The Impact of Different Types of Hydrocarbon Disturbance on the Resiliency of Native Desert Vegetation in a War-Affected Area: A Case Study from the State of Kuwait
Abstract
:1. Introduction
2. Methods and Materials
2.1. Study Area
2.2. Data Collection
2.2.1. Soil Data Collection
2.2.2. Remote-Sensing Data Collection and Processing
2.2.3. Assessing Impact of TPH and Soil Parameter Concentrations on the Resiliency of Native Plants
3. Results
3.1. Concentration of Hydrocarbon Contamination
3.2. Impact of Hydrocarbon Contamination on Soil Properties
3.3. Influence of Oil Properties and TPH on Vegetation Coverage
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Morkunas, I.; Woźniak, A.; Mai, V.C.; Rucińska-Sobkowiak, R.; Jeandet, P. The role of heavy metals in plant response to biotic stress. Molecules 2018, 23, 2320. [Google Scholar] [CrossRef] [Green Version]
- Srivastava, V.; Sarkar, A.; Singh, S.; Singh, P.; de Araujo, A.S.; Singh, R.P. Agroecological responses of heavy metal pollution with special emphasis on soil health and plant performances. Front. Environ. Sci. 2017, 5, 64. [Google Scholar] [CrossRef] [Green Version]
- Shen, Z.G.; Li, X.D.; Wang, C.C.; Chen, H.M.; Chua, H. Lead phytoextraction from contaminated soil with high-biomass plant species. J. Environ. Qual. 2002, 31, 1893–1900. [Google Scholar] [CrossRef] [PubMed]
- Sharma, M.R.; Raju, N. Correlation of heavy metal contamination with soil properties of industrial areas of Mysore, Karnataka, India by cluster analysis. Int. Res. J. Environ. Sci. 2013, 2, 22–27. [Google Scholar]
- Al-Sarawi, M.; Massoud, M.; Al-Abdali, F. Preliminary assessment of oil contamination levels in soils contaminated with oil lakes in the greater Burgan oil fields, Kuwait. Water Air Soil Pollut. 1998, 106, 493–504. [Google Scholar] [CrossRef]
- Khordagui, H.; Al-Ajmi, D. Environmental impact of the Gulf War: An integrated preliminary assessment. Environ. Manag. 1993, 17, 557–562. [Google Scholar] [CrossRef]
- Kwarteng, A.Y. Multitemporal remote sensing data analysis of Kuwait’s oil lakes. Environ. Int. 1998, 24, 121–137. [Google Scholar] [CrossRef]
- Omar, S.A.; Briskey, E.; Misak, R.; Asem, A. The Gulf War impact on the terrestrial environment of Kuwait: An overview. In The Environmental Consequences of War: Legal, Economic and Scientific Perspectives; Cambridge University Press: Cambridge, UK, 2000; pp. 316–337. [Google Scholar]
- Al_Ateeqi, S.; Al-Musawi, L.; Sharma, V.; Abdullah, M.; Ma, X. Plant communities and potential native phytoremediator species in petroleum hydrocarbon-polluted desert systems. Authorea Prepr. 2021. [Google Scholar] [CrossRef]
- Abdullah, M.M.; Feagin, R.A.; Abdullah, M.T.; Al-Musawi, L. Will autogenic succession be sufficient to recover from vegetation cover loss or will soil condition need to be addressed in the arid lands of Kuwait? Arab. J. Geosci. 2017, 10, 111. [Google Scholar] [CrossRef]
