Journal of Chemical, Biological and Physical Sciences

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May 19, 2018 - D.8.3.15671.] Influence of Diogo heavy mineral sand mining process on physical and chemical characteristics and microbiological properties of.

JCBPS; Section D; May 2018 – July - 2018, Vol. 8, No. 3; 156-171.

E- ISSN: 2249 –1929

[DOI: 10.24214/jcbps.D.8.3.15671]

Journal of Chemical, Biological and Physical Sciences An International Peer Review E-3 Journal of Sciences Available online atwww.jcbsc.org

Section D: Environmental Sciences CODEN (USA): JCBPAT

Research Article

Influence of Diogo heavy mineral sand mining process on physical and chemical characteristics and microbiological properties of dune soils in the northern coastal line of Senegal Mandiéré FALL1,2*, Dioumacor FALL3,4 , Saliou NDIAYE2, Idrissa GUIRO1, Niokhor BAKHOUM4, Diegane DIOUF 4,5 1

Grande Cote Operation S.A, Atryum Center 2ème étage, 6 Route de Ouakam, BP 16844 DakarSénégal

Ecole Nationale Supérieure d’Agriculture (ENSA), Université de Thiès, Route de Khombole , BP

2

A296, Thiès-Sénégal 3

Institut Sénégalais de Recherches Agricoles (ISRA) / Centre National de Recherches Agronomiques (CNRA), Route de Diourbel, BP 53, Bambey-Sénégal

4

LCM-Laboratoire Commun de Microbiologie IRD/ISRA/UCAD, Centre de Recherche de Bel-Air, BP 1386, Dakar-Sénégal

5

Département de Biologie Végétale, Université Cheikh Anta Diop de Dakar, BP 5005, Dakar-Sénégal

Received: 26 April 2018; Revised: 5 May 2018; Accepted: 19 May 2018

Abstract: Whilst important for the Senegalese economy, mining may causes destruction of vegetation, soil degradation and other environmental impacts. Our study aimed to assess the effect of the Diogo mineral sand process on the physical and chemical characteristics and microbiological properties of dunes soils in order to successfully rehabilitate the mined area in the northern coast of Senegal. Soils were sampled before and after mining at 0-20, 20-40, 40-60, 60-100 cm, 4-5 and 9-10 m horizons in dry season (March). Results showed that depth and the interaction between depth and mining had no significant effect on physical and chemical characteristics (pH, electrical conductivity, carbon, nitrogen, exchangeable bases, granulometry) of dune soils. However, mined dune soils demonstrated significantly decreased electrical conductivity, calcium, potassium, manganese and cation exchange capacity. No 156

J. Chem. Bio. Phy. Sci. Sec. D ; May 2018 – July - 2018, Vol. 8, No. 3; 156-171 DOI:10.24214/jcbps.D.8.3.15671.]

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Mandiéré FALL et al.

