0.1142 0.1246 Cd (mg/kg) 0.0314 0.0185 0.0346 0.0037 0.0279 Ni (mg/kg) 0.2212 0.8007 0.8303 0.9281 0.7646 January Site 1 Site 2 Site 3 Site 4 Site 5 Dust (g/m 2 ) 1285 3107 10071 8535 5500 Zn (mg/kg) 0.2676 0.2579 0.0407 0.2426 0.2537 Fe (mg/kg) 1.436 1.0942 0.0867 0.9294 1.0115 Pb (mg/kg) 0.0246 0.0935 0.1596 0.1171 0.1273 Cd (mg/kg) 0.0336 0.0206 0.0375 0.0089 0.0308 Ni (mg/kg) 0.251 0.8036 0.834 0.9315 0.7695 April Site 1 Site 2 Site 3 Site 4 Site 5 Dust (g/m 2 ) 607 1571 6892 5535 3428 Zn (mg/kg) 0.2617 0.2532 0.0349 0.2393 0.2481 Fe (mg/kg) 1.4302 1.0889 0.0824 0.9238 1.0145 Pb (mg/kg) 0.0206 0.0887 0.1539 0.1107 0.1213 Cd (mg/kg) 0.0297 0.0161 0.0319 0.0013 0.0258 Ni (mg/kg) 0.2176 0.7984 0.8273 0.9232 0.7609 July Site 1 Site 2 Site 3 Site 4 Site 5 Dust (g/m 2 ) 750 1892 8107 6750 4142 Zn (mg/kg) 0.2606 0.2518 0.0331 0.2378 0.2466 Fe (mg/kg) 1.4272 1.0865 0.0801 0.9216 1.0123 Pb (mg/kg) 0.0177 0.0866 0.1516 0.1083 0.1181 Cd (mg/kg) 0.0276 0.0138 0.0301 0.0089 0.0227 Ni (mg/kg) 0.2143 0.7959 0.8265 0.9218 0.7585 Selection of study sites After conducting a thorough survey, five environmentally varied study locations were chosen. Site selection was based on differences in environmental factors, primarily dust pollution (Ahmad et al., 2012 ). Site 1 It was situated at Sargodha bypass 18 km away from Sargodha City. It was totally dust free site with no stone crushers. S ite 2 : It was located in Shaheenabad, 12 km from Sargodha City. It was a low dust site because it was located some distance from active stone crushers. Site 3 It was situated near Shaheenabad 15 km away from Sargodha City. It was extremely dust polluted site, surrounded by large number of active stone crushing units. Site 4 It was situated near Pull 111, 22 km away from Sargodha City. It was highly dust polluted site, due to high stone crushing activity. Site 5 It was situated near 46 adda 27 km away from Sargodha City. It was moderately dust polluted site, because it was situated at some distance from stone crushers. Sampling Seasons Data for dust, soil (Table 1 ), and vegetation was recorded in triplicate from all the selected study sites throughout the year at regular interval of three months to include each season. Floristic composition of Kirana Hills For this reason, Kirana Hills was frequently visited throughout the year, and a thorough floristic catalogue of the area's vegetation was created. Plants were collected, dried, preserved, and identified by comparing them to the Pakistani flora (Nasir and Ali, 1978 ) before being deposited in the University of Sargodha's herbarium. Ecological analysis of vegetation On each study site 10 regular quadrates (each of 01m 2 ) were randomly laid. Density, frequency and coverage of vegetation were calculated by the method of (Ludwig et al. , 1988). Frequency % = Number of quadrates in which a species occurred/Total number of quadrates X 100 Density % = Number of plants of a species in a quadrate/Number of plants of all species in a quadrate x 100 Coverage % = Total area covered by a species in a quadrate/Total area covered by all the species in a quadrate X 100 Relative Frequency % = Frequency of a species/Total frequency of all the species X 100 Relative Density % = Density of a species in/Total density of all the species X 100 Relative Coverage % = Coverage of a species/Total coverage for all the species X 100 Importance value = Relative Coverage + Relative Density + Relative Frequency Selection of plants for study The current study chose four readily available, commonly growing local plants that can be found at all study sites throughout the year. Morpho-anatomical characteristics of these plants were examined for comparative purposes. The effects of dust pollution on the morpho-anatomical characteristics of chosen plants were investigated, as well as the mechanisms of their survival. Morphological parameters Following morphological parameters were measured, Leaf length (cm), Leaf width (cm), Leaf thickness (mm), Leaf area (cm 2 ). Anatomical Parameters Triplicate samples were taken to conduct anatomical studies on the leaf, stem, and root. A 1cm section of the leaf center was cut for anatomical research. For the stem anatomical research, a 1cm piece from the central node of the greatest stem was obtained, and for the root anatomical study, a 1 cm base of the thickest root was taken. All samples were maintained in a 70% ethyl alcohol solution. Then, free hand section cutting and staining were performed using the method of (Ruzin, 1999 ). After staining, portions were cleaned with xylene solution and mounted on slides with Canada balsam already pasted. Finally, the slices were coated with a cover slip, inspected using a compound microscope, and photographed. Physiological Parameters Leaf chlorophyll content (Chl. a , b , total chlorophyll) and carotenoids The chlorophyll a , chlorophyll b , total chlorophyll content, and carotenoids were determined using the method of Arnon ( 1949 ). For chlorophyll extraction, fresh leaf material (0.1 g) was collected and stored overnight in 80% acetone at 4°C; the extract was centrifuged at 10,000 x g for 5 minutes. The absorbance of the supernatant was measured at 663nm, 645nm, and 480nm using a spectrophotometer (Hitachi-U2001; Tokyo, Japan). The chlorophyll a and chlorophyll b were calculated by the following formula: Chlorophyll a (mg/g F. wt)= [12.7 (OD 663) -2.69 (OD 645)] x V/1000 x W] Chlorophyll b (mg/g F. wt)= [22.9 (OD 645) -4.68 (OD 663)] x V/1000 x W] Total Chlorophyll chl, a + chl. b Carotenoids (mg/g f.wt.) = Acar/ Em ×100 V = volume of the extract (ml) W = weight of the fresh leaf material (g) Acar = OD 480 + 0.114(OD 663)-0.638(OD 645) Em = 2500 Leaf relative water contents (LRWC): For calculation, the LRWC method of (Barr and Weatherley, 1962) was employed. Fresh weight of completely formed leaf samples was measured. The leaf samples were then soaked in distilled water for 8 hours before being weighed. At the end all samples were oven-dried at 70°C and their dry weight was recorded. Finally LRWC was calculated using the following equation: LRWC (%) = Leaf fresh weight - Leaf dry weight/Leaf turgid weight - Leaf dry weight x 100 Hydrogen peroxide (H 2 O 2 ) concentration [Reactive oxygen species (ROS)] Hydrogen peroxide content was determined by following the method of (Velikova et al., 2000 ). Leaf samples were homogenized in ice bath with 1ml of 0.1% (w/v) trichloroacetic acid (TCA). Then the homogenate was centrifuged at 12000g for 15 minutes. 0.1ml of the supernatant was then mixed with 10mM potassium phosphate buffer (pH 7.0) and 1M potassium iodide. Absorbance of mixture was read at 390nm using spectrophotometer. H 2 O 2 content was calculated by comparing with a standard calibration curve prepared by using different concentrations of H 2 O 2 . Peroxidase activity (Enzymatic antioxidant) Enzyme extraction: Fresh leaf sample (0.5g) was grinded in 5ml of 50m M cooled potassium phosphate buffer (pH 7.8). Grinded material was then filtered. Than homogenate was centrifuged at 15000g for 20 minutes at 4°C and supernatant was used for the enzyme assay. Peroxidase (POD) activity (Chance and Maehly, 1955) guaiacol oxidation method was used to for this purpose. The final volume of the reaction mixture for POD (3ml) contained 50m M phosphate buffer (pH 7.0), 20m M guaiacol, 40m M H 2 O 2 , and 0.1ml enzyme extract. Changes in absorbance of the reaction solution at 470 nm were read with the interval of 20s. One unit POD activity was defined as the change of 0.01 absorbance unit per min per mg of protein. Statistical analysis The data obtained after dust and soil analysis along with ecological, morpho-anatomical, physiological, biochemical and heavy metal analysis of vegetation was subjected to Canonical Correspondence Analysis (CCA) using conoco software version 4.5. The data were also subjected to a two-way ANOVA using the MS Excel tool. Results Floral density and relative density CCA triplots of density and relative density at regional scale showed the distinctive distribution of environmental and additional variables as well as species data. Soil attributes and heavy metals in dust showed variation at each site (Table 1 ). Dust concentration was highly associated with Site 3 and Site 4. Plant density was higher at Site 1 and lowest at Site 3. October depicts fall in the area. During October, CCA triplots of density and relative density revealed that in soil characteristics, EC was strongly related with Site 3, phosphorus, organic matter, and pH were plotted alongside Site 2, and potassium was positively associated with Site 1. In dust heavy metals nickel and lead were associated with Site 3 and Site 4 while cadmium, iron and zinc were plotted towards Site 1 and Site 2. Dust concentration was highly associated with Site 3 and Site 4. The density and relative density of most plants were positively associated with Sites 1 and 2, but negatively associated with Site 3. The density of plants decreased in the autumn. The density and relative density of Lathyrus sativus, Mullogo cerviana, Capparis deciduas, Tetrapogon villosus, Haloxylon recurvum, Dactylotenium scindicum, Croton capitatus, Echinochloa colona, Enneapogon persicus, Stipagrostis hirtigluma and Vachellia nilotica were associated with Site 1, Salvadora oleoides and Croton bonplandianusa were associated with Site 2 (Fig. 1 A & 2 A). January indicates winter in the area. During January, CCA triplots of density and relative density revealed that all environmental and supplemental factors were essentially identically associated with sites as in October, with a few outliers. Organic matter in soil variables was associated with both Sites 1 and 2. In dust, heavy metal zinc was associated with Site 1, whereas cadmium was associated with Site 5. Dust concentration was strongly linked with Site 3. Plant density was reduced during January. Density and relative density of plants showed same association as in October and most of the plants were associated with Site 1 (Fig. 1 B & 2 B). April is the month that depicts spring in this area. Plant density increased in April. The density and relative density of most plants were associated with Site 1. The density and relative density of the majority of plants were connected with Site 1, Salvadora oleoides and Croton bonplandianusa with Site 2, and Oxalis corniculata and Echinops echinatus with Site 5 (Fig. 1 C & 2 C). July represents summer in the area. During July CCA triplots of density and relative density showed that soil variables except pH were associated with Site 4 and Site 2, pH was again associated with Site 3. In dust heavy metals iron, zinc and cadmium were associated with Site 1, lead and nickel was associated with Site 3 and also Site 4 again. Dust concentration continued to be higher at Site 3. Plant density dropped in July compared to April. The density and relative density of most plants were associated with Site 1. Plant density was similarly higher at Sites 2 and 5, but declined at Sites 3 and 4. The density and relative density were high at Site 1 (Fig. 1 D & 2 D). CCA triplots of density and relative density at the temporal scale revealed more detailed distributions of environmental and supplemental factors, as well as species data. All soil factors except pH were connected with January and October, whereas pH was associated with April and July. All heavy metals found in dust were related with January. Dust concentration was also associated with January. Plant density peaked in April and fell to its lowest in January. Floral frequency and relative frequency CCA triplots of frequency and relative frequency at the spatial scale indicated the specific distribution of environmental and supplemental variables, as well as species data. Soil characteristics and heavy metal levels in dust varied between sites. Dust concentration was strongly linked with Site 3. The species frequency was highest at Site 1 and lowest at Site 3. During October, CCA triplots of frequency and relative frequency revealed that in soil parameters, EC was strongly linked with Site 3, phosphorus with Site 2, and organic matter, pH, and potassium were not connected with any site. Heavy metals nickel and lead were found in dust at Sites 4 and 3, respectively, while cadmium was found at Sites 1 and 5, although iron and zinc were not. Dust concentrations were once again strongly related with Site 3. Most plants' frequency and relative frequency were positively correlated with Site 1, but negatively correlated with Site 3 (Fig. 3 A & 4 A). CCA triplots of frequency and relative frequency showed that in soil parameters potassium was plotted alongside Site 1, EC was associated with Site 3, pH was positively associated with Site 2 while organic matter and phosphorus were not associated with any site. In dust heavy metals nickel and lead were associated with Site 3 and 4; cadmium was plotted towards Site 1 while iron and zinc were again not associated with any site. Dust concentration was associated with Site 3 and Site 4. Plant Frequency was decreased further during January. Frequency and relative frequency of most plants was in positive association with Site 1 while less frequent on Site 3 (Fig. 3 B & 4 B). During April CCA triplots of frequency and relative frequency showed that in soil parameters phosphorus, organic matter and potassium were plotted alongside Site 1 while EC was associated with Site 4 and pH was positively associated with Site 3. In dust heavy metals nickel and lead were associated with Site 4 and Site 3 respectively, cadmium was plotted towards Site 1 while iron and zinc were again not associated with any site. Dust concentration was again highly associated with Site 3. Plant frequency was increased during spring season. Frequency and relative frequency of most plants was in positive association with Site 1 while minimum on Site 3 (Fig. 3 C & 4 C). During July, CCA triplots of frequency and relative frequency revealed that all soil parameters except pH were related with Site 4, but pH was positively associated with Site 1. Heavy metals iron and cadmium were positively connected with Site 1, lead was plotted towards Site 3, nickel with Site 4, and zinc was not associated with any site. Dust concentrations were once again strongly related with Site 3. Plant frequency reduced during the hot summer of July compared to April (Fig. 3 D & 4 D). CCA triplots of frequency and relative frequency at temporal scale presented specific distribution of environmental and supplementary variables and species data. Soil variables except pH and EC were associated with January and October while pH was associated with April and July. Mostly all heavy metals in dust were associated with January. Dust concentration was also associated with January. Species frequency was highest in April and lowest in January. Floral cover and relative cover CCA triplots of cover and relative cover at spatial scale represented specific distribution of environmental and supplementary variables and species data. Soil attributes and heavy metals showed variation at each site. Dust was associated with Site 3 and Site 4. Species cover was highest on Site 1 and the lowest on Site 3. During October CCA triplots of cover and relative cover showed that all soil parameters except EC were not associated with any site while EC was associated with Site 3. The cover and relative cover of Lathyrus sativus, Mullogo cerviana, Capparis deciduas, Tetrapogon villosus, Haloxylon recurvum, Dactylotenium scindicum, Croton capitatus, Echinochloa colona, Enneapogon persicus, Stipagrostis hirtigluma and Vachellia