- Abella, S. Restoration of desert ecosystems. Nat. Educ. Knowl. 2012, 4, 7. [Google Scholar]
- Abdullah, M.M.; Assi, A.T.; Abdullah, M.T.; Feagin, R.A. Arid ecosystem resilience to total petroleum hydrocarbons disturbance: A case-study from the State of Kuwait associated with the Second Gulf War. Land Degrad. Dev. 2020, 31, 155–167. [Google Scholar] [CrossRef]
- Duniway, M.C.; Herrick, J.E.; Monger, H.C. The high water-holding capacity of petrocalcic horizons. Soil Sci. Soc. Am. J. 2007, 71, 812–819. [Google Scholar] [CrossRef] [Green Version]
- Yan, A.; Wang, Y.; Tan, S.N.; Mohd Yusof, M.L.; Ghosh, S.; Chen, Z. Phytoremediation: A promising approach for revegetation of heavy metal-polluted land. Front. Plant Sci. 2020, 11, 359. [Google Scholar] [CrossRef] [PubMed]
- Kwarteng, A.Y. Remote sensing assessment of oil lakes and oil-polluted surfaces at the Greater Burgan oil field, Kuwait. Int. J. Appl. Earth Obs. Geoinf. 1999, 1, 36–47. [Google Scholar] [CrossRef]
- Abdullah, M.M.; Assi, A.; Feagin, R.; Al Ali, Z. Quantifying vegetation response to seasonal rainfall fluctuation to estimate the probability of receiving maximum vegetation growth in arid landscapes: An investigative study in Kuwait. Rangel. Ecol. Manag. 2021, in press. [Google Scholar]
- Omar, S.A.; Al-Mutawa, Y.A.A.; Zaman, S. Vegetation of Kuwait: A Comprehensive Illustrative Guide to the Flora and Ecology of the Desert of Kuwait; Aridland Agriculture Department, Food Resources Division, Kuwait Institute for Scientific Research: Safat, Kuwait, 2000. [Google Scholar]
- KISR. Soil Survey for the State of Kuwait; AACM International: Adelaide, Australia, 1999. [Google Scholar]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Segarra, J.; Buchaillot, M.L.; Araus, J.L.; Kefauver, S.C. Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy 2020, 10, 641. [Google Scholar] [CrossRef]
- Bhatnagar, S.; Gill, L.; Regan, S.; Naughton, O.; Johnston, P.; Waldren, S.; Ghosh, B. Mapping vegetation communities inside wetlands using Sentinel-2 imagery in Ireland. Int. J. Appl. Earth Obs. Geoinf. 2020, 88, 102083. [Google Scholar] [CrossRef]
- Al-Ali, Z.; Abdullah, M.; Asadalla, N.; Gholoum, M. A comparative study of remote sensing classification methods for monitoring and assessing desert vegetation using a UAV-based multispectral sensor. Environ. Monit. Assess. 2020, 192. [Google Scholar] [CrossRef]
- Wood Jr, C.W.; Nash III, T.N. Copper smelter effluent effects on Sonoran Desert vegetation. Ecology 1976, 57, 1311–1316. [Google Scholar] [CrossRef]
- Ouzounidou, G.; Eleftheriou, E.; Karataglis, S. Ecophysical and ultrastructural effects of copper in Thlaspi ochroleucum (Cruciferae). Can. J. Bot. 1992, 70, 947–957. [Google Scholar] [CrossRef]