significant effect of mining was observed on total carbon and total nitrogen. However, a significant increase in pH, phosphorus, magnesium, sodium, iron, saturation rate, clays, silts and coarse sands was noted in the tailings. The depth had no significant negative effect on soil total microbial biomass, Azospirillum, Phosphobacteria populations. In contrast, mining decreased these microorganisms. AMF spores, Azospirillum and Phosphobacteria were significantly higher before than after mining at 4-5 m horizon. Mining process modified the structure of soil microbial communities and decreased the number and the activities of soil microorganisms. Mineral sand mining process alters the physical and chemical parameters, decreases the microorganism richness and modifies the structure of microbial communities of soil. However, it is necessary to follow the dynamic of these parameters during the years of site rehabilitation. Key words: Mineral sand mining, soil characteristics, tailings, northern coast, Niayes, Senegal. INTRODUCTION Mining operations, which involve mineral extraction from the earth’s crust, tends to cause alteration of the landscape, topography and biological communities of the soil1-3. In the northern coast of Senegal (Diogo district), open cast zircon, ilmenite and leucoxene mining, is carried out by the Grande Côte Operations (GCO), which is the largest single dredge mineral sands operation in the world. After mining, the mined areas must be rehabilitated according to Senegal's mining and forestry codes in order to achieve a sustainable land use. Any changes in the soil may alter the number and/or the activity of soil microorganisms, which can affect soil biochemical processes and ultimately influence soil fertility and plant growth4. Indeed, soil microorganisms are sensitive to land use and management and can be used to indicate soil health 5-9. Organic carbon, microbial biomass, microbial diversity and enzyme activity have been widely used to assess impact of change in land use and reclaimed soils 10. Soil microbial biomass, the living part of soil organic matter (SOM), acts as an important ecological indicator and is responsible for the decomposition and mineralization of plant and animal residues present in the soil 11. Measurement of microbial biomass provides a sensitive indication of changes brought by soil management. Such changes can be detected in soil organic carbon 12,13. Therefore, soil microbial biomass can be considered as an important parameter for quantitative assessment of soil degradation as well as reclamation process 14 and soil structural stability 15, soil management and perturbation studies 16,17. Arbuscular mycorrhizal fungi (AMF), plant growth promoting rhizobacteria (PGPR) as Azospirillum and Phosphobacteria are free-living rhizobacteria found in close association with plants roots, where they exert beneficial effects on plant growth. The activity of soil microorganisms and microbial community composition determine the decomposition and accumulation of soil organic matter 18, 19, but also N2-fixation, nitrification and denitrification, and the accumulation of plant available NH4+ and NO3- 20. Moreover, enzyme activities can provide indications of quantitative changes in SOM. Soil microbes secreted extracellular enzymes that enhanced decomposition of organic matter and transformation of nitrogen compounds 21. The soil enzyme activities reflected the dynamics of microbial metabolic processes associated with nutrient cycling and were sensitive indicators of environmental stresses due to degradation of soil quality. Enzyme activities measurement is widely used to examine nutrient cycling processes in soil 22-24. Soil hydrolyses measurements provide an early indication of changes in soil fertility, since they are related 157

J. Chem. Bio. Phy. Sci. Sec. D ; May 2018 – July - 2018, Vol. 8, No. 3; 156-171 DOI:10.24214/jcbps.D.8.3.15671.]

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Mandiéré FALL et al.

to the mineralization of important elements such as nitrogen, phosphorus and carbon. Hydrolysis of fuorescein diacetate (FDA) has been widely used as accurate, sensitive, and simple method for determining total microbial activity in soil 20, 25, 26. Phosphatases are enzymes that hydrolyze phosphate groups from a wide variety of organic substrates (phosphate esters), producing an alcohol and phosphoric acid. In this respect, these microbial parameters were used as indicators of soil health 27, 28. Thus, in order to facilitate the ongoing rehabilitation process of soils after mining, it is necessary to evaluate the effects of mineral sand mining on dune soils properties in Diogo. The present work aimed to assess the effect of mineral sand mining process on the physical and chemical characteristics and the microbiological properties (abundance, microbial activities, structure and microbial community’s diversity) of dune soils in the northern coast of Senegal. We hypothesized that the mining process affects negatively the physical and chemical characteristics of soil and the abundance, the microbial activities and the structure and diversity of soil microorganisms. MATERIAL AND METHODS Site description, experimental design and soils sampling: The experiment was conducted in March 2017 in the dune sands of Diogo (UTM_WGS_84: 28P 308160 E, 1691962 N), a northern coast located at 100 km in the north of Dakar (Senegal), where total annual rainfall varies between 200 and 600 mm with an average of 412.3 ± 141 mm (Fig. 1).

