Phytoextraction response of conocarpus erectus

Geoscience

1. Spatial patterns for phytoextraction of heavy metals by Conocarpus erectus in distinct urban land uses of Karachi A thesis submitted for the degree of PhD Bilal Aslam Qureshi

2. Aim of the study To explore the cause-effect relationship among selected environmental and ecological variables and the phytoextraction response of Conocarpus erectus. These factors include, levels of heavy metal (Fe, Cu, Zn, Cr & Pb ) in soil, seasonal changes and Land Use and Land Cover ( LULC ) in the urban areas of Karachi.

3. Objectives Assessing the heavy metal concentration in soil and Conocarpus samples of uniform age, collected from various locations in Karachi along the seasonal timeline of monsoon. Establishing a local geochemical baseline for these metals in the soil, and utilizing this baseline to evaluate the pollution status of the sampled locations, employing Enrichment Factor ( EF ) and Geoaccumulation Index ( I geo ).

4. Objectives (contd.) To determine Bioaccumulation Factor (BAF) of Conocarpus erectus for the said heavy metals as an estimate of phytoextraction response of the plant. Finally, to explore the relationship BAF of the plant with soil’s metal contents and associated land use in different seasons with the help of statistical analyses such as Discriminant Analysis (DA) and Cluster Analysis (CA).

5. (a) Urban areas in 2000 (b) urban areas in 2020 Significance of the study Yan, X., Wang, J. Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm. Sci Rep 11, 23118 (2021). https://doi.org/10.1038/s41598-021-02565-9

6. LULC of Karachi consists of a broad spectrum of attributes, including residential, industrial, commercial (service and institutional), recreational spaces, barren land, vegetated areas and wetlands. Every part of the city exhibits a highly fragmented mosaic of various LULC types. As a result, categorizing land use in urban areas of Karachi proves challenging when applying conventional existing land use and urban planning models. Review of the Literature; Urban LULC of Karachi

7. An extensive research in the recent past reported elevated heavy metal burden of soil in urban areas over time around the world. Studies on the heavy metal content of soil in urban areas of Karachi are limited both in terms of quantity and scope. There is a lack of baseline data to establish the levels of soil contamination by heavy metals. Existing literature primarily focuses on heavy metal concentrations with specific land-use areas. Heavy metals in urban environment

8. Limited literature is available about the metal accumulation by this plant. Most of the studies found over this subject lack field experimentation and are based on controlled pot experiments. The age of the plants were not taken into account in any of these studies. Phytoextraction response of Conocarpus erectus

9. Materials and Methods Selection and characteristics of the study area The central part of the city, stretched over 500 km 2 of land, comprising 20 administrative units whose population densities range from 10,000 to almost 100,000 persons per km 2 is the most urbanized part of the city was selected as the study area.

10. Attributes of Sampling Locations

11. Extraction of Land cover from Satellite image

12. Acquisition of Conocarpus for plantation

13. Plant and soil sampling Soil samples were collected for Pre-monsoon( PRM ) and Post-monsoon ( POM ) seasons.

14. Sample Preparation for AAS analysis Soil samples were prepared by pseudo-total digestion method. Plant samples were digested by AOAC official method 975.03 (AOAC Int. 2000).

15. F AAS analysis Perkin–Elmer AAnalyst-700 atomic absorption spectrometer equipped with air-acetylene burner was used. Standard calibration method with background correction was adopted.

16. The data was tested for normality in SPSSv20®. Shapiro- Wilk test was used for the present study, as this test is considered more robust for smaller datasets. For the data not showing a normal distribution, a log 10 transformation was carried out to achieve a Gaussian distribution. All data was Z-scale standardized to eliminates the effects of differences in unites, mean values and variances. The cumulative distribution function (CDF) curves of the metal’s data sets were used to estimate the baseline metal concentrations in soil. The upper inflexion point of the regression line under the linear regression model for P≤0.05 and R2≥0.999 was considered the baseline concentration. Statistical and analysis of Soil-data

17. Spatial Variability of the concentrations of metals in soil was assessed and compared with each other by using the Coefficients of variation (CV). CV= (SD×100)/mean) The assessment of enrichment/ pollution status of heavy metal in the soil was mainly done by evaluation of soil Geo-accumulation index ( I geo ) and Enrichment Factor ( EF ). Statistical analysis of Soil-data (contd.)

18. Hierarchical Clustering Analysis (CA) was used in two ways to assess the behavior of metals’ distribution in soil. it was used on the individual PRM and POM soil-metal data to identify the groups of the metals in soil that are mutually related across the seasonal- timeline, and hence to deduce their origin and fate. it was used to classify sampling sites with respect to the metals’ spatial distribution in soil to find out their behavior in different LULC. Statistical analysis of Soil-data (contd.)

19. Discriminant Analysis (DA) was used to study the relation between different soil parameters (including metal contents of the soil, EF and I geo ) and LULC, both in PRM and POM scenarios. Statistical analysis of Soil-data (contd.)

20. Bioaccumulation factor (BAF) was evaluated as the phytoextraction response of the plants: BAF values were subjected to CA and DA to find if the spatial change in LULC has any relation with it. Analysis of Plant-data

21. Site-wise comparison of PRM and POM metal concentration in soil Results and discussion

22. Results and discussion (contd.) The metal concentration in soil can change after rain due to several reasons including: Sinking of airborne matter containing metal, therefore causing an increase of metal in soil, and, Elution and relocation via floodwater runoffs, which may decrease metal contents of soil , depending upon the topography of the region .