- Keltjens, W.; Van Beusichem, M. Phytochelatins as biomarkers for heavy metal stress in maize (Zea mays L.) and wheat (Triticum aestivum L.): Combined effects of copper and cadmium. Plant Soil 1998, 203, 119–126. [Google Scholar] [CrossRef]
- Nicholls, A.M.; Mal, T.K. Effects of lead and copper exposure on growth of an invasive weed, Lythrum salicaria L. (Purple Loosestrife). Ohio J. Sci. 2003, 103, 129–133. [Google Scholar]
- Hewelke, E.; Szatyłowicz, J.; Hewelke, P.; Gnatowski, T.; Aghalarov, R. The impact of diesel oil pollution on the hydrophobicity and CO 2 efflux of forest soils. Water Air Soil Pollut. 2018, 229, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Cho-Ruk, K.; Kurukote, J.; Supprung, P.; Vetayasuporn, S. Perennial plants in the phytoremediation of lead-contaminated soils. Biotechnology 2006, 5, 1–4. [Google Scholar]
- Antonijević, M.; Dimitrijević, M.; Milić, S.; Nujkić, M. Metal concentrations in the soils and native plants surrounding the old flotation tailings pond of the Copper Mining and Smelting Complex Bor (Serbia). J. Environ. Monit. 2012, 14, 866–877. [Google Scholar] [CrossRef]
- Bainbridge, D.A. A Guide for Desert and Dryland Restoration: New Hope for Arid Lands; Island Press: Washington, DC, USA, 2007. [Google Scholar]
- Al-Ali, Z.M.; Abdullah, M.M.; Assi, A.A.; Alhumimidi, M.S.; Wasan, A.-Q.S.; Ali, T.S. The immediate impact of the associated COVID-19’s lockdown campaign on the native vegetation recovery of Wadi Al Batin Tri-state desert. Remote Sens. Appl. Soc. Environ. 2021, 23, 100557. [Google Scholar]
- Abdullah, M.M.; Al-Ali, Z.M.; Abdullah, M.M.; Srinivasan, S.; Assi, A.T.; Al Atiqi, S. Investigating the applicability of UAVs in characterizing desert shrub biomass and developing biological indicators for the selection of suitable revegetation sites. J. Environ. Manag. 2021, 288, 112416. [Google Scholar] [CrossRef]
Sites | Surface Layer | Subsurface Layer | Deep Layer | Number of Samples | Method of Sampling |
---|---|---|---|---|---|
Dry Oil Lake | Dry-S (22 samples) 0.0–0.15 m | Dry-D1 (19 samples) 0.16–0.40 m | Dry-D2 (10 samples) >0.41 m | 51 | Grab method |
Wet Oil Lake | Wet-S (11 samples) 0.0–0.30 m | Wet-D1 (16 samples) 0.31–0.65 m | Wet-D2 (7 samples) >0.65 m | 34 | Core method |
Tarcrete | Tar-S (22 samples) 0.01–0.05 m | Tar-D (13 samples) 0.10–0.15 m | No samples were collected | 35 | Grab method |
Bare Soil | BS-S 0.0–0.05 m | No samples were collected | No samples were collected | 25 | Grab method |
Type of Parameter | Preparation Method | Analysis Method | |
---|---|---|---|
pH | Chemical properties | USEPA 9045 | USEPA 9045 |
Moisture Content (%) | Indicator of quality and fertility of soil | ASTM D2216 | ASTM D2216 |
Salinity (SAR) | Chemical properties | USEPA 6010 B | USEPA 6010 B |
Electrical conductivity (µS/cm) (EC) | Chemical properties | USEPA 9050 A | USEPA 9050 A |
Aliphatic Aromatics C35–C90 (mg/kg) | Chemical compounds | USEPA 8260 | USEPA 8260 |