Figure 1: Localization of the study in the northern coastal line of Senegal In this part, temperatures vary between 15 and 33°C with an average of approximately 24.8°C. The experimental site was a block of 9 ha delimited in the mining path. Before mining (control), the site was sub-divided into 3 plots of 3 ha each in front of the dredge (Fig. 2). In each plot, soils were sampled in each plot using the "X" method, i.e. 5 sample points. Soils samples were collected at 0-20 cm, 20-40 cm, 40-60 cm, 60-100 cm, 4-5 m, and 9-10 m layers. Soil samples collected at the same layer in the 3 158

J. Chem. Bio. Phy. Sci. Sec. D ; May 2018 – July - 2018, Vol. 8, No. 3; 156-171 DOI:10.24214/jcbps.D.8.3.15671.]

Influence …

Mandiéré FALL et al.

plots, were pooled to get a homogenous soil sample in the plot. After the dredge and the wet concentration plant finished mining this plot (about ten days), the soils samples were collected in the tailings of the mined plot. Then, thirty-six (36) soil samples (18 for each date sampling) were collected and stored in cool environment (4˚C) to avoid any microbial activity before the analyses.

Figure 2: Experimental design of soils sampling before and after mining Effect of mineral sand mining process on the physical and chemical characteristics of dune soils:Soils physical and chemical characteristics were carried out by the Laboratoire Sol-Eaux-Plante of the Centre National de Recherches Agronomiques (CNRA) of Bambey (Institut Sénégalais de Recherches Agricoles). Effect of mineral sand mining process on microbiological properties of dune soils Determination of soil total microbial biomass: Soil microbial biomass was determined using the fumigation-extraction method (Amato and Ladd, 1988). Fumigated and unfumigated soil samples were suspended in KCl 1M solution, shaken at 25°C for 2 h and then filtered. Ninhydrin-reactive N content was determined by flow injection analysis (Evolution II, Alliance-Instruments, France). Microbial biomass C was estimated from the gain in ninhydrin-reactive N after a 10-days fumigation period, multiplied by Amato and Ladd,29. Microbial biomass was expressed in µg C g-1 of dry soil. Estimation of arbuscular mycorrhizal fungi (AMF) spores: AMF spores were extracted from 100 g of soil of each sample by wet sieving followed by floatation centrifugation in 50% sucrose 30. Estimation of Azospirillum and Phosphobacter: The populations of Azospirillum and Phosphobacter were enumerated by the most probable number (MPN) technique. For Azospirillum population, soil suspensions were inoculated into tubes containing semi-solid N-free malic acid medium. Based on the number of positive tubes, the population of Azospirillum was estimated using a MPN table. Phosphobacteria population was estimated on Pikovskaya agar medium with serial dilutions of soil. 159

J. Chem. Bio. Phy. Sci. Sec. D ; May 2018 – July - 2018, Vol. 8, No. 3; 156-171 DOI:10.24214/jcbps.D.8.3.15671.]

Influence …

Mandiéré FALL et al.

