Trace Elements in Rainfall Collected Around Menengai Area Kenya

Proceedings World Geothermal Congress 2015

Melbourne, Australia, 19-25 April 2015

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Trace Elements in Rainfall Collected around Menengai Area Kenya

Gabriel N. Wetang’ula and Henry M. Wamalwa

Geothermal Development Co. Limited, P.O. Box 100746-00100, Nairobi Kenya

[email protected], [email protected]

Keywords: Precipitation, Trace elements; Acidity; Enrichment factors; potable, Menengai, Kenya

ABSTRACT

Trace element concentrations were measured in precipitation collected from five sites in the Menengai geothermal area, central

Kenyan rift region: two sites in the Centre of the caldera (Menengai Well 2, MW2 and Mlima Punda, MPA), two at the caldera

edge, at Laydown Meteorological station (MTS) and Menengai Viewpoint (MVPT), and one in the northern part outside the caldera

at Rigogo (RIGGO), between November 2011 and June 2012. The concentrations at these sites were compared with precipitation

collected at a control station 30 km from study area at Egerton University Meteorological station. All samples were analyzed for

trace metals (Al, Cd, Cr, Cu, Zn, Ni, Pb, Mn, B, As, Se and Mo). The majority of the rain samples collected had pH values higher

than 6.2. The mean, median and mode pH was 6.58. High values of pH were attributed to the neutralization by natural alkaline local

dusts which contain large fractions of calcite. Concentrations of measured trace elements species were within similar

concentration range as those reported for other sites worldwide. Sources of the trace elements were also assessed based on

enrichment factor method. The trace elements of anthropogenic origins (Hg, Zn, As, Se, Mo and Cd) were highly enriched with

respect to crustal composition indicating the influence of anthropogenic input for these metals. The concentrations of trace metals

that were determined in the rainwater samples were relatively low compared to World Health Organization drinking water quality

guidelines hence potable for domestic use through rainwater harvesting.

1 INTRODUCTION

Rainwater chemistry has been the subject of intense research for the last two decades due to increased awareness of environmental

problems caused by acid rain. Concerns about air pollution have emerged to be significant social and scientific issues in developing

countries due to rapid economic development and consequent increase in energy consumption. The southern area of China has

become the third region in the world seriously affected by acid rain during the past several decades following Northeast America

(Galloway et al., 1976; Khwaja and Husain, 1990; Heuer et al., 2000; Ito et al., 2002) and Central Europe (Almomani et al., 1998;

Marquardt et al., 2001; Topcu et al., 2002). Rainwater compositions play an important role in scavenging soluble components from

the atmosphere and helping understand the relative contributions of different sources of atmospheric pollutants. Various sources of

the dissolved components in rainwater can generally be divided into three groups: (a) sea salt aerosols; (b) terrestrial aerosols (soil

dust, biological emissions); and (c) anthropogenic inputs (industry, agriculture and fertilizers, combustion of fossil fuels) (Negrel

and Roy, 1998).Concentrations of many compounds present in the atmosphere have been significantly affected by human activities

such as industry, agriculture, burning of vegetation and fossil fuel (Nègrel and Roy, 1998). Quantification of these changes and

their effect on terrestrial and aquatic ecosystems is important because of their potential adverse effects (Nriagu and Davidson,

1986).

Rainwater functions as a major sink for both gaseous and particulate matter including trace metals in the atmosphere and therefore

plays an important role in controlling the concentrations of these species. The chemical composition of atmospheric depositions is

the signature of numerous physical and chemical mechanisms of aerosol and precursor emissions, transport, chemical reactions, and

removal processes. Therefore, the chemistry of wet depositions can provide insights into temporal evolution of atmospheric

pollution, and be used as a pertinent indicator to evaluate natural process versus anthropogenic influences. This is because rainwater

compositions play an important role in scavenging soluble components from the atmosphere and helping understand the relative

contributions of different sources of atmospheric pollutants (Tang et al., 2005; Özsoy et al., 2008; Yang et al., 2011). Scavenging of

the atmospheric pollutants by rain affects the chemical composition and the pH of rainwater. The degree of acidity in rainwater

depends on the neutralization effects of certain components such as ammonia and calcium carbonate and/or hydroxide on the acidic

ions present in the water (Kulshrestha et al., 1995a, b).

Determination of rainwater composition reveals the relative importance of the different sources/types of gases and particulate

matter present in the atmosphere. Determination of background concentration levels of these pollutant in precipitation becomes

useful tools in diagnosis where possible environmental impact caused in a specific zone is required and, establishing a comparison

when reference laws or standards of non-criteria pollutants are not available. Commonly, these values are established for remote

areas with a minimal anthropogenic influence, by this way, it is possible to accomplish a diagnosis of impact caused by non criteria

pollutants. In fact, research on precipitation chemical composition of probably impacted zones, allow us to identify, first if there is

an acidity problem, and second, to identify the main sources contributing to this acidity by means of correlation between elements,

local meteorology and anthropogenic sources. Therefore, to assess the impact from geochemical and anthropogenic sources (human

activity, volcanic activity, and crustal) on chemical composition of rainwater in a specific region, knowledge of the background

levels of wet deposition is critical for new project areas.