TPH (HEM) (mg/kg) | Chemical compounds | USEPA 9071 B | USEPA 9071 B |
Chromium III (mg/kg) (Cr) | Heavy metals | USEPA 3015 B | USEPA 6010 B |
Total Chromium (mg/kg) (Cr) | Heavy metals | USEPA 3015 B | USEPA 6010 B |
Copper (mg/kg) (Cu) | Heavy metals | USEPA 3015 B | USEPA 6010 B |
Nickel (mg/kg) (Ni) | Heavy metals | USEPA 3015 B | USEPA 6010 B |
Lead (mg/kg) (Pb) | Heavy metals | USEPA 3015 B | USEPA 6010 B |
Zinc (mg/kg) (Zn) | Heavy metals | USEPA 3015 B | USEPA 6010 B |
Vanadium (mg/kg) (V) | Heavy metals | USEPA 3015 B | USEPA 6010 B |
Water-soluble Boron (mg/kg) (B) | Nutrient | USEPA 3015 B | USEPA 6010 B |
Barium (mg/kg) (Ba) | Heavy metals | USEPA 3015 B | USEPA 6010 B |
DOL | WOL | Tarcrete | Bare Soil (Uncontaminated) | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MAX | MIN | AVG | STDEV | MAX | MIN | AVG | STDEV | MAX | MIN | AVG | STDEV | MAX | MIN | AVG | STDEV | |
pH | 8.7 | 6.9 | 7.7 | 0.3 | 8.3 | 6.5 | 7.2 | 0.4 | 8.9 | 7.4 | 8.1 | 0.4 | 8.6 | 7.5 | 7.9 | 0.3 |
Moisture (%) | 9.2 | 0.2 | 1.4 | 1.5 | 28.7 | 1.3 | 7.7 | 6.3 | 2.6 | 0.5 | 1.3 | 0.5 | 2.3 | 0.5 | 1.1 | 0.5 |
Salinity (SAR) | 8.7 | 0.9 | 3.2 | 1.8 | 16.3 | 2.4 | 7.7 | 2.6 | 4.6 | 0.9 | 2.4 | 0.9 | 1.8 | 0.8 | 1.3 | 0.3 |
Electrical conductivity (µS/cm) | 8950 | 173 | 2291 | 2323 | 32,560 | 1400 | 15,634 | 7847.9 | 5213 | 168 | 1312.3 | 1105.3 | 624.5 | 125 | 349.1 | 163 |
Aliphatic Aromatics C35–C90 (mg/kg) | 162,354 | 176 | 40,059 | 40,406 | 586,100 | 626 | 97,776 | 129,882.9 | 131,680 | 0.5 | 16,746.2 | 30,795.5 | 2658 | 1 | 988.7 | 779.9 |
TPH (mg/kg) | 146,119 | 138 | 35,741 | 36,347 | 527,490 | 563 | 87,961 | 116,916.3 | 128,541 | 135 | 2,4063.3 | 31,014.4 | 1685 | 186 | 726.8 | 404.3 |
Chromium III (mg/kg) | 67.7 | 7.2 | 21.6 | 13.5 | 27 | 7.8 | 14.9 | 4.9 | 26.5 | 0.2 | 11.2 | 8.4 | 23.5 | 0.2 | 10.5 | 8.1 |
Chromium Total (mg/kg) | 67.5 | 7.2 | 21.6 | 13.4 | 27 | 7.5 | 14.9 | 4.9 | 26.5 | 0.2 | 11.2 | 8.4 | 23.5 | 0.2 | 10.5 | 8.1 |
Copper (mg/kg) | 15.6 | 1.2 | 4.6 | 3.2 | 6 | 1.7 | 3.7 | 1.2 | 13.6 | 1.4 | 7.1 | 3.9 | 12.1 | 1.1 | 4.6 | 2.9 |
Nickel (mg/kg) | 28.9 | 2.2 | 13.8 | 7.8 | 24 | 5.6 | 13.6 | 4.4 | 15.8 | 1.9 | 6.2 | 3.8 | 12.2 | 1.6 | 6.3 | 3.5 |
Lead (mg/kg) | 7.5 | 0.9 | 2.4 | 1.5 | 4 | 0.8 | 1.9 | 0.7 | 9.4 | 0.8 | 3.9 | 2.7 | 10.3 | 0.9 | 2.5 | 2.1 |
Zinc (mg/kg) | 23.5 | 4.9 | 14.7 | 4.6 | 21 | 4.1 | 11.9 | 4.2 | 16.5 | 1.2 | 6.9 | 52 | 16.2 | 1.1 | 8.3 | 5.1 |
Vanadium (mg/kg) | 32.5 | 2.3 | 12.9 | 8.3 | 26 | 5.6 | 13.3 | 5.7 | 11.2 | 0.2 | 4.9 | 3.6 | 16.7 | 0.2 | 5.5 | 4.5 |
Water-soluble boron (mg/kg) | 23.5 | 1 | 5.7 | 5.4 | 10 | 1.5 | 5.2 | 2.1 | 2.8 | 0.2 | 1.4 | 0.8 | 2.7 | 0.2 | 1 | 0.8 |
Barium (mg/kg) | 172.6 | 18.1 | 62.8 | 33.3 | 154 | 33.8 | 105.1 | 31.5 | 65.3 | 0.2 | 34.8 | 20.5 | 57.7 | 0.2 | 29 | 17.5 |