After four days at 28 °C in the dark, the number of colonies with a clear halo was counted. The population of Phosphobacteria per g of soil was expressed in CFU (colony forming units) relative to the weight of dry soil. Soil enzymes assays: FDA hydrolysis was performed as described by Adam and Duncan 25. Briefly, 2 g soil was placed in a 50 ml conical flask and 15 ml of 60 mM potassium phosphate buffer pH 7.6 were added. Controls were prepared without the addition of the FDA substrate along with a suitable number of sample replicates. The fluorescein released during the assay was extracted with chloroform/methanol (2:1, v/v) and measured at 490 nm using a spectrophotometer (Spectronic 401, Spectronic Instruments, France). Results were expressed as mg Fluorescein released kg-1 h-1. Acid phosphatase (EC 3.1.3.2) activity was determined using p-nitrophenyl phosphate (pNPP, 5 mM) as substrate. 400 µl of 1 M universal modified buffer at pH 6 and 100 µl of substrate were added to 100 mg of soil and incubated in an orbital shaking incubator (100 rev. min-1) at 37 °C for one hour. The reaction was stopped by adding respectively, 100 µl of CaCl2 0.5 M and 400 µl of NaOH 0.5 M. The mixture was then centrifuged at 10 000 rev. min-1 for 5 min and the p-nitrophenol (pNP) measured using a spectrophotometer set at 400 nm 31. Soil microbial communities diversity: Soils from the three plots were analyzed separately. Total DNA was extracted by fast DNA spin kit for soil (Mp Biomedicals, llc) from 0.5 g aliquots of soil. After extraction, the DNA was quantified by nanodrop 2000 de thermo Fisher scientific. PCR amplification targeting V3 region of 16S rDNA bacterial community was performed using the eubacterial primer pair EUB338f-GC (5’-ACTCCTACGGGAGGCAGCAG-3’) 32 and UNIV518r (5’ATTACCGCGGCTGCTGG-3’) Ovreas et al.33. PCR was done in a model 9700 thermal cycler (Perking-Elmer, France) using Ready To Go Beads (Amersham Pharmacia, France). 10 ng DNA was used per PCR reaction tube with 0.5 μM of each primer in 25 μl PCR mixtures. The PCR mixtures were submitted to the following thermal cycling conditions: 2 min at 94°C (denaturation); 30 s at 65°C (annealing), and 1 min at 72°C (extension), the annealing temperature decreased from 65 °C to 55 °C (touchdown) by 0.5°C degree per cycle (20 cycles), followed by 10 cycles with an annealing temperature of 55°C. Amplicons from PCR were separated on a 2% agarose gel in Tris-borate-EDTA buffer (89 mM Tris-borate, 2 mM EDTA) and stained for 30 min with ethidium bromide (1 mg l -1). DGGE was performed by using 8% acrylamide gels [acrylamide-bisacrylamide 40% (37.5:1)] with a 45 to 70% denaturant gradient, where 100% denaturant was defined as 7 M urea plus 40% formamide. Electrophoresis was performed in 1X TAE buffer at 60°C at constant voltage of 100 V for 18 h by using the Ingeny Phor2 system. The gels were stained for 20 min with ethidium bromide and washed for 10 min with H2O prior to UV transillumination. Banding patterns were visualized on a Dark Reader and were digitized by using a charge-coupled device camera and the Bio-Capt software program (Vilber Lourmat, France). The structural diversity of the microbial community was examined by the Shannon of general diversity H’ 34 and Simpson index 35. They were calculated from the following equations: H’ (Shannon and Weaver index) = -Σ (Pi log Pi), with Pi = ni/N; D (Simpson index) = Σ Pi2 , ni = intensity/volume of

a band = number of individual of some population into the community ; N = sum of all bands = total number of community's individuals. Statistical analysis: The data was subjected to a two-way-ANOVA analysis to assess the effect of depth, mining and their interaction on measured variables. Student-Newman-Keuls´s post-hoc test was used to determine significant differences (p ≤ 0.05) among horizons for variables. Student's t test was performed to compare before and after mining on parameters measured. Analyses were carried out with the XLSTAT Premium software version 2017. 160

J. Chem. Bio. Phy. Sci. Sec. D ; May 2018 – July - 2018, Vol. 8, No. 3; 156-171 DOI:10.24214/jcbps.D.8.3.15671.]

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Mandiéré FALL et al.

RESULTS Effect of mining process on the physical and chemical characteristics of dune soils: Results showed that depth had no significant effect (p > 0.05) on physical and chemical characteristics of dune soils (Table 1). However, the mining process had a significant (p < 0.05) effect on these parameters with the exception of total carbon (C), total nitrogen (N) and organic matter (OM). The interaction between depth and mining process had no significant (p > 0.05) effect on these parameters except the potassium (K) (Table 1). Table 1: Significance level obtained from two-way ANOVA testing the effects of mining process, depth and their interaction on physical and chemical characteristics of dune soils

Parameters pHH2O pHKCl Electrical conductivity (EC) Available Phosphorus (av. P) Total phosphorus (to. P) Total carbon (C) Total nitrogen (N) Organic matter (OM) C/N Calcium (Ca2+) Magnesium (Mg2+) Potassium (K+) Sodium (Na+) Cation Exchange Capacity (T) Saturation rate (V) Iron (Fe) Manganese (Mn) Copper (Cu) Zinc (Zn) Clays Silt Fine sand Medium sand Coarse Sand