Precipitation is an efficient pathway for removing the gases and particles from the atmosphere. It also plays a significant role in

controlling the concentration of these species. Incorporation of S and N oxides in wet deposition is particularly important as they

are precursors of major acids (H2SO4 and HNO3) (Voldner et al., 1986; Minoura and Iwasaka, 1996). Therefore, precipitation

chemistry measurements play a critical role in defining both the level of acid deposition and the state of some important

biogeochemical cycles of the earth-atmosphere system (Moody et al., 1991). Precipitation composition thus is an integral

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Wetang’ula and Wamalwa

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measurement and helps to understand the relative importance of the different sources of these materials (gases and particles).

Because of this concern, precipitation chemistry in both rural-continental and urban areas has been the subject of intense research in

the last two decades (Smirnioudi and Siskos, 1992; Samara et al., 1992; Sanusi et al., 1996; Raper and Lee, 1996).

An important feature of the environmental assessment studies is the identification of all possible sources (biogenic, geochemical,

natural, and anthropogenic) to determine their relative contribution to the rainwater chemistry. Human activities have an important

effect on global and regional cycles of most of elements (Nriagu and Pacina, 1988); however, geochemical sources may also have

an important effect (Duce et al., 1991). For this reason, it is essential to discuss the origin of major elements to identify their main

sources. A method commonly Wetang’ula et al. 2008used by geochemists to relate an element in rainwater with its geochemical

source, involves the use of a tracer derived from the specific reference source. Therefore, to assess the enrichment of an element

relative to its source, it is necessary to define the excess (fraction with an origin different from reference source) in terms of an

enrichment factor (EF). Chemicals present in the atmosphere influence the chemical characteristics of rainwater whereas the

material deposited by the rain affects soil, surface water and vegetation.

At present, there are more than 1000 stations conducting precipitation chemistry measurements around the world. All these working

stations may be categorised into Global, Regional and Local Networks. Global Networks (Global precipitation chemistry project)

sites, collect precipitation at remote areas and provide worldwide information on the background concentration of air pollutants and

long range transport of trace substances in the atmosphere while the regional networks in Europe, North America and various other

countries aim at documenting the spatial patterns and long-term trends in deposition of acidic materials. A large number of local

networks are operated in several countries, which address specific scientific questions, abatement strategies and pollution issues.

Data on the chemistry of precipitation in remote areas offer a large potential for clarifying the effects of urban, industrial and

agricultural emissions, and to understand the processes that control the chemical composition of the atmosphere in remote regions

and the natural biogeochemical cycles.

With regard to mercury wet deposition monitoring around the world, there are currently, over 102 Mercury Deposition Network

(MDN) sites in North America of which 92 sites are in the United States, 8 sites in Canada and 2 sites in Mexico. Mercury wet

deposition is currently being monitored at these sites (National Atmospheric Deposition Program, 2007). Estimating mercury wet

deposition fluxes is important for better understanding the influence of atmospheric mercury on mercury biogeochemical cycling

and it’s potentially adverse effects in aquatic ecosystem (Harris et al., 2007; Gray 2006).

In Kenya studies on wet deposition fluxes of airborne chemicals are limited. In 2008 decisions were made to develop the Menengai

area for harnessing geothermal energy (). As an important part of an environmental assessment study in Menengai area,

precipitation concentrations for mercury, major and trace elements plus wet deposition fluxes of THg were measured from

November 2011 to June 2012 at 5 sites. Mercury was of major interest because geothermal power plants have received increasing

attention as regards the mobilisation of mercury to the environment. Mercury as a trace element that may be present in the

geothermal fluid, and is, due to high volatility, transferred mainly into the vapour phase alongside other non-condensable gases

(Robertson et al., 1977; Vitolo and Seggiani, 2002). Since the non-condensable gases present in the geothermal fluid are generally

characterized by a reducing behaviour, mercury is prevalently emitted as elemental mercury (Hg°) (Robertson et al., 1977; Vitolo

and Seggiani, 2002). Mercury (Hg) is a persistent, hazardous air pollutant and bioaccumulative neurotoxin. Atmospheric deposition

is widely recognized as a dominant mechanism by which Hg enters terrestrial and aquatic ecosystems (Landis and Keeler, 2002;

Hammerschmidt and Fitzgerald, 2006) where, upon conversion to the organic form methylmercury, it can bioaccumulate within the

food chain (Schroeder and Munthe, 1998).