nilotica were associated with Site 1, Salvadora oleoides and Croton bonplandianusa were associated with Site 2, while Oxalis corniculata and Echinops echinatus were found to be associated with Site 5. No plants were found in direct association with Site 3 and Site 4 (Fig. 5 A & 6 A). In January, CCA triplots of cover and relative cover revealed that soil characteristics Site 1 was connected with potassium, Site 2 with pH, Site 3 with EC, and no site was associated with organic matter or phosphorus. Nickel and lead were found in dust at Sites 3 and 4, respectively, while cadmium was found at Site 1, but iron and zinc were not. Site 3 had a higher concentration of dust. Plant cover reduced much further in January. Most plants' cover and relative cover were positively correlated with Site 1, but negatively correlated with Sites 3, and 4 (Fig. 5 B & 6 B). The Cover and relative cover of Indigofera atropurpurea, Arundo donax , Lathyrus sativus, Mullogo cerviana, Capparis deciduas, Tetrapogon villosus, Haloxylon recurvum, Dactylotenium scindicum, Croton capitatus, Echinochloa colona, Enneapogon persicus, Stipagrostis hirtigluma, Vachellia nilotica and Launaea procumbens were associated with Site 1, Salvadora oleoides and Croton bonplandianusa were associated with Site 2, Saccharum bengalense was associated with Site 3, Salsola imbricata was associated with Site 4 while Oxalis corniculata and Echinops echinatus were found to be associated with Site 5. Grewia villosa, Cynodon dactylon, Salsola fruticosa Aeluropus lagopoides, Sida ovate, Heliotropium strigosum and Cenchrus ciliaris were associated with all sites (Fig. 5 C & 6 C). During July CCA triplots of cover and relative cover showed that all soil parameters except pH were associated with Site 4 while pH was not associated with any site. In dust heavy metals nickel and lead were associated with Site 3 and Site 4, cadmium was associated with Site 1 while iron and zinc were not associated with any site. Dust concentration was again associated with Site 3. Plant cover decreased during July compared to April. Cover and relative cover of most plants was in positive association with Site 1 while in negative correlation with Site 3 (Fig. 5 D & 6 D). CCA triplots of cover and relative cover at temporal scale presented specific distribution of environmental and supplementary variables and species data. Soil attributes showed variation with each season. Mostly all heavy metals were associated with January. Dust concentration was associated with October and January. Species cover was highest in April and lowest in January. ANOVA showing morpho-anatomical attributes of Fagonia indica A variance analysis of Fagonia indica leaf morpho-anatomical attributes revealed that variation in all leaf parameters (leaf area, leaf length, leaf width, leaf thickness, leaf upper and lower epidermis cell area, leaf metaxylem cell area, leaf mesophyll cell area, and leaf sclerenchyma thickness) was highly significant at the spatial scale among all sites. Except for leaf area, leaf mesophyll cell area, and leaf upper epidermis cell area, there were substantial temporal fluctuations across all leaf characteristics throughout all seasons. Leaf area showed significant while leaf mesophyll cell area and leaf upper epidermis significant and highly significant variation. Analysis of variance showing root morpho-anatomical attributes of Fagonia indica highlighted at spatial scale among all sites variation in all root parameters (root radius, root aerenchyma cell area, root epidermal cell area, root metaxylem cell area, root endodermis thickness, root parenchyma cell area, root radius, root sclerenchyma thickness and root vascular bundle thickness) was very highly significant. At temporal scale in all seasons variation among most root parameters was very highly significant except for root epidermal cell area, root endodermis thickness, root parenchyma cell area and root sclerenchyma thickness which showed not very highly significant but highly significant variation. Variation in all stem parameters (stem epidermal cell area, stem metaxylem cell area, stem parenchyma cell area, stem radius, stem sclerenchyma thickness, and stem vascular bundle thickness) was highly significant at the spatial scale across all sites, according to a variance analysis of Fagonia indica stem morpho-anatomical attributes. At the temporal scale in all seasons, variation among all stem parameters was similarly very highly significant, except for stem parenchyma cell area and stem vascular bundle thickness, which exhibited not very highly significant but very significant variation (Table 2 ). Table 2 Summary of ANOVA ( F ratios) for morpho-anatomical attributes of Fagonia indica Parameters Sites Seasons LA 27.54545*** 4.63636* LL 11.96317*** 18.93315*** LLE 45.16766*** 32.52695*** LM 77.94188*** 19.3234*** LP 54.74151*** 7.251572** LS 28.38462*** 62.15385*** LT 166.3714*** 25.71429*** LUE 39.79245*** 9.912253** LW 50.20388*** 36.7767*** RA 13.05998*** 18.02999*** RE 34.71792*** 6.965005** RM 84.89905*** 25.35695*** RN 13.66667*** 9.333333** RP 47.20328*** 7.906683** RR 173.2424*** 49.53535*** RS 55.36364*** 9.636364** RV 158.1818*** 42.36364*** SE 20.72028*** 13.01632*** SM 656.7692*** 120.6154*** SP 31.08293*** 6.63528** SR 117.4669*** 19.07393*** SS 25.69565*** 29.91304*** SV 178.2447*** 8.623729** df: Sites 4, Season 3, Error 12, * = significant variation, **= highly significant variation ***= very highly significant variation, ns = non significant variation Bar graphs showing morpho-anatomical attributes of Fagonia indica Bar graphs depicting stem morpho-anatomical features of Fagonia indica revealed that at a spatial scale, all stem parameters dropped at heavily dust contaminated Site 3, with the exception of stem sclerenchyma thickness, which grew significantly. All other stem anatomical characteristics rose significantly at dust-free Site 1, with the exception of stem sclerenchyma thickness, which fell dramatically. At the temporal scale, all other stem anatomical characteristics dropped during the peak winter season in January, with the exception of stem sclerenchyma thickness, which grew significantly in January. All other stem anatomical characteristics grew significantly throughout the spring season in April, with the exception of stem sclerenchyma thickness, which fell dramatically (Fig. 7 A- 7 D). The stem anatomical variations at each sites during different seasons indicates the clear differences in stem structures of study ecotype (Fig. 11 ). Bar graphs showing root morpho-anatomical attributes of Fagonia indica highlighted at spatial scale all root anatomical parameters decreased at extremely dust polluted Site 3 except root aerenchyma cell area and root sclerenchyma thickness which highly increased at Site 3. Values of all other root anatomical parameters were recorded highest at dust free Site 1 except root aerenchyma cell area and root sclerenchyma thickness which highly decreased at Site 1. At temporal scale values of all other root anatomical parameters were recorded highest during spring season in April except root aerenchyma cell area and root sclerenchyma thickness which highly decreased in April. All other root anatomical parameters decreased during peak winter season in January except root aerenchyma cell area and root sclerenchyma thickness which highly increased in January (Fig. 8 A- 8 H). The root anatomical variations at each sites during different seasons indicates the clear differences in root structures of study ecotype (Fig. 12 ). Bar graphs showing leaf morpho-anatomical attributes of Fagonia indica highlighted at spatial scale among all leaf morpho-anatomical parameters decreased at extremely dust polluted Site 3 except leaf sclerenchyma thickness which highly increased at Site 3. All other leaf morpho-anatomical parameters highly increased at dust free Site 1 except leaf sclerenchyma thickness which highly decreased at Site 1. At temporal scale, content of all leaf morpho-anatomical parameters decreased during peak winter season in January except leaf sclerenchyma thickness which highly increased in January. All other leaf morpho-anatomical parameters highly increased during spring season in April except leaf sclerenchyma thickness which highly decreased in April (Fig. 9 A- 9 I). The leaf anatomical variations at each sites during different seasons indicates the clear differences in leaf structures of study ecotype (Fig. 13 ). CCA triplots showing morpho-anatomical attributes of Fagonia indica CCA triplots depicting morpho-anatomical characteristics of Fagonia indica at a geographical scale revealed a particular distribution of environmental and additional factors. Soil characteristics and heavy metal levels in dust varied between sites. Dust concentrations were strongly related with Sites 3 and 4. Leaf, stem, and root sclerenchyma, as well as root aerenchyma, were connected with Sites 3 and 4, respectively, whereas all other characteristics were linked to Site 1. No parameter was directly related to Site 5. CCA triplots showing morpho-anatomical attributes of Fagonia indica presented that in soil parameters potassium and organic matter were associated with Site 1, phosphorus and EC were associated with Site 3 while pH was associated with Site 2. In dust heavy metals nickel and lead were associated with Site 4 and Site 5, cadmium was associated with Site 1 while iron and zinc were not associated with any particular site (Fig. 10 A). January CCA triplots showing morpho-anatomical attributes of Fagonia indica presented that in soil parameters potassium, organic matter and phosphorus were associated with Site 1 and Site 2, EC was associated with Site 3 while pH was associated with Site 2. In dust heavy metals nickel and lead were associated with Site 4, cadmium was associated with Site 1 and Site 2 while iron and zinc were not associated with any particular site. Dust concentration was associated with Site 3 and 4. Leaf and root sclerenchyma were associated with Site 3. Stem sclerenchyma and root aerenchyma were associated with Site 4 (Fig. 10 B). In dust heavy metals nickel and lead were associated with Site 4, cadmium was associated with Site 1 and Site 2 while iron and zinc were not associated with any particular site. Dust concentration was associated with Site 3. Leaf, stem and root sclerenchyma were associated with Site 3. Stem and root vascular bundle thickness was associated with Site 4. Root and stem metaxylem area, leaf length, leaf area and root parenchyma were positively associated with Site 2 while all other parameters were associated with Site 1. No parameter was found in direct association with Site 5 (Fig. 10 C). Root sclerenchyma was associated with Site 3. Stem and root radius, stem and root sclerechyma, and root aerenchyma were associated with Site 4. Root and stem metaxylem area, leaf length, leaf area and root parenchyma were positively associated with Site 2 while all other parameters were associated with Site 1. No parameter was found in direct association with Site 5 (Fig. 10 D). CCA triplots showing morpho-anatomical attributes of Fagonia indica at temporal scale highlighted specific distribution of environmental and supplementary variables. All soil variables except pH were associated with January and October while pH was associated with April and July. All heavy metals in dust were associated with January and October. Dust concentration was also associated with January and October. Leaf, stem and root sclerenchyma were associated with January, root aerenchyma was associated with October, while all other parameters were associated with April and July respectively. CCA triplots showing physiological attributes along with heavy metals in Fagonia indica CCA triplots of physiological and biochemical parameters, as well as heavy metals, in Fagonia indica at different geographical scales revealed a particular distribution of ambient and additional factors. Soil attributes and heavy metals in dust showed variation at each site. Dust concentration was highly associated with Site 3 and Site 4. Leaf chlorophyll a , leaf chlorophyll b , and leaf relative water content were associated with Site 1 while hydrogen peroxide was associated with Site 3 and Site 4. In root and shoot heavy metals zinc and iron were not associated with any particular site while lead, nickel and cadmium were associated with Site 3 and Site 4. CCA triplots of physiological and biochemical parameters, as well as heavy metals in Fagonia indica , revealed a particular distribution of ambient and additional factors on a time scale. Soil attributes showed variation in each season. Heavy metals in dust were associated with October and January. Dust concentration was highly associated with October and January. Leaf chlorophyll a and chlorophyll b , and leaf relative water content were associated with April and July while peroxidase (POD), and hydrogen peroxide were associated with October and January. In root and shoot all heavy metals were associated with October and January (Fig. 10 A- 10 D). ANOVA showing physiological and biochemical attributes in Fagonia indica An analysis of variance of physiological properties of Fagonia indica revealed that at the geographic scale, variation in all physiological parameters (chlorophyll a , chlorophyll b , total chlorophyll, and leaf relative water content) was quite significant. At the temporal scale, most physiological indicators varied significantly throughout all seasons, with the exception of chlorophyll a and leaf relative water content, which varied little but significantly. Analysis of variance displaying biochemical properties of Fagonia indica revealed that at geographical scale among all sites variation in all biochemical attributes (hydrogen peroxide, peroxidase) was quite significant. At temporal scale in all seasons variation among all biochemical parameters was also very highly significant. Analysis of variance showing heavy metals in Fagonia indica highlighted that at spatial scale among all sites variation in concentration of all heavy metals (cadmium, iron, nickel, lead and zinc) in root Fand stem was very highly significant. At temporal scale in all seasons variation among all heavy metals in root and stem was also very highly significant (Table 3 ). Table 3 Summary of ANOVA ( F ratios) for physiological and biochemical attributes along with heavy metals in Fagonia indica Parameters Sites Seasons Chl a 48.67547*** 9.213566** Chl b 377.4299*** 31.7656*** T Chl 125.3072*** 16.72954*** Car 125.979*** 24.30677*** LRWC 176.3699*** 10.30647** H2O2 327.621*** 54.86912*** POD 45.57717*** 10.03477** AsA 437.6077*** 71.46053*** Pro 85.66406*** 33.79379*** RCd 87.92323*** 29.60736*** RFe 338.4718*** 45.34413*** RNi 989.9669*** 143.3724*** RPb 84.14751*** 15.73137*** RZn 363.4377*** 30.61378*** SCd 231.3588*** 63.59681*** SFe 240.1655*** 65.00392*** SNi 1088.401*** 157.071*** SPb 201.5119*** 29.66209*** SZn 411.9715*** 67.5304*** df: Sites 4, Season 3, Error 12, * = significant variation, **= highly significant variation, ***= very highly significant variation, ns = non significant variation Bar graphs showing physiological and biochemical attributes in Fagonia indica Bar graphs of physiological attributes of Fagonia indica revealed that at the spatial scale, all physiological parameters (chlorophyll a , chlorophyll b , total chlorophyll, carotenoids, and leaf relative water content) decreased at extremely dust polluted Site 3, while their values were highest at dust-free Site 1. On a time scale, the content of all physiological markers declined