Independent Parameters | (DOL) | (WOL) | Tarcrete | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | p Value | R2-Added | p Value | R2 | RMSE | p Value | R2-Added | p Value | R2 | RMSE | p Value | R2-Added | p Value | |
Simple Linear Regression | Multivariate, Stepwise | Simple Linear Regression | Multivariate, Stepwise | Simple Linear Regression | Multivariate, Stepwise | ||||||||||
pH | 0.02 | 36,416 | 0.371 | 0.31 | 98,927.3 | <0.001 * | 0.31 | <0.001 * | 0.032 | 31,054 | 0.351 | ||||
Moisture % | 0.05 | 35,734 | 0.121 | 0.01 | 118,083 | 0.558 | 0.051 | 30,612 | 0.182 | ||||||
Electrical conductivity (µS/cm) | 0.27 | 31,309 | <0.001 * | 0.27 | <0.001 * | 0.13 | 111,089 | 0.041 * | 0.371 | 24,974 | <0.001 * | 0.37 | <0.001 * | ||
Chromium III (mg/kg) | 0.16 | 33,586 | 0.003 * | 0.07 | 0.031 * | 0.22 | 105,157 | 0.006 * | 0.001 | 31,471 | 0.891 | ||||
Chromium Total (mg/kg) | 0.16 | 33,696 | 0.004 * | 0.06 | 0.033 * | 0.22 | 105,173 | 0.006 * | 0.001 | 31,471 | 0.891 | ||||
Copper (mg/kg) | 0.06 | 35,503 | 0.071 | 0.29 | 100,081 | 0.001 * | 0.29 | 0.001 * | 0.031 | 30,998 | 0.323 | ||||
Nickel (mg/kg) | 0.02 | 36,429 | 0.382 | 0.13 | 110,828 | 0.037 * | 0.012 | 31,356 | 0.612 | ||||||
Lead (mg/kg) | 0.14 | 33,951 | 0.006 * | 0.09 | 113,029 | 0.078 | 0.051 | 30,663 | 0.192 | ||||||
Zinc (mg/kg) | 0.25 | 31,903 | <0.001 * | 0.25 | <0.001 * | 0.17 | 108,311 | 0.016 * | 0 | 31,480 | 0.981 | ||||
Vanadium (mg/kg) | 0.01 | 36,528 | 0.481 | 0.06 | 0.043 * | 0.04 | 116,206 | 0.245 | 0.16 | 0.006 * | 0 | 31,480 | 0.991 | ||
Water-soluble boron (mg/kg) | 0.01 | 36,469 | 0.421 | 0.12 | 111,326 | 0.044 * | 0.031 | 30,942 | 0.293 | ||||||
Barium (mg/kg) | 0.1 | 34,814 | 0.023 * | 0.05 | 0.045 * | 0.16 | 109,104 | 0.021 * | 0.022 | 31,230 | 0.472 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kalander, E.; Abdullah, M.M.; Al-Bakri, J. The Impact of Different Types of Hydrocarbon Disturbance on the Resiliency of Native Desert Vegetation in a War-Affected Area: A Case Study from the State of Kuwait. Plants 2021, 10, 1945. https://doi.org/10.3390/plants10091945
Kalander E, Abdullah MM, Al-Bakri J. The Impact of Different Types of Hydrocarbon Disturbance on the Resiliency of Native Desert Vegetation in a War-Affected Area: A Case Study from the State of Kuwait. Plants. 2021; 10(9):1945. https://doi.org/10.3390/plants10091945
Chicago/Turabian StyleKalander, Eman, Meshal M. Abdullah, and Jawad Al-Bakri. 2021. "The Impact of Different Types of Hydrocarbon Disturbance on the Resiliency of Native Desert Vegetation in a War-Affected Area: A Case Study from the State of Kuwait" Plants 10, no. 9: 1945. https://doi.org/10.3390/plants10091945
APA StyleKalander, E., Abdullah, M. M., & Al-Bakri, J. (2021). The Impact of Different Types of Hydrocarbon Disturbance on the Resiliency of Native Desert Vegetation in a War-Affected Area: A Case Study from the State of Kuwait. Plants, 10(9), 1945. https://doi.org/10.3390/plants10091945