Depth

Mining Process Depth*Mining Process

p-value 0.814 0.869 0.997 0.758 0.758 0.479 0.155 0.479 0.668 0.557 0.375 0.835 0.299 0.529 0.992 0.957 0.532 0.322 0.987 0.986 0.442 0.191 0.503 0.513

p-value 0.000 0.000 < 0.0001 < 0.0001 < 0.0001 0.234 0.152 0.234 0.000 < 0.0001 < 0.0001 0.000 < 0.0001 < 0.0001 0.002 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.001 0.000 < 0.0001 0.000 < 0.0001

p-value 0.611 0.691 0.863 0.683 0.683 0.278 0.119 0.278 0.780 0.440 0.110 0.038 0.107 0.175 0.519 0.957 0.532 0.322 0.987 0.081 0.433 0.732 0.832 0.814

Table 2 showed the independent effect of mining process on physical and chemical characteristics of dune soils. Results showed that the mining process decreased significantly electrical conductivity (EC), calcium (Ca), potassium (K), manganese (Mn), copper (Cu), zinc (Zn) and cation exchange capacity (CEC) of dune soils. No trace of Mn, Cu and Zn was obtained after mining. The mining process increased significantly pHH2O, phosphorus (P), magnesium (Mg), sodium (Na), iron (Fe), saturation rate, clays, silts and coarse sands of dune soils. Phosphorus content was increased tenfold after the mining process. No significant effect of mining process was observed on C, N and organic matter (OM) (Table 2) 161

J. Chem. Bio. Phy. Sci. Sec. D ; May 2018 – July - 2018, Vol. 8, No. 3; 156-171 DOI:10.24214/jcbps.D.8.3.15671.]

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Mandiéré FALL et al.

Table 2: Physical and chemical characteristics of dune soils before and after mining process

Parameters pHH2O pHKCl Electrical conductivity (EC) Available Phosphorus (av. P) Total phosphorus (to. P) Total carbon (C) Total nitrogen (N) Organic matter (OM) C/N Calcium (Ca2+) Magnesium (Mg2+) Potassium (K+) Sodium (Na+) Cation Exchange Capacity (T) Saturation rate (V) Iron (Fe) Manganese (Mn) Copper (Cu) Zinc (Zn) Clays Silt Fine sand Medium sand Coarse Sand

Units µS/cm ppm ppm % %) % Meq/100 gr Meq/100 gr Meq/100 gr Meq/100 gr Meq/100 gr % Meq/100 gr Meq/100 gr Meq/100 gr Meq/100 gr % % % % %

Before 6.02b 5.32 a 119.17 a 3.12 b 1.36 b 0.21 a 0.02 a 0.36 a 11.11 a 1.60 a 0.08 b 0.04 a 0.13 b 2.20 a 87.93 b 0.00 b 0.79 a 0.14 a 1.57 a 0.51 b 0.37 b 1.35 a 59.95 a 37.83 b

After 6.37 a 5.74 a 43.10 b 30.82 a 13.46 a 0.19 a 0.02 a 0.33 a 9.77 b 0.31 b 0.29 a 0.03 b 0.24 a 0.93 b 92.89 a 9.25 a 0.00 b 0.00 b 0.00 b 0.74 a 0.52 a 0.61 b 52.89 b 45.24 a

p-values 0.045 0.057 < 0.0001 < 0.0001 < 0.0001 0.349 0.069 0.349 0.074 < 0.0001 < 0.0001 0.003 < 0.0001 < 0.0001 0.209 < 0.0001 < 0.0001 0.001 < 0.0001 0.026 0.027 0.002 0.032 0.021

Values within a line followed by the same lower case letter comparing before and after mining are not significantly different at P

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