during the peak winter season in January, while their levels peaked in April. Bar graphs of physiological attributes of Fagonia indica highlighted that at spatial scale all physiological parameters (chlorophyll a , chlorophyll b , total chlorophyll, carotenoids and leaf relative water content) decreased at extremely dust polluted Site 3 while their value was recorded highest at dust free Site 1. At temporal scale, content of all physiological parameters decreased during peak winter season in January while their values was recorded highest during spring season in April. Bar graphs showing biochemical attributes of Fagonia indica highlighted that at spatial scale all biochemical attributes (hydrogen peroxide) were lower in value at dust free Site 1 while their maximum values were recorded at extremely dust polluted Site 3. At temporal scale, all biochemical attributes increased during peak winter season in January while their values was recorded lowest during spring season in April. The RD29 was recorded high at the dust polluted sites as compared to dust free sites and the metabolic rate was decreased at sites near to the dust source (Fig. 14 A- 14 I). Bar graphs showing heavy metals in root and shoot of Fagonia indica highlighted that at spatial scale concentration of all heavy metals (cadmium, iron, nickel, lead and zinc) in root and stem was higher at extremely dust polluted Site 3 while their lowest concentration was recorded at dust free Site 1. At temporal scale concentration of all heavy metals was recorded lowest during spring season in April while their concentration was on the higher side during peak winter season in January (Fig. 15 A- 15 J). Discussion Soil and dust analysis On temporal scale almost all soil parameters were higher in concentration in winter and autumn and their concentration was relatively lower in spring and summer. It may be due to the changes in soil moisture, amount of rainfall and temperature which are minimum in winter and maximum in summer as revealed by (Alavi et al., 2016 ) or it may be due to amount of dust-fall which is maximum in winter and minimum in summer, dust carry heavy metals and deposit them on soil according to the study of (Sun et al. , 2020) on dust pollution. On temporal scale dust in maximum concentration was recorded in winter followed by autumn, higher dust concentration may be due to low rainfall, high humidity, low temperature, cool wind and dust storms as revealed by (Khusfi et al., 2020 ). Minimum dust concentration was recorded in spring followed by summer, lower dust concentration may be due high rainfall, low humidity and high temperature as studied by (Behrouzi et al. , 2019; Khusfi et al., 2020 ) in their study on seasonal variations in dust pollution. Ecological analysis of vegetation On spatial scale some plants i.e. Aristida mutabilis, Aerva javanica, Cenchrus ciliaris, Cynodon dactylon Fagonia indica and Salsola imbricata were found on all selected study sites, it may be due to their dust tolerance abilities as revealed by (Roy et al., 2020 ) in their study on dust pollution tolerance in certain tree species that certain plants can tolerate dust pollution. Plants also have survival mechanisms limited supply of soil nutrients as studied by (Ahmed et al., 2020 ) in their study on stress tolerance in plants due to shortage of soil nutrients. At the temporal scale, CCA triplots showed that the density, frequency, cover, and important value of vegetation were highest in spring, followed by summer. It may be due to lower dust concentration in spring and summer which supported plant growth as revealed by (Prajapati and Tripathi, 2008 ) and high soil moisture in July followed by spring which favours vegetation growth as revealed by the study of (Bhatt et al., 2020 ) on impact of seasonal rainfall on vegetation dynamics. On temporal scale some plants i.e. Aristida mutabilis, Aerva javanica, Cenchrus ciliaris, Cynodon dactylon Fagonia indica and Salsola imbricata were found in all seasons. It may be due to their abilities for seasonal environmental dust tolerance as studied by (Roy et al., 2020 ) in their study on dust tolerance abilities of certain tress, their dust tolerance mechanisms in accordance with the study of (Rai, 2020 ). On temporal scale some plants i.e. Aristida mutabilis, Aerva javanica, Cenchrus ciliaris, Cynodon dactylon Fagonia indica and Salsola imbricata were found all seasons, it may be due to their dust tolerance abilities in high dust concentration in winters as studied by (Roy et al., 2020 ) in their study on seasonal air pollution tolerance abilities of certain trees. Morpho-anatomical analysis of vegetation Leaf sclerification increased at extreme and high dust polluted sites, it may be adaptive response of local plants to the abiotic stress induced by dust pollution of stone crushing industry in accordance with the study of (Ivanescu and Gostin, 2007 ; Öztürk et al., 2015 ) on plant pollutants. Stem sclerification increased at extreme and high dust polluted sites, it may be an adaptive response of local plants to the abiotic stress induced by dust pollution of stone crushing industry as supported by the studies of (Ivanescu and Gostin, 2007 ; Öztürk et al., 2015 ) on cytological changes in plants in response to air pollution. Root sclerification increased at extreme and high dust polluted sites, it may be adaptive response of local plants to the abiotic stress induced by dust pollution of stone crushing industry in accordance with the study of (Ivanescu and Gostin, 2007 ; Öztürk et al., 2015 ) plant adaptations to dust pollution. At temporal scale all leaf, root and stem morpho-anatomical attributes increased in spring followed by summer except leaf, stem and root sclerification along with root arenchyma formation which decreased in spring and summer. It may be due to the lower dust concentration in spring and summer as supported by the study of (Prajapati and Tripathi, 2008 ). In winter and fall, all leaf, root, and stem morpho-anatomical qualities declined, with the exception of leaf, stem, and root sclerification and root arenchyma development, which rose. It may be due to the higher dust concentration in winter and autumn which negatively effects vegetation growth as revealed by (Kameswaran et al., 2019 ). All selected plants survived in all seasons especially in cold winters with high suspended dust, it may be due to their morpho-anatomical modifications i.e. presence of large parenchyma cells and leaf, root and stem sclerification as revealed by the findings of (Öztürk et al., 2015 ) and root aerenchyma formation as described by (Naseer et al., 2017 ) in their study on plant morpho-anatomical adaptations in response to dust pollution. Physiological, biochemical and heavy metal analysis of vegetation Dust from stone crushing industries negatively affect the plant physiological and biochemical attributes i.e. decreased photosynthetic pigments e.g. chlorophyll and carotenoids as in accordance with the results of the study conducted by (Sharma et al., 2019 ) on impact of dust pollution on two selected plant species, decreased leaf relative water content as revealed by the study of (Jabeen, 2019 ) conducted to find out dust tolerance abilities of some selected plants, production of reactive oxygen species (ROS) e.g. hydrogen peroxide as mentioned by (Dhir, 2016 ) in his study conducted on effects of dust pollution on plants. Plant survive in harsh dust polluted conditions physiologically and biochemically by producing enzymatic antioxidants e.g. peroxidase as described by (Siqueira-Silva et al., 2016 ) in their study on impact of dust pollution on Cedrela fissilis . In present study at spatial scale physiological parameters of all selected plants i.e. leaf chlorophyll a , chlorophyll b and carotenoids, leaf relative water showed significant variation in response to dust pollution. All these parameters were highest in concentration at dust free site followed by low dust site and intermediate at moderate dust site. Higher values of physiological attributes may be because of no dust pollution caused by stone crushing industry due to the absence of stone crushers on these sites as revealed by (Iqbal et al., 2016 ; Padhy, 2013 ) that dust pollution severely effect plant physiological attributes, lower heavy metals concentration in soil and air because of no industrial effluents so reduced effect of heavy metals on physiology of selected plants as described by (Jaiswal et al., 2018 ). All the above physiological attributes were lower at high dust site and extreme dust site. This decrease in these physiological parameters may be due to high dust pollution caused by active stone crushers at these sites as supported by the study of (Padhy, 2013 ) that dust pollution strongly effects plant physiological attributes, higher heavy metals concentration in soil and air because of higher dust pollution of stone crushing industry which increase the impact of heavy metals on plant physiology as revealed by (Asati et al., 2016 ; Padhy, 2013 ). All the above biochemical attributes were increased at extreme dust site and high dust site. The increase in these biochemical parameters may be due to high dust pollution caused by active stone crushes at these sites which cause abiotic stress and reactive oxygen species (ROS) are produced in response to which plants produce enzymatic and non-enzymatic antioxidants along with osmoprotactants to survive in such harsh environment (Padhy, 2013 ), lower concentration of soil organic matter also cause abiotic stress to plants as revealed by (Stefanowicz et al., 2020 ). Increase in plant enzymatic antioxidants and non-enzymatic e.g. peroxidase and ascorbic acid may be due to their role in scavenging over-produced ROS through different ways as supported by the study of (Chaudhary and Rathore, 2019 ; Siqueira-Silva et al., 2016 ). Heavy metals (Cd, Fe, Ni, Zn, Pb) in high concentrations can be toxic to plants and can decrease plant growth and yield as revealed by (Ryzhenko et al. , 2018) in their study on heavy metal toxicity. Heavy metal concentration i.e. Cd, Fe, Ni, Zn, Pb in root and shoot of selected plants varied in response to dust pollution. All heavy metals were lowest in concentration at dust free site followed by low dust site and moderate dust site, lower concentration of heavy metals may be due to the absence of stone crushers on these sites as revealed by (Iqbal et al., 2016 ; Padhy, 2013 ) in their study that dust pollution from stone crushing industry carry heavy metals, or may be due to lower heavy metals concentration in soil because of no industrial effluents at these sites as in accordance with study of (Jaiswal et al., 2018 ) on heavy metals. Concentration of all above heavy metals in root and shoot of all selected plants were highest at extreme dust site followed by high dust site. This increase in heavy metal concentration may be due to high dust pollution caused by active stone crushes at these Sites as revealed by (Padhy, 2013 ) that dust pollution emitted during crushing process carry heavy metals, or may be due to higher heavy metals concentration in soil because of higher dust pollution of stone crushing industry as supported by the study of (Asati et al., 2016 ; Padhy, 2013 ) on dust pollution and heavy metals. At temporal scale in spring and summer season all physiological parameters increased while all biochemical attributes along with heavy metals decreased. It may be due to the lower dust concentration in spring and summer as mentioned by (Prajapati and Tripathi, 2008 ). All selected plants survived in all seasons especially in cold winters with high suspended dust, it may be due to their physiological and biochemical modification i.e. formation of enzymatic and non-enzymatic anti-oxidants as revealed by (Gill and Tuteja, 2010 ) along with increase in osmoprotecrants as elaborated by (Ashraf and Foolad, 2007 ) in their study on plants abiotic stress resistance mechanisms. Conclusions Kirana hills are heavily contaminated by stone crusher dust, with the strongest impacts documented near the active crushing region. Dust pollution had a considerable impact on soil physicochemical parameters as well as heavy metal deposition at these sites. The vegetation cover of local plant community was severely affected by extreme dust pollution of stone crushing industry and Fagonia indica exhibited high morpho-anatomical adaptations for survival in such harsh dust polluted environment. Vegetation at such areas can be enhanced by promoting cultivation of local dust tolerant plants species. Declarations Acknowledgments The authors extend their appreciation to the Researchers Supporting Project number (RSP2024R118) of King Saud University, Riyadh, Saudi Arabia. Author Contributions Muhammad Asim Sultan wrote the original draft; Iftikhar Ahmad Supervised the study; Toqeer Abbas perform the formal analysis; Anis Ali Shah conducted the experiment; Hosam O. Elansary helped in drafting and writing, and Shankarappa Sridhara helped in Conceptualization, graphs and statistics. Funding Researchers supporting the project (RSP2024R118) at King Saud University. Riyadh, Saudi Arabia Data availability All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Ethical Acceptance and Participation The institutional human ethics council at the University of Sargodha gave its approval to each of the protocols used in this study (Approval No. 37-21S IEC UOS). All of the experimental procedures used in this investigation complied with all applicable guidelines and laws. The authors state that the manuscript had never before been published. Permission to publish All participants granted their approval for the textual information (the "Material") to be published in the aforementioned article and journal. For the publication of this manuscript, all authors have provided their written approval. Conflict of Interests The authors assert that there are no competing interests. References Abbas, T., Ahmad, I., Khan, Z.I., Shah, A.A., Casini, R. and Elansary, H.O., 2023. Stress mitigation by riparian flora in industrial contaminated area of River Chenab Punjab, Pakistan. PeerJ, 11 , p.e15565. 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Journal of Environmental Assessment Policy and Management, 24(03), p.2250032. Zhou, P., Zhang, L. and Qi, S., 2022. Plant Diversity and Aboveground Biomass Interact with Abiotic Factors to Drive Soil Organic Carbon in Beijing Mountainous Areas. Sustainability, 14(17), p.10655. Additional Declarations No competing interests reported. Supplementary Files ANOVAMACC.