23. Pearson Correlation between metals in soil (PRM and POM) Results and discussion (contd.) A strong positive correlations for Cu-Zn and Cu- Pb POM pairs indicate the presence of common sources of these metals in the environment.

24. H ighest variability in both PRM and POM scenarios was found in Pb concentration ( CV = 132.70% and 99.4% respectively). Cu showed greatest variation next to Pb in both PRM and POM scenarios, (CV = 52.99 % and 83.55% respectively ). The CV for Zn, Fe and Cr show a moderate variability in both seasons. A low variation indicates that the average amount of a metal in soil have not much exceeded the natural background. Results and discussion (contd.) Spatial Variability of metals in soil

25. Baseline metal concentrations in soil obtained from CFD curves Results and discussion (contd.)

26. Fe was used as a normalizer for the determination of EF because: its high abundance in soil yet the least variability indicates that its concentration is less susceptible to anthropic impacts. its insignificant correlation to the other heavy metals also makes it suitable to differentiate between natural and contaminated soil. The EF values obtained for Cr, Cu and Pb show moderately high metal enrichment, more likely due to human input. Zn is only found to be moderately enriched. All of those locations showing an enrichment of Pb in soil are of commercial or residential or a composite of these land uses. Results and discussion (contd.)

27. Enrichment factor (EF) for Heavy Metals Results and discussion (contd.)

28. Variation of I geo across sampling sites for (a) PRM and (b) POM seasons Results and discussion (contd.) All the locations showing high values of I geo for Pb have residential and commercial or their composite land use.

29. DA for soil’s data Scatter plot of Discriminant Functions (metal indices PRM+POM) ~90% success rate of classification Scatter plot of Discriminant Functions (metal concentration PRM+POM) ~ 77% success rate of classification Results and discussion (contd.)

30. CA for soil’s data Results and discussion (contd.) CA based on soil indices of PRM+POM data sets showed an absolute classification with an 85 % within clusters similarity level.

31. Spatial analysis The land-cover map of Karachi

32. Change in the soil’s metal distribution in the PRM to POM transition Spatial analysis of metal in Soil

33. (Right) Traffic congestion points Mehdi , M. R., Kim, M., Seong , J. C., & Arsalan , M. H. (2011). Spatio -temporal patterns of road traffic noise pollution in Karachi, Pakistan. Environment International, 37(1), 97-104. doi:10.1016/j.envint.2010.08.003 (Left) Change in the soil’s Zn distribution in the PRM to POM transition Spatial analysis of metal in soil (contd.)

34. The eastern part of the AOI consists of lower proportion of impervious urban land cover. Cu concentration decreased after precipitation in that area. A contrasting pattern for Pb and Zn can be observed for the region The effect of rain-water runoff on a metal’s concentration in soil is related to its mobility through soil profile

35. In all plant samples, the concentration of Cr and Pb metals were found to be below the detection limits. Probable reason can be low solubility of these metal, their low translocation factor or some rejection mechanism of plant root system. The concentration and the Bioaccumulation Factor (BAF) of Cu, Fe and Zn metals were therefore used for the plant analysis. Results of Plant analysis

36. Descriptive statistics of Plant anaylsis The mean concentration of Fe in plant is almost 4 times higher than that of Cu and 8 times the concentration of Zn metal, yet the ratio of mean value of BAF of Fe is less than 1/250 times BAF of Cu and 1/20 times of that of Zn. This is due to the very high Fe contents of the soil samples.

37. No significant correlation was found between the concentration of metal in plant and in the related soil samples. Pearson correlation

38. Scatter plot of Discriminant Functions showed only a 46.7% success rate of classification DA based on BAF values

39. CA based on BAF values Equally inconsistent classification of sites into the land use types by the use of BAF values

40. Enrichment of copper, lead and zinc in the urban soil of Karachi is strongly correlated to anthropogenic activities, especially vehicular transport. The concentration indices of chromium, copper, lead and zinc have a strong relationship with the surrounding land use. The effect of rain on the soil’s heave metal content is also correlated to the land use and cover (LULC). Conocarpus erectus showed moderately high accumulation for copper and a low bioaccumulation for iron and zinc, where as it did not accumulate any measurable quantity of chromium and lead through the active mechanism during the experimental period. Conclusion

41. Bioaccumulation response of the plant for copper, iron and zinc was not correlated to soil’s metal contents, while the dependence of bioaccumulation response of plant over the local LULC was very weak. Conocarpus plant can be used to remove copper metal from the soil regardless of the land use pattern, considering the bioaccumulation factor as well as the short crop time. This ability of the plant to accumulate copper in the harvestable parts in a short time makes it a potential candidate for the further study to investigate its potential to be used for phytoextraction of Cu. Conclusion (contd.)

42. This study is an initial step in the in the advancement of phytoextraction technology utilizing Conocarpus erectus. It is recommended to conduct an extensive field study involving multiple harvests of these plants from various locations to assess the impact of the crop-period. It is important to delve into the underlying reasons for the probable rejection of metals by the plant. For future research, it is worthwhile to incorporate a wider range of metals and to implement a broader experimental designs to enhance the scope and depth of investigation. Recommendations

43. Publications

44. Publications

45. Publications

46. Research papers in progress (1) Monsoon-Induced spatial variation in soil's heavy metal content across different land covers in urban areas of Karachi (2) Assessing In-Situ Phytoextraction Potential of Conocarpus erectus in the Urban Environment of Karachi (3) Utilizing Conocarpus erectus as a Bioindicator for Assessing Heavy Metal Pollution in Urban Environments