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4369086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":305650104,"identity":"ddf220aa-c374-40bd-928e-3c9bf67a50a4","order_by":0,"name":"Muhammad Asim Sultan","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Asim","lastName":"Sultan","suffix":""},{"id":305650105,"identity":"26d42ee9-2de5-4d3b-827f-c09b95130c81","order_by":1,"name":"Iftikhar Ahmad","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"","firstName":"Iftikhar","middleName":"","lastName":"Ahmad","suffix":""},{"id":305650106,"identity":"749f42f9-6f22-4426-a77c-6ad4a0efc1cb","order_by":2,"name":"Toqeer Abbas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBCDBAMgcSChAkgyMzfgVcqDpIXxwYMzIC2MxGthNnzYBmIT0GLP3rzxwcc9dnnm7MefSSTOq43mbwdq+VGxDbctPMeKDWc8Sy627Mkxk0jcdjx3xmHGBsaeM7dxa5HIMZPmOcCcuOFADhtQy7HcBqAWZsY2vFrMf/85UJ+44fxzoMPmHMudT4QWM2aGA4cTN9xIMDZIbKjJ3UBQy5ljxZI9B44XW854Y/gg4diB3I1ALQfx+YW9vXnjhx8HqvPM+dMfHPxRU5c77/zhgw9+VODWAgQGyJzDYPIAPvXoWuoIKB4Fo2AUjIKRCAA5DGKY2DXFhgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Sargodha","correspondingAuthor":true,"prefix":"","firstName":"Toqeer","middleName":"","lastName":"Abbas","suffix":""},{"id":305650107,"identity":"968e52b4-6602-4f25-9f9d-6b47686469ae","order_by":3,"name":"Anis Ali Shah","email":"","orcid":"","institution":"University of Education","correspondingAuthor":false,"prefix":"","firstName":"Anis","middleName":"Ali","lastName":"Shah","suffix":""},{"id":305650108,"identity":"baf3eba4-9fa1-4ef5-89e2-223b518d09f5","order_by":4,"name":"Hosam O. Elansary","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Hosam","middleName":"O.","lastName":"Elansary","suffix":""},{"id":305650109,"identity":"49052203-1cf3-4913-9a1c-af8469fcd25b","order_by":5,"name":"Shankarappa Sridhara","email":"","orcid":"","institution":"University of Agriculture and Horticulture Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shankarappa","middleName":"","lastName":"Sridhara","suffix":""}],"badges":[],"createdAt":"2024-05-04 15:53:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4369086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4369086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57002819,"identity":"3eb3362b-3521-43fd-aa3f-5397cbc9e5e8","added_by":"auto","created_at":"2024-05-23 09:14:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":307833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e1A-1D: CCA tripolts showing density of vegetation on spatial scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/d2b3ed46f299e8195f2f695c.png"},{"id":57002485,"identity":"16029042-0d67-45de-ae25-33aef8937952","added_by":"auto","created_at":"2024-05-23 09:06:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":317841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2A-2D: CCA tripolts showing relative density of vegetation on spatial scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/1ceb4eb61274380eff4d1588.png"},{"id":57002489,"identity":"4451d968-69a6-46f3-9767-3018be6d4a5c","added_by":"auto","created_at":"2024-05-23 09:06:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":324171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3A-3D: CCA tripolts showing frequency of vegetation on spatial scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/b7ef20de5cdf4fe1c31a8ea5.png"},{"id":57002492,"identity":"71eb39fe-f2c0-4a7a-9cbe-9382feb5ee28","added_by":"auto","created_at":"2024-05-23 09:06:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":330897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e4A-4D: CCA tripolts showing relative frequency of vegetation on spatial scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/f003dd0e660a7ce354a7286f.png"},{"id":57002493,"identity":"98f72abb-7d04-4422-a3f2-a2e5153aa130","added_by":"auto","created_at":"2024-05-23 09:06:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":318043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5A-5D: CCA tripolts showing cover of vegetation on spatial scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/9b1d121ad256c1222997afb6.png"},{"id":57002818,"identity":"e6706071-6f54-4122-9f75-d643876ec142","added_by":"auto","created_at":"2024-05-23 09:14:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":364050,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e6A-6D: CCA tripolts showing relative cover of vegetation on spatial scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/4658148f5af947f5e6e8c5d6.png"},{"id":57002499,"identity":"acfd938b-81df-4ead-9b26-ffe3c9500f8c","added_by":"auto","created_at":"2024-05-23 09:06:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1028492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e7A-7D: Seasonal variations in stem morpho-anatomical attributes of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at each site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/49b7bb76af4f89d4fa6da99c.png"},{"id":57002821,"identity":"50705e70-633b-4b3b-a493-ce31469fc41e","added_by":"auto","created_at":"2024-05-23 09:14:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":893140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e8A-8H: Seasonal variations in root morpho-anatomical attributes of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at each site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/e59d7c56d557d7ed5d64c4b6.png"},{"id":57002823,"identity":"f81b70d4-356b-4012-aa9a-c1f67b93c94b","added_by":"auto","created_at":"2024-05-23 09:14:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":881529,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e9A-9I: Seasonal variations in leaf morpho-anatomical attributes of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at each site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/a183be573ca09af8a5f47a05.png"},{"id":57002498,"identity":"85114b83-edb6-4873-8de4-ca5bd6cbb642","added_by":"auto","created_at":"2024-05-23 09:06:12","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":241195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e10A-10D: CCA tripolts showing morpho-anatomical attributes of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e on spatial scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/dc5bfbec60efe06a4ea14364.png"},{"id":57002496,"identity":"cf5722b6-7513-4874-83a0-f68eadc57a32","added_by":"auto","created_at":"2024-05-23 09:06:11","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1464396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal variations in stem anatomical features of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eat each site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/ecd042c596578f324c2c564d.png"},{"id":57002494,"identity":"0e62f014-5eb5-469e-a68f-43c442fe0ef8","added_by":"auto","created_at":"2024-05-23 09:06:11","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1580410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal variations in root anatomical features of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eat each site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/0f9db5fa4e9ec524cdbd10f3.png"},{"id":57003255,"identity":"b3bf0ff7-09e7-4c4c-b65b-324981b7c7ba","added_by":"auto","created_at":"2024-05-23 09:22:10","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1287895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal variations in leaf anatomical features of by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAerva javanica \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eat each site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/74418647423e9cb84c3f77b9.png"},{"id":57002500,"identity":"2856cd9f-ef71-4c43-bb28-b54329834c58","added_by":"auto","created_at":"2024-05-23 09:06:12","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":916394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e14A-14H: Seasonal variations in heavy metals concentration of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at each site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/46d69b0c43a9024bbea702bf.png"},{"id":57002824,"identity":"c53b93ba-b1f4-4682-ac90-dc27dab946bc","added_by":"auto","created_at":"2024-05-23 09:14:12","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":871293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e15A-15J Seasonal variations in heavy metals concentration of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagonia indica\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at each site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/8a0cca33dc11203ebcb56142.png"},{"id":61402120,"identity":"f4ce75e8-1b0d-480d-9320-d2d47cc8fe44","added_by":"auto","created_at":"2024-07-30 10:12:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11534693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/0373ae09-a8a2-4169-b711-cc256ed72a89.pdf"},{"id":57003256,"identity":"74c46228-9605-4e10-8c53-0a4f8f8d8a20","added_by":"auto","created_at":"2024-05-23 09:22:10","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":44763,"visible":true,"origin":"","legend":"","description":"","filename":"ANOVAMACC.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4369086/v1/5ffa3499ee847b91196d15bb.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of Heavy dust pollution reduces biodiversity by altering the metabolism and biochemical characteristics of Fagonia indica","fulltext":[{"header":"Key findings","content":"\u003cp\u003eUsing physiological and metabolic results in the deposition of dust in the soil, plants its transfer to metabolic pathways, the activity of enzymatic antioxidants increased. On-going monitoring of dust pollution and treatment is essential to mitigate the health hazards linked to metal toxicity in soil and plants.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePlant diversity increases biomass production and soil organic matter (Zhou et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plant diversity plays an essential role in pest control and nutrient cycling (Cappelli et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plant diversity has different levels like ecosystem diversity, genetic diversity species diversity (Wan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plant diversity maintains ecosystems and serves mankind by climate regulation, clean air, disease control, food and fiber. Diversity of plants increases genetic variability provides resilience, aesthetic enjoyment, cultural services, inspiration, education, recreation, and tourism (Paudel and States, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plant diversity is under serious threats and is facing rapid decline day by day all over the world. Preserving species richness is primarily important about plant diversity. Urbanization is affecting abundance of biodiversity (Parisha \u003cem\u003eet al.\u003c/em\u003e, 2022). Environmental pollution, climate change, habitat destruction, and invasive species are particular threats to biodiversity (Kolavoli and Iyiola, 2023). Anthropogenic exploitation of plant diversity is leading to decline in kind and number of important species (Prakash and Verma, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Global warming is also effect plant diversity gradually (Shao et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Invasive plant species threaten the existence of other plant species (Duell \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003cp\u003eDust blocks the stomata, which affects the gaseous exchange of plants, which may eventually affect biochemical reactions such as photosynthesis and respiration. Leaf morphological attributes may also be disturbed, such as a reduction in leaf area take, which weakens the plants and causes them to die, disrupting the ecosystem (Nawaz \u003cem\u003eet al.\u003c/em\u003e, 2022). Effects of dust on plants also depend upon their dust capturing abilities mainly depending on their leaf morphology (Tan \u003cem\u003eet al.\u003c/em\u003e, 2022). Dust carrying toxic substances badly affect all the plant attributes (Colzi \u003cem\u003eet al\u003c/em\u003e, 2022). Dust also reduces the yield of plants (Zarei et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlant organs growing under dusty stressed conditions exhibit altered anatomical characteristics as adaptive responses to their habitat ecology (Iqbal et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plants often tolerate stresses by developing abilities to withstand them. When the stress crosses the normal tolerance level, plants gather variable quantities of organic osmolytes, osmoprotectants (Gycinebetaine, proline etc.) and antioxidant enzymes (superoxide dismutase, catalase etc.) which help the plants to tolerate stresses and help them in their acclimatization towards maintaining their growth and development (Bandurska, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Increase in leaf thickness under dust pollution is also an adaptation to dust pollution in various plants (Soheili et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlants show high sclerification to tolerate dust pollution (Delian and Savulescu, 2022). Aerenchyma formation on roots of plants growing in dust-polluted environments is also an anatomical adaptation to survive (Jan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Formation of large parenchyma cells also helps the plant to cope with dust pollution (De Micco et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study was conducted on riparian vegetation in industrial contaminated area of Faisalabad and Most of the morphoanatomical parameters notably attained a decrease in \u003cem\u003eE. alba\u003c/em\u003e (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePresent project was planned to explore the flora of the area, which gave knowledge about the plants present in the area which can be used in different ways to benefit humanity. Second purpose was to explore the morpho-anatomical, biochemical and physiological modifications along with heavy metal analysis in the commonly growing plants in the area. The aim was to investigate the effects and plant adaptations and their mechanism of survival under severe dust pollution caused by stone crushing industry of Kirana hills.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSargodha Hills\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor this purpose extensive survey of the area was conducted during which to study sites were selected. For exploring seasonal variations in the plant community structure and the factors responsible for these variations dust, soil (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and vegetation data was collect from all study sites during all seasons. Geographical aspects of the sites selected were recorded. Plant community structure in the sites selected was observed.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetal analysis of soil at Kirana Hills Sargodha, Dust polluted area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSite 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSite 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDust (g/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZn (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePb (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCd (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNi (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eJanuary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSite 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSite 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDust (g/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZn (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePb (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCd (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNi (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eApril\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSite 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSite 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDust (g/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZn (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePb (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCd (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNi (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eJuly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSite 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSite 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDust (g/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZn (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePb (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCd (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNi (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSelection of study sites\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter conducting a thorough survey, five environmentally varied study locations were chosen. Site selection was based on differences in environmental factors, primarily dust pollution (Ahmad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSite 1\u003c/strong\u003e \u003cp\u003eIt was situated at Sargodha bypass 18 km away from Sargodha City. It was totally dust free site with no stone crushers.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eS\u003cb\u003eite 2\u003c/b\u003e: It was located in Shaheenabad, 12 km from Sargodha City. It was a low dust site because it was located some distance from active stone crushers.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSite 3\u003c/strong\u003e \u003cp\u003eIt was situated near Shaheenabad 15 km away from Sargodha City. It was extremely dust polluted site, surrounded by large number of active stone crushing units.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSite 4\u003c/strong\u003e \u003cp\u003eIt was situated near Pull 111, 22 km away from Sargodha City. It was highly dust polluted site, due to high stone crushing activity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSite 5\u003c/strong\u003e \u003cp\u003eIt was situated near 46 adda 27 km away from Sargodha City. It was moderately dust polluted site, because it was situated at some distance from stone crushers.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSampling Seasons\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData for dust, soil (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and vegetation was recorded in triplicate from all the selected study sites throughout the year at regular interval of three months to include each season.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eFloristic composition of Kirana Hills\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor this reason, Kirana Hills was frequently visited throughout the year, and a thorough floristic catalogue of the area's vegetation was created. Plants were collected, dried, preserved, and identified by comparing them to the Pakistani flora (Nasir and Ali, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) before being deposited in the University of Sargodha's herbarium.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEcological analysis of vegetation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOn each study site 10 regular quadrates (each of 01m\u003csup\u003e2\u003c/sup\u003e) were randomly laid. Density, frequency and coverage of vegetation were calculated by the method of (Ludwig \u003cem\u003eet al.\u003c/em\u003e, 1988).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFrequency % = Number of quadrates in which a species occurred/Total number of quadrates X 100\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDensity % = Number of plants of a species in a quadrate/Number of plants of all species in a quadrate x 100\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCoverage % = Total area covered by a species in a quadrate/Total area covered by all the species in a quadrate X 100\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRelative Frequency % = Frequency of a species/Total frequency of all the species X 100\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRelative Density % = Density of a species in/Total density of all the species X 100\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRelative Coverage % = Coverage of a species/Total coverage for all the species X 100\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImportance value\u0026thinsp;=\u0026thinsp;Relative Coverage\u0026thinsp;+\u0026thinsp;Relative Density\u0026thinsp;+\u0026thinsp;Relative Frequency\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSelection of plants for study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe current study chose four readily available, commonly growing local plants that can be found at all study sites throughout the year. Morpho-anatomical characteristics of these plants were examined for comparative purposes. The effects of dust pollution on the morpho-anatomical characteristics of chosen plants were investigated, as well as the mechanisms of their survival.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMorphological parameters\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFollowing morphological parameters were measured, Leaf length (cm), Leaf width (cm), Leaf thickness (mm), Leaf area (cm\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAnatomical Parameters\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTriplicate samples were taken to conduct anatomical studies on the leaf, stem, and root. A 1cm section of the leaf center was cut for anatomical research. For the stem anatomical research, a 1cm piece from the central node of the greatest stem was obtained, and for the root anatomical study, a 1 cm base of the thickest root was taken. All samples were maintained in a 70% ethyl alcohol solution. Then, free hand section cutting and staining were performed using the method of (Ruzin, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). After staining, portions were cleaned with xylene solution and mounted on slides with Canada balsam already pasted. Finally, the slices were coated with a cover slip, inspected using a compound microscope, and photographed.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological Parameters\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eLeaf chlorophyll content (Chl.\u003c/b\u003e \u003cb\u003ea\u003c/b\u003e, \u003cb\u003eb\u003c/b\u003e, \u003cb\u003etotal chlorophyll) and carotenoids\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e, total chlorophyll content, and carotenoids were determined using the method of Arnon (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1949\u003c/span\u003e). For chlorophyll extraction, fresh leaf material (0.1 g) was collected and stored overnight in 80% acetone at 4\u0026deg;C; the extract was centrifuged at 10,000 x g for 5 minutes. The absorbance of the supernatant was measured at 663nm, 645nm, and 480nm using a spectrophotometer (Hitachi-U2001; Tokyo, Japan).\u003c/p\u003e \u003cp\u003eThe chlorophyll \u003cem\u003ea\u003c/em\u003e and chlorophyll \u003cem\u003eb\u003c/em\u003e were calculated by the following formula:\u003c/p\u003e \u003cp\u003e \u003cb\u003eChlorophyll\u003c/b\u003e \u003cb\u003ea\u003c/b\u003e (mg/g F. wt)= [12.7 (OD 663) -2.69 (OD 645)] x V/1000 x W]\u003c/p\u003e \u003cp\u003e \u003cb\u003eChlorophyll\u003c/b\u003e \u003cb\u003eb\u003c/b\u003e (mg/g F. wt)= [22.9 (OD 645) -4.68 (OD 663)] x V/1000 x W]\u003c/p\u003e \u003cp\u003e \u003cb\u003eTotal Chlorophyll\u003c/b\u003e chl, \u003cem\u003ea\u003c/em\u003e\u0026thinsp;+\u0026thinsp;chl. \u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eCarotenoids\u003c/b\u003e (mg/g f.wt.)\u0026thinsp;=\u0026thinsp;Acar/ Em \u0026times;100\u003c/p\u003e \u003cp\u003eV\u0026thinsp;=\u0026thinsp;volume of the extract (ml)\u003c/p\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;weight of the fresh leaf material (g)\u003c/p\u003e \u003cp\u003eAcar\u0026thinsp;=\u0026thinsp;OD 480\u0026thinsp;+\u0026thinsp;0.114(OD 663)-0.638(OD 645)\u003c/p\u003e \u003cp\u003eEm\u0026thinsp;=\u0026thinsp;2500\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLeaf relative water contents (LRWC):\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor calculation, the LRWC method of (Barr and Weatherley, 1962) was employed. Fresh weight of completely formed leaf samples was measured. The leaf samples were then soaked in distilled water for 8 hours before being weighed. At the end all samples were oven-dried at 70\u0026deg;C and their dry weight was recorded. Finally LRWC was calculated using the following equation:\u003c/p\u003e \u003cp\u003eLRWC (%)\u0026thinsp;=\u0026thinsp;Leaf fresh weight - Leaf dry weight/Leaf turgid weight - Leaf dry weight x 100\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) concentration [Reactive oxygen species (ROS)]\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHydrogen peroxide content was determined by following the method of (Velikova et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Leaf samples were homogenized in ice bath with 1ml of 0.1% (w/v) trichloroacetic acid (TCA). Then the homogenate was centrifuged at 12000g for 15 minutes. 0.1ml of the supernatant was then mixed with 10mM potassium phosphate buffer (pH 7.0) and 1M potassium iodide. Absorbance of mixture was read at 390nm using spectrophotometer. H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content was calculated by comparing with a standard calibration curve prepared by using different concentrations of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePeroxidase activity (Enzymatic antioxidant)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEnzyme extraction:\u003c/p\u003e \u003cp\u003eFresh leaf sample (0.5g) was grinded in 5ml of 50m\u003cem\u003eM\u003c/em\u003e cooled potassium phosphate buffer (pH 7.8). Grinded material was then filtered. Than homogenate was centrifuged at 15000g for 20 minutes at 4\u0026deg;C and supernatant was used for the enzyme assay.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePeroxidase (POD) activity\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e(Chance and Maehly, 1955) guaiacol oxidation method was used to for this purpose. The final volume of the reaction mixture for POD (3ml) contained 50m\u003cem\u003eM\u003c/em\u003e phosphate buffer (pH 7.0), 20m\u003cem\u003eM\u003c/em\u003e guaiacol, 40m\u003cem\u003eM\u003c/em\u003e H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, and 0.1ml enzyme extract. Changes in absorbance of the reaction solution at 470 nm were read with the interval of 20s. One unit POD activity was defined as the change of 0.01 absorbance unit per min per mg of protein.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data obtained after dust and soil analysis along with ecological, morpho-anatomical, physiological, biochemical and heavy metal analysis of vegetation was subjected to Canonical Correspondence Analysis (CCA) using conoco software version 4.5. The data were also subjected to a two-way ANOVA using the MS Excel tool.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFloral density and relative density\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCCA triplots of density and relative density at regional scale showed the distinctive distribution of environmental and additional variables as well as species data. Soil attributes and heavy metals in dust showed variation at each site (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Dust concentration was highly associated with Site 3 and Site 4. Plant density was higher at Site 1 and lowest at Site 3.\u003c/p\u003e \u003cp\u003eOctober depicts fall in the area. During October, CCA triplots of density and relative density revealed that in soil characteristics, EC was strongly related with Site 3, phosphorus, organic matter, and pH were plotted alongside Site 2, and potassium was positively associated with Site 1. In dust heavy metals nickel and lead were associated with Site 3 and Site 4 while cadmium, iron and zinc were plotted towards Site 1 and Site 2. Dust concentration was highly associated with Site 3 and Site 4. The density and relative density of most plants were positively associated with Sites 1 and 2, but negatively associated with Site 3. The density of plants decreased in the autumn. The density and relative density of \u003cem\u003eLathyrus sativus, Mullogo cerviana, Capparis deciduas, Tetrapogon villosus, Haloxylon recurvum, Dactylotenium scindicum, Croton capitatus, Echinochloa colona, Enneapogon persicus, Stipagrostis hirtigluma\u003c/em\u003e and \u003cem\u003eVachellia nilotica\u003c/em\u003e were associated with Site 1, \u003cem\u003eSalvadora oleoides\u003c/em\u003e and \u003cem\u003eCroton bonplandianusa\u003c/em\u003e were associated with Site 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eJanuary indicates winter in the area. During January, CCA triplots of density and relative density revealed that all environmental and supplemental factors were essentially identically associated with sites as in October, with a few outliers. Organic matter in soil variables was associated with both Sites 1 and 2. In dust, heavy metal zinc was associated with Site 1, whereas cadmium was associated with Site 5. Dust concentration was strongly linked with Site 3. Plant density was reduced during January. Density and relative density of plants showed same association as in October and most of the plants were associated with Site 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eApril is the month that depicts spring in this area. Plant density increased in April. The density and relative density of most plants were associated with Site 1. The density and relative density of the majority of plants were connected with Site 1, \u003cem\u003eSalvadora oleoides\u003c/em\u003e and \u003cem\u003eCroton bonplandianusa\u003c/em\u003e with Site 2, and \u003cem\u003eOxalis corniculata\u003c/em\u003e and \u003cem\u003eEchinops echinatus\u003c/em\u003e with Site 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eJuly represents summer in the area. During July CCA triplots of density and relative density showed that soil variables except pH were associated with Site 4 and Site 2, pH was again associated with Site 3. In dust heavy metals iron, zinc and cadmium were associated with Site 1, lead and nickel was associated with Site 3 and also Site 4 again. Dust concentration continued to be higher at Site 3. Plant density dropped in July compared to April. The density and relative density of most plants were associated with Site 1. Plant density was similarly higher at Sites 2 and 5, but declined at Sites 3 and 4. The density and relative density were high at Site 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eCCA triplots of density and relative density at the temporal scale revealed more detailed distributions of environmental and supplemental factors, as well as species data. All soil factors except pH were connected with January and October, whereas pH was associated with April and July. All heavy metals found in dust were related with January. Dust concentration was also associated with January. Plant density peaked in April and fell to its lowest in January.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFloral frequency and relative frequency\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCCA triplots of frequency and relative frequency at the spatial scale indicated the specific distribution of environmental and supplemental variables, as well as species data. Soil characteristics and heavy metal levels in dust varied between sites. Dust concentration was strongly linked with Site 3. The species frequency was highest at Site 1 and lowest at Site 3.\u003c/p\u003e \u003cp\u003eDuring October, CCA triplots of frequency and relative frequency revealed that in soil parameters, EC was strongly linked with Site 3, phosphorus with Site 2, and organic matter, pH, and potassium were not connected with any site. Heavy metals nickel and lead were found in dust at Sites 4 and 3, respectively, while cadmium was found at Sites 1 and 5, although iron and zinc were not. Dust concentrations were once again strongly related with Site 3. Most plants' frequency and relative frequency were positively correlated with Site 1, but negatively correlated with Site 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eCCA triplots of frequency and relative frequency showed that in soil parameters potassium was plotted alongside Site 1, EC was associated with Site 3, pH was positively associated with Site 2 while organic matter and phosphorus were not associated with any site. In dust heavy metals nickel and lead were associated with Site 3 and 4; cadmium was plotted towards Site 1 while iron and zinc were again not associated with any site. Dust concentration was associated with Site 3 and Site 4. Plant Frequency was decreased further during January. Frequency and relative frequency of most plants was in positive association with Site 1 while less frequent on Site 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eDuring April CCA triplots of frequency and relative frequency showed that in soil parameters phosphorus, organic matter and potassium were plotted alongside Site 1 while EC was associated with Site 4 and pH was positively associated with Site 3. In dust heavy metals nickel and lead were associated with Site 4 and Site 3 respectively, cadmium was plotted towards Site 1 while iron and zinc were again not associated with any site. Dust concentration was again highly associated with Site 3. Plant frequency was increased during spring season. Frequency and relative frequency of most plants was in positive association with Site 1 while minimum on Site 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eDuring July, CCA triplots of frequency and relative frequency revealed that all soil parameters except pH were related with Site 4, but pH was positively associated with Site 1. Heavy metals iron and cadmium were positively connected with Site 1, lead was plotted towards Site 3, nickel with Site 4, and zinc was not associated with any site. Dust concentrations were once again strongly related with Site 3. Plant frequency reduced during the hot summer of July compared to April (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eCCA triplots of frequency and relative frequency at temporal scale presented specific distribution of environmental and supplementary variables and species data. Soil variables except pH and EC were associated with January and October while pH was associated with April and July. Mostly all heavy metals in dust were associated with January. Dust concentration was also associated with January. Species frequency was highest in April and lowest in January.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFloral cover and relative cover\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCCA triplots of cover and relative cover at spatial scale represented specific distribution of environmental and supplementary variables and species data. Soil attributes and heavy metals showed variation at each site. Dust was associated with Site 3 and Site 4. Species cover was highest on Site 1 and the lowest on Site 3.\u003c/p\u003e \u003cp\u003eDuring October CCA triplots of cover and relative cover showed that all soil parameters except EC were not associated with any site while EC was associated with Site 3. The cover and relative cover of \u003cem\u003eLathyrus sativus, Mullogo cerviana, Capparis deciduas, Tetrapogon villosus, Haloxylon recurvum, Dactylotenium scindicum, Croton capitatus, Echinochloa colona, Enneapogon persicus, Stipagrostis hirtigluma\u003c/em\u003e and \u003cem\u003eVachellia nilotica\u003c/em\u003e were associated with Site 1, \u003cem\u003eSalvadora oleoides\u003c/em\u003e and \u003cem\u003eCroton bonplandianusa\u003c/em\u003e were associated with Site 2, while \u003cem\u003eOxalis corniculata\u003c/em\u003e and \u003cem\u003eEchinops echinatus\u003c/em\u003e were found to be associated with Site 5. No plants were found in direct association with Site 3 and Site 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eIn January, CCA triplots of cover and relative cover revealed that soil characteristics Site 1 was connected with potassium, Site 2 with pH, Site 3 with EC, and no site was associated with organic matter or phosphorus. Nickel and lead were found in dust at Sites 3 and 4, respectively, while cadmium was found at Site 1, but iron and zinc were not. Site 3 had a higher concentration of dust. Plant cover reduced much further in January. Most plants' cover and relative cover were positively correlated with Site 1, but negatively correlated with Sites 3, and 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe Cover and relative cover of \u003cem\u003eIndigofera atropurpurea, Arundo donax\u003c/em\u003e, \u003cem\u003eLathyrus sativus, Mullogo cerviana, Capparis deciduas, Tetrapogon villosus, Haloxylon recurvum, Dactylotenium scindicum, Croton capitatus, Echinochloa colona, Enneapogon persicus, Stipagrostis hirtigluma, Vachellia nilotica\u003c/em\u003e and \u003cem\u003eLaunaea procumbens\u003c/em\u003e were associated with Site 1, \u003cem\u003eSalvadora oleoides\u003c/em\u003e and \u003cem\u003eCroton bonplandianusa\u003c/em\u003e were associated with Site 2, \u003cem\u003eSaccharum bengalense\u003c/em\u003e was associated with Site 3, \u003cem\u003eSalsola imbricata\u003c/em\u003e was associated with Site 4 while \u003cem\u003eOxalis corniculata\u003c/em\u003e and \u003cem\u003eEchinops echinatus\u003c/em\u003e were found to be associated with Site 5. \u003cem\u003eGrewia villosa, Cynodon dactylon, Salsola fruticosa Aeluropus lagopoides, Sida ovate, Heliotropium strigosum\u003c/em\u003e and \u003cem\u003eCenchrus ciliaris\u003c/em\u003e were associated with all sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eDuring July CCA triplots of cover and relative cover showed that all soil parameters except pH were associated with Site 4 while pH was not associated with any site. In dust heavy metals nickel and lead were associated with Site 3 and Site 4, cadmium was associated with Site 1 while iron and zinc were not associated with any site. Dust concentration was again associated with Site 3. Plant cover decreased during July compared to April. Cover and relative cover of most plants was in positive association with Site 1 while in negative correlation with Site 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eCCA triplots of cover and relative cover at temporal scale presented specific distribution of environmental and supplementary variables and species data. Soil attributes showed variation with each season. Mostly all heavy metals were associated with January. Dust concentration was associated with October and January. Species cover was highest in April and lowest in January.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eANOVA showing morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA variance analysis of Fagonia indica leaf morpho-anatomical attributes revealed that variation in all leaf parameters (leaf area, leaf length, leaf width, leaf thickness, leaf upper and lower epidermis cell area, leaf metaxylem cell area, leaf mesophyll cell area, and leaf sclerenchyma thickness) was highly significant at the spatial scale among all sites. Except for leaf area, leaf mesophyll cell area, and leaf upper epidermis cell area, there were substantial temporal fluctuations across all leaf characteristics throughout all seasons. Leaf area showed significant while leaf mesophyll cell area and leaf upper epidermis significant and highly significant variation.\u003c/p\u003e \u003cp\u003eAnalysis of variance showing root morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e highlighted at spatial scale among all sites variation in all root parameters (root radius, root aerenchyma cell area, root epidermal cell area, root metaxylem cell area, root endodermis thickness, root parenchyma cell area, root radius, root sclerenchyma thickness and root vascular bundle thickness) was very highly significant. At temporal scale in all seasons variation among most root parameters was very highly significant except for root epidermal cell area, root endodermis thickness, root parenchyma cell area and root sclerenchyma thickness which showed not very highly significant but highly significant variation.\u003c/p\u003e \u003cp\u003eVariation in all stem parameters (stem epidermal cell area, stem metaxylem cell area, stem parenchyma cell area, stem radius, stem sclerenchyma thickness, and stem vascular bundle thickness) was highly significant at the spatial scale across all sites, according to a variance analysis of \u003cem\u003eFagonia indica\u003c/em\u003e stem morpho-anatomical attributes. At the temporal scale in all seasons, variation among all stem parameters was similarly very highly significant, except for stem parenchyma cell area and stem vascular bundle thickness, which exhibited not very highly significant but very significant variation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of ANOVA (\u003cem\u003eF\u003c/em\u003e ratios) for morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.54545***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.63636*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.96317***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.93315***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLLE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.16766***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.52695***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.94188***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.3234***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.74151***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.251572**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.38462***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.15385***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166.3714***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.71429***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLUE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.79245***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.912253**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.20388***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.7767***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.05998***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.02999***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.71792***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.965005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.89905***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.35695***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.66667***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.333333**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.20328***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.906683**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173.2424***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.53535***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.36364***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.636364**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158.1818***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.36364***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.72028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.01632***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e656.7692***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.6154***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.08293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.63528**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117.4669***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.07393***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.69565***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.91304***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e178.2447***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.623729**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003edf: Sites 4, Season 3, Error 12, * = significant variation, **= highly significant variation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e***= very highly significant variation, ns\u0026thinsp;=\u0026thinsp;non significant variation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eBar graphs showing morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBar graphs depicting stem morpho-anatomical features of \u003cem\u003eFagonia indica\u003c/em\u003e revealed that at a spatial scale, all stem parameters dropped at heavily dust contaminated Site 3, with the exception of stem sclerenchyma thickness, which grew significantly. All other stem anatomical characteristics rose significantly at dust-free Site 1, with the exception of stem sclerenchyma thickness, which fell dramatically. At the temporal scale, all other stem anatomical characteristics dropped during the peak winter season in January, with the exception of stem sclerenchyma thickness, which grew significantly in January. All other stem anatomical characteristics grew significantly throughout the spring season in April, with the exception of stem sclerenchyma thickness, which fell dramatically (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). The stem anatomical variations at each sites during different seasons indicates the clear differences in stem structures of study ecotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBar graphs showing root morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e highlighted at spatial scale all root anatomical parameters decreased at extremely dust polluted Site 3 except root aerenchyma cell area and root sclerenchyma thickness which highly increased at Site 3. Values of all other root anatomical parameters were recorded highest at dust free Site 1 except root aerenchyma cell area and root sclerenchyma thickness which highly decreased at Site 1. At temporal scale values of all other root anatomical parameters were recorded highest during spring season in April except root aerenchyma cell area and root sclerenchyma thickness which highly decreased in April. All other root anatomical parameters decreased during peak winter season in January except root aerenchyma cell area and root sclerenchyma thickness which highly increased in January (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH). The root anatomical variations at each sites during different seasons indicates the clear differences in root structures of study ecotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBar graphs showing leaf morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e highlighted at spatial scale among all leaf morpho-anatomical parameters decreased at extremely dust polluted Site 3 except leaf sclerenchyma thickness which highly increased at Site 3. All other leaf morpho-anatomical parameters highly increased at dust free Site 1 except leaf sclerenchyma thickness which highly decreased at Site 1. At temporal scale, content of all leaf morpho-anatomical parameters decreased during peak winter season in January except leaf sclerenchyma thickness which highly increased in January. All other leaf morpho-anatomical parameters highly increased during spring season in April except leaf sclerenchyma thickness which highly decreased in April (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eI). The leaf anatomical variations at each sites during different seasons indicates the clear differences in leaf structures of study ecotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCCA triplots showing morpho-anatomical attributes of\u003c/b\u003e \u003cb\u003eFagonia indica\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCCA triplots depicting morpho-anatomical characteristics of \u003cem\u003eFagonia indica\u003c/em\u003e at a geographical scale revealed a particular distribution of environmental and additional factors. Soil characteristics and heavy metal levels in dust varied between sites. Dust concentrations were strongly related with Sites 3 and 4. Leaf, stem, and root sclerenchyma, as well as root aerenchyma, were connected with Sites 3 and 4, respectively, whereas all other characteristics were linked to Site 1. No parameter was directly related to Site 5.\u003c/p\u003e \u003cp\u003eCCA triplots showing morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e presented that in soil parameters potassium and organic matter were associated with Site 1, phosphorus and EC were associated with Site 3 while pH was associated with Site 2. In dust heavy metals nickel and lead were associated with Site 4 and Site 5, cadmium was associated with Site 1 while iron and zinc were not associated with any particular site (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eJanuary CCA triplots showing morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e presented that in soil parameters potassium, organic matter and phosphorus were associated with Site 1 and Site 2, EC was associated with Site 3 while pH was associated with Site 2. In dust heavy metals nickel and lead were associated with Site 4, cadmium was associated with Site 1 and Site 2 while iron and zinc were not associated with any particular site. Dust concentration was associated with Site 3 and 4. Leaf and root sclerenchyma were associated with Site 3. Stem sclerenchyma and root aerenchyma were associated with Site 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn dust heavy metals nickel and lead were associated with Site 4, cadmium was associated with Site 1 and Site 2 while iron and zinc were not associated with any particular site. Dust concentration was associated with Site 3. Leaf, stem and root sclerenchyma were associated with Site 3. Stem and root vascular bundle thickness was associated with Site 4. Root and stem metaxylem area, leaf length, leaf area and root parenchyma were positively associated with Site 2 while all other parameters were associated with Site 1. No parameter was found in direct association with Site 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eRoot sclerenchyma was associated with Site 3. Stem and root radius, stem and root sclerechyma, and root aerenchyma were associated with Site 4. Root and stem metaxylem area, leaf length, leaf area and root parenchyma were positively associated with Site 2 while all other parameters were associated with Site 1. No parameter was found in direct association with Site 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eCCA triplots showing morpho-anatomical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e at temporal scale highlighted specific distribution of environmental and supplementary variables. All soil variables except pH were associated with January and October while pH was associated with April and July. All heavy metals in dust were associated with January and October. Dust concentration was also associated with January and October. Leaf, stem and root sclerenchyma were associated with January, root aerenchyma was associated with October, while all other parameters were associated with April and July respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCCA triplots showing physiological attributes along with heavy metals in\u003c/b\u003e \u003cb\u003eFagonia indica\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCCA triplots of physiological and biochemical parameters, as well as heavy metals, in Fagonia indica at different geographical scales revealed a particular distribution of ambient and additional factors. Soil attributes and heavy metals in dust showed variation at each site. Dust concentration was highly associated with Site 3 and Site 4. Leaf chlorophyll \u003cem\u003ea\u003c/em\u003e, leaf chlorophyll \u003cem\u003eb\u003c/em\u003e, and leaf relative water content were associated with Site 1 while hydrogen peroxide was associated with Site 3 and Site 4. In root and shoot heavy metals zinc and iron were not associated with any particular site while lead, nickel and cadmium were associated with Site 3 and Site 4.\u003c/p\u003e \u003cp\u003eCCA triplots of physiological and biochemical parameters, as well as heavy metals in \u003cem\u003eFagonia indica\u003c/em\u003e, revealed a particular distribution of ambient and additional factors on a time scale. Soil attributes showed variation in each season. Heavy metals in dust were associated with October and January. Dust concentration was highly associated with October and January. Leaf chlorophyll \u003cem\u003ea\u003c/em\u003e and chlorophyll \u003cem\u003eb\u003c/em\u003e, and leaf relative water content were associated with April and July while peroxidase (POD), and hydrogen peroxide were associated with October and January. In root and shoot all heavy metals were associated with October and January (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cb\u003eANOVA showing physiological and biochemical attributes in\u003c/b\u003e \u003cb\u003eFagonia indica\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAn analysis of variance of physiological properties of \u003cem\u003eFagonia indica\u003c/em\u003e revealed that at the geographic scale, variation in all physiological parameters (chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e, total chlorophyll, and leaf relative water content) was quite significant. At the temporal scale, most physiological indicators varied significantly throughout all seasons, with the exception of chlorophyll \u003cem\u003ea\u003c/em\u003e and leaf relative water content, which varied little but significantly. Analysis of variance displaying biochemical properties of \u003cem\u003eFagonia indica\u003c/em\u003e revealed that at geographical scale among all sites variation in all biochemical attributes (hydrogen peroxide, peroxidase) was quite significant. At temporal scale in all seasons variation among all biochemical parameters was also very highly significant.\u003c/p\u003e \u003cp\u003eAnalysis of variance showing heavy metals in \u003cem\u003eFagonia indica\u003c/em\u003e highlighted that at spatial scale among all sites variation in concentration of all heavy metals (cadmium, iron, nickel, lead and zinc) in root Fand stem was very highly significant. At temporal scale in all seasons variation among all heavy metals in root and stem was also very highly significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of ANOVA (\u003cem\u003eF\u003c/em\u003e ratios) for physiological and biochemical attributes along with heavy metals in \u003cem\u003eFagonia indica\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChl\u003c/b\u003e \u003cb\u003ea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.67547***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.213566**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChl\u003c/b\u003e \u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e377.4299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.7656***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT Chl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125.3072***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.72954***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125.979***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.30677***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLRWC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176.3699***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.30647**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH2O2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e327.621***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.86912***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePOD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.57717***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.03477**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e437.6077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.46053***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.66406***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.79379***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRCd\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.92323***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.60736***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e338.4718***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.34413***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRNi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e989.9669***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.3724***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRPb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.14751***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.73137***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRZn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e363.4377***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.61378***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCd\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231.3588***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.59681***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240.1655***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.00392***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1088.401***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157.071***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201.5119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.66209***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSZn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e411.9715***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.5304***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003edf: Sites 4, Season 3, Error 12, * = significant variation, **= highly significant variation, ***= very highly significant variation, ns\u0026thinsp;=\u0026thinsp;non significant variation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBar graphs showing physiological and biochemical attributes in\u003c/b\u003e \u003cb\u003eFagonia indica\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBar graphs of physiological attributes of \u003cem\u003eFagonia indica\u003c/em\u003e revealed that at the spatial scale, all physiological parameters (chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e, total chlorophyll, carotenoids, and leaf relative water content) decreased at extremely dust polluted Site 3, while their values were highest at dust-free Site 1. On a time scale, the content of all physiological markers declined during the peak winter season in January, while their levels peaked in April.\u003c/p\u003e \u003cp\u003eBar graphs of physiological attributes of \u003cem\u003eFagonia indica\u003c/em\u003e highlighted that at spatial scale all physiological parameters (chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e, total chlorophyll, carotenoids and leaf relative water content) decreased at extremely dust polluted Site 3 while their value was recorded highest at dust free Site 1. At temporal scale, content of all physiological parameters decreased during peak winter season in January while their values was recorded highest during spring season in April.\u003c/p\u003e \u003cp\u003eBar graphs showing biochemical attributes of \u003cem\u003eFagonia indica\u003c/em\u003e highlighted that at spatial scale all biochemical attributes (hydrogen peroxide) were lower in value at dust free Site 1 while their maximum values were recorded at extremely dust polluted Site 3. At temporal scale, all biochemical attributes increased during peak winter season in January while their values was recorded lowest during spring season in April. The RD29 was recorded high at the dust polluted sites as compared to dust free sites and the metabolic rate was decreased at sites near to the dust source (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eA-\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003eBar graphs showing heavy metals in root and shoot of \u003cem\u003eFagonia indica\u003c/em\u003e highlighted that at spatial scale concentration of all heavy metals (cadmium, iron, nickel, lead and zinc) in root and stem was higher at extremely dust polluted Site 3 while their lowest concentration was recorded at dust free Site 1. At temporal scale concentration of all heavy metals was recorded lowest during spring season in April while their concentration was on the higher side during peak winter season in January (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003eA-\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSoil and dust analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOn temporal scale almost all soil parameters were higher in concentration in winter and autumn and their concentration was relatively lower in spring and summer. It may be due to the changes in soil moisture, amount of rainfall and temperature which are minimum in winter and maximum in summer as revealed by (Alavi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) or it may be due to amount of dust-fall which is maximum in winter and minimum in summer, dust carry heavy metals and deposit them on soil according to the study of (Sun \u003cem\u003eet al.\u003c/em\u003e, 2020) on dust pollution. On temporal scale dust in maximum concentration was recorded in winter followed by autumn, higher dust concentration may be due to low rainfall, high humidity, low temperature, cool wind and dust storms as revealed by (Khusfi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Minimum dust concentration was recorded in spring followed by summer, lower dust concentration may be due high rainfall, low humidity and high temperature as studied by (Behrouzi \u003cem\u003eet al.\u003c/em\u003e, 2019; Khusfi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in their study on seasonal variations in dust pollution.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eEcological analysis of vegetation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOn spatial scale some plants i.e. \u003cem\u003eAristida mutabilis, Aerva javanica, Cenchrus ciliaris, Cynodon dactylon Fagonia indica\u003c/em\u003e and \u003cem\u003eSalsola imbricata\u003c/em\u003e were found on all selected study sites, it may be due to their dust tolerance abilities as revealed by (Roy et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in their study on dust pollution tolerance in certain tree species that certain plants can tolerate dust pollution. Plants also have survival mechanisms limited supply of soil nutrients as studied by (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in their study on stress tolerance in plants due to shortage of soil nutrients.\u003c/p\u003e \u003cp\u003eAt the temporal scale, CCA triplots showed that the density, frequency, cover, and important value of vegetation were highest in spring, followed by summer. It may be due to lower dust concentration in spring and summer which supported plant growth as revealed by (Prajapati and Tripathi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and high soil moisture in July followed by spring which favours vegetation growth as revealed by the study of (Bhatt et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) on impact of seasonal rainfall on vegetation dynamics.\u003c/p\u003e \u003cp\u003eOn temporal scale some plants i.e. \u003cem\u003eAristida mutabilis, Aerva javanica, Cenchrus ciliaris, Cynodon dactylon Fagonia indica\u003c/em\u003e and \u003cem\u003eSalsola imbricata\u003c/em\u003e were found in all seasons. It may be due to their abilities for seasonal environmental dust tolerance as studied by (Roy et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in their study on dust tolerance abilities of certain tress, their dust tolerance mechanisms in accordance with the study of (Rai, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn temporal scale some plants i.e. \u003cem\u003eAristida mutabilis, Aerva javanica, Cenchrus ciliaris, Cynodon dactylon Fagonia indica\u003c/em\u003e and \u003cem\u003eSalsola imbricata\u003c/em\u003e were found all seasons, it may be due to their dust tolerance abilities in high dust concentration in winters as studied by (Roy et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in their study on seasonal air pollution tolerance abilities of certain trees.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eMorpho-anatomical analysis of vegetation\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLeaf sclerification increased at extreme and high dust polluted sites, it may be adaptive response of local plants to the abiotic stress induced by dust pollution of stone crushing industry in accordance with the study of (Ivanescu and Gostin, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; \u0026Ouml;zt\u0026uuml;rk et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) on plant pollutants. Stem sclerification increased at extreme and high dust polluted sites, it may be an adaptive response of local plants to the abiotic stress induced by dust pollution of stone crushing industry as supported by the studies of (Ivanescu and Gostin, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; \u0026Ouml;zt\u0026uuml;rk et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) on cytological changes in plants in response to air pollution.\u003c/p\u003e\u003cp\u003eRoot sclerification increased at extreme and high dust polluted sites, it may be adaptive response of local plants to the abiotic stress induced by dust pollution of stone crushing industry in accordance with the study of (Ivanescu and Gostin, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; \u0026Ouml;zt\u0026uuml;rk et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) plant adaptations to dust pollution. At temporal scale all leaf, root and stem morpho-anatomical attributes increased in spring followed by summer except leaf, stem and root sclerification along with root arenchyma formation which decreased in spring and summer. It may be due to the lower dust concentration in spring and summer as supported by the study of (Prajapati and Tripathi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn winter and fall, all leaf, root, and stem morpho-anatomical qualities declined, with the exception of leaf, stem, and root sclerification and root arenchyma development, which rose. It may be due to the higher dust concentration in winter and autumn which negatively effects vegetation growth as revealed by (Kameswaran et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All selected plants survived in all seasons especially in cold winters with high suspended dust, it may be due to their morpho-anatomical modifications i.e. presence of large parenchyma cells and leaf, root and stem sclerification as revealed by the findings of (\u0026Ouml;zt\u0026uuml;rk et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and root aerenchyma formation as described by (Naseer et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in their study on plant morpho-anatomical adaptations in response to dust pollution.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003ePhysiological, biochemical and heavy metal analysis of vegetation\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDust from stone crushing industries negatively affect the plant physiological and biochemical attributes i.e. decreased photosynthetic pigments e.g. chlorophyll and carotenoids as in accordance with the results of the study conducted by (Sharma et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on impact of dust pollution on two selected plant species, decreased leaf relative water content as revealed by the study of (Jabeen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conducted to find out dust tolerance abilities of some selected plants, production of reactive oxygen species (ROS) e.g. hydrogen peroxide as mentioned by (Dhir, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in his study conducted on effects of dust pollution on plants. Plant survive in harsh dust polluted conditions physiologically and biochemically by producing enzymatic antioxidants e.g. peroxidase as described by (Siqueira-Silva et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in their study on impact of dust pollution on \u003cem\u003eCedrela fissilis\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn present study at spatial scale physiological parameters of all selected plants i.e. leaf chlorophyll \u003cem\u003ea\u003c/em\u003e, chlorophyll \u003cem\u003eb\u003c/em\u003e and carotenoids, leaf relative water showed significant variation in response to dust pollution. All these parameters were highest in concentration at dust free site followed by low dust site and intermediate at moderate dust site. Higher values of physiological attributes may be because of no dust pollution caused by stone crushing industry due to the absence of stone crushers on these sites as revealed by (Iqbal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Padhy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) that dust pollution severely effect plant physiological attributes, lower heavy metals concentration in soil and air because of no industrial effluents so reduced effect of heavy metals on physiology of selected plants as described by (Jaiswal et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll the above physiological attributes were lower at high dust site and extreme dust site. This decrease in these physiological parameters may be due to high dust pollution caused by active stone crushers at these sites as supported by the study of (Padhy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) that dust pollution strongly effects plant physiological attributes, higher heavy metals concentration in soil and air because of higher dust pollution of stone crushing industry which increase the impact of heavy metals on plant physiology as revealed by (Asati et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Padhy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll the above biochemical attributes were increased at extreme dust site and high dust site. The increase in these biochemical parameters may be due to high dust pollution caused by active stone crushes at these sites which cause abiotic stress and reactive oxygen species (ROS) are produced in response to which plants produce enzymatic and non-enzymatic antioxidants along with osmoprotactants to survive in such harsh environment (Padhy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), lower concentration of soil organic matter also cause abiotic stress to plants as revealed by (Stefanowicz et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIncrease in plant enzymatic antioxidants and non-enzymatic e.g. peroxidase and ascorbic acid may be due to their role in scavenging over-produced ROS through different ways as supported by the study of (Chaudhary and Rathore, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Siqueira-Silva et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Heavy metals (Cd, Fe, Ni, Zn, Pb) in high concentrations can be toxic to plants and can decrease plant growth and yield as revealed by (Ryzhenko \u003cem\u003eet al.\u003c/em\u003e, 2018) in their study on heavy metal toxicity.\u003c/p\u003e\u003cp\u003eHeavy metal concentration i.e. Cd, Fe, Ni, Zn, Pb in root and shoot of selected plants varied in response to dust pollution. All heavy metals were lowest in concentration at dust free site followed by low dust site and moderate dust site, lower concentration of heavy metals may be due to the absence of stone crushers on these sites as revealed by (Iqbal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Padhy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in their study that dust pollution from stone crushing industry carry heavy metals, or may be due to lower heavy metals concentration in soil because of no industrial effluents at these sites as in accordance with study of (Jaiswal et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) on heavy metals.\u003c/p\u003e\u003cp\u003eConcentration of all above heavy metals in root and shoot of all selected plants were highest at extreme dust site followed by high dust site. This increase in heavy metal concentration may be due to high dust pollution caused by active stone crushes at these Sites as revealed by (Padhy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) that dust pollution emitted during crushing process carry heavy metals, or may be due to higher heavy metals concentration in soil because of higher dust pollution of stone crushing industry as supported by the study of (Asati et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Padhy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) on dust pollution and heavy metals.\u003c/p\u003e\u003cp\u003eAt temporal scale in spring and summer season all physiological parameters increased while all biochemical attributes along with heavy metals decreased. It may be due to the lower dust concentration in spring and summer as mentioned by (Prajapati and Tripathi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). All selected plants survived in all seasons especially in cold winters with high suspended dust, it may be due to their physiological and biochemical modification i.e. formation of enzymatic and non-enzymatic anti-oxidants as revealed by (Gill and Tuteja, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) along with increase in osmoprotecrants as elaborated by (Ashraf and Foolad, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) in their study on plants abiotic stress resistance mechanisms.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eKirana hills are heavily contaminated by stone crusher dust, with the strongest impacts documented near the active crushing region. Dust pollution had a considerable impact on soil physicochemical parameters as well as heavy metal deposition at these sites. The vegetation cover of local plant community was severely affected by extreme dust pollution of stone crushing industry and \u003cem\u003eFagonia indica\u003c/em\u003e exhibited high morpho-anatomical adaptations for survival in such harsh dust polluted environment. Vegetation at such areas can be enhanced by promoting cultivation of local dust tolerant plants species.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to the Researchers Supporting Project number (RSP2024R118) of King Saud University, Riyadh,\u0026nbsp;Saudi\u0026nbsp;Arabia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMuhammad Asim Sultan wrote the original draft; Iftikhar Ahmad Supervised the study; Toqeer Abbas perform the formal analysis; Anis Ali Shah conducted the experiment;\u0026nbsp;Hosam O.\u0026nbsp;Elansary helped in drafting and writing,\u0026nbsp;and Shankarappa Sridhara helped in Conceptualization, graphs and statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers supporting the project (RSP2024R118) at King Saud University. Riyadh, Saudi Arabia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Acceptance and Participation \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional human ethics council at the University of Sargodha gave its approval to each of the protocols used in this study (Approval No. 37-21S IEC UOS). All of the experimental procedures used in this investigation complied with all applicable guidelines and laws. The authors state that the manuscript had never before been published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants granted their approval for the textual information (the \u0026quot;Material\u0026quot;) to be published in the aforementioned article and journal. For the publication of this manuscript, all authors have provided their written approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors assert that there are no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas, T., Ahmad, I., Khan, Z.I., Shah, A.A., Casini, R. and Elansary, H.O., 2023. Stress mitigation by riparian flora in industrial contaminated area of River Chenab Punjab, Pakistan. 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Sustainability, 14(17), p.10655.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biodiversity, chlorophyll, Dust Pollution, heavy metals, H2O2, metabolism, sclerification","lastPublishedDoi":"10.21203/rs.3.rs-4369086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4369086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePresent study was conducted to explore the population dynamics in vegetation of Kirana Hills, Sargodha growing under extreme dust pollution of stone crushing industry. Through extensive survey study sites were selected and floristic composition of the area was also completed. Heavy metal analysis of the dust revealed that all heavy metals were higher at extreme dust sites particularly in winter. The soil at each site and at each season varies based on the soil analysis. Vegetation data was collected by using quadrate method. Density, frequency, coverage and importance value of vegetation was significantly decreased at extreme dust sites specifically in winter. \u003cem\u003eFagonia indica\u003c/em\u003e was collected throughout the study sites and evaluated for morpho-anatomical, biochemical, and physiological characteristics. Metabolic and morpho-anatomical features of all plants were severely affected at extreme dust sites, however high metabolic rate, high sclerification in leaf, root and stem along with presence of large aerenchyma cells in roots were also noticed at extreme dust sites, and these modifications help to survive in such harsh dust polluted environment. In biochemical parameters reactive oxygen species (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) production was increased at extreme dust sites, furthermore activity of enzymatic antioxidants, non-enzymatic antioxidants and osmoprotectant were increased at extreme dust sites in \u003cem\u003eF. indica\u003c/em\u003e. Metabolic rate and concentration of heavy metals in selected ecotype also increased at extreme dust sites.\u003c/p\u003e","manuscriptTitle":"The impact of Heavy dust pollution reduces biodiversity by altering the metabolism and biochemical characteristics of Fagonia indica","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 09:06:03","doi":"10.21203/rs.3.rs-4369086/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed5e0b93-0800-43ee-a777-80527dd96b77","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32277660,"name":"Biological sciences/Physiology"},{"id":32277661,"name":"Biological sciences/Plant sciences"},{"id":32277662,"name":"Biological sciences/Structural biology"},{"id":32277663,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2024-07-30T10:03:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 09:06:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4369086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4369086","identity":"rs-4369086","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}