Mercury in the atmosphere exists in three major forms: elemental mercury (Hg0), reactive gaseous divalent mercury (RGM) and

particulate mercury (PHg). PHg can be formed by physical adsorption of Hg(II) to atmospheric particulate matter in aqueous phase

as the secondary particulate mercury (Wen 2006). Hg0 represents more than 95% of the total amount and due to its low solubility in

water, Hg0 must first be oxidized before it is efficiently deposited by atmospheric wet and dry depositional processes (Lindberg et

al., 2001). Therefore, mercury in precipitation mainly comes from the scavenging of PHg and RGM in air (Guentzel et al., 2001).

Most of the mercury in atmospheric wet deposition is in ionic form and less than one percent of the total mercury is in MeHg form

(Mason et al., 1997), and therefore atmospheric deposition of MeHg is generally thought to be a minor contributor to MeHg

contamination in aquatic ecosystem (Mason et al., 2000). In the aquatic environment, inorganic mercury can be methylated by

microorganisms to methylmercury and accumulated in the tissues of organisms. It is the ingestion of methylmercury via fish

consumption that poses the greatest risk of exposure to the general public and, therefore, has the greatest potential for adverse

environmental and human health effects. This mercury transformation has been summarized by Mason et al., 1994.

On the other hand, Nakuru and Menengai’s water resources depend mostly on rainwater for groundwater recharge; therefore

rainwater quality is an important factor. The same is true for the residents who depend on rainwater harvesting for domestic use due

to water scarcity in the project area and high fluoride content in groundwater sources that leads to community environmental health

problems such dental fluorosis. Due to this reason, understanding the rain chemistry in the Menengai geothermal project area was

considered necessary e.g. in assessing potential geothermal influence on the THg concentration in the rain, but Hg is a chemical

constituent in geothermal steam that sometimes is emitted into the atmosphere. For other trace elements in precipitation, it is vital to

establish the baseline rainwater quality, preclude the current geothermal well drilling activities being undertaken in the area with

regard to trace element concentration levels in the precipitation and establish a yard stick for future reference when geothermal

development in the area progresses to geothermal power plant generation.

The present paper describes an analysis of rain water collected in the Menengai area and nearby Nakuru, a provincial capital of Rift

valley Province, Kenya. The Menengai geothermal area is a green field (an area of geothermal development at exploratory drilling

and well discharge stage) with no geothermal power generation yet taking place. The field is however currently undergoing

geothermal exploratory well drilling and discharge testing. The geothermal area is surrounded by agricultural fields and deciduous

trees. Nakuru town, located south of the geothermal area has several industries including battery manufacturing, soap and cooking

fat processing units, maize flour milling, milk processing, chemicals and engineering, and quarrying, all using common fuels like


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wood and oil. Emissions from these industries include CO, SO2, NOx gases and particulate matter. In addition, the population of

Nakuru town (473,280 people) and the surrounding area (1495.3 km2) has witnessed increased vehicular density.

In 2011-2012, Geothermal Development Company Limited initiated a precipitation monitoring program in and around the

Menengai area to evaluate the magnitude as well as temporal trends and, if feasible, to identify sources of toxics substances. As an

important part of the study, precipitation concentrations for trace elements were measured from November 2011 to June 2012 at 5

sites in and near the Menengai caldera and at two control sites. This study also provides the first baseline information on the status

of mercury and other trace elements in precipitation around Menengai geothermal area. The results are compared with those in

other regions and countries and the possible sources contributing to observed chemical composition are discussed. The study will

significantly contribute to the very limited knowledge available on precipitation chemistry and rainwater quality for domestic use in

the Menengai area.

2 MATERIALS AND METHOD

2.1 Site description

Menengai Caldera geothermal area is one of many geothermal fields in the Kenyan Rift Valley. The caldera is located in the Rift

Valley Province, in larger Nakuru District (at 35 28’, 3536’E, and 0o13’, 110’S),which is one of the eighteen larger districts of

the Rift Valley Province (Figure 1) The larger Nakuru district has recently been subdivided administratively, placing a large section

of Menengai caldera in the Rongai and Nakuru North districts. The Menengai caldera lies north of Lake Nakuru and forms the

northern divide of the lakes’ catchment basin (Figure 1). The caldera is the remnant of a volcano that erupted about 200,000 years

ago with the last eruption occurring around 1,400 years ago. It rises to a maximum height of 2,278 masl and covers an area of 90

km2. With a diameter of 12 km, it is the second largest in Africa after Ngorongoro in Tanzania. Rainfall has been recorded for the

last 32 years (1980-2011) near the Menengai caldera, i.e. at Nakuru Meteorological Station (Station ID 9036261, co-ordinate 36o

6.243E, 0o 16.23S which is about 1.5km outside the Menengai caldera edge (Figure 1). The annual rainfall ranges from 500-1450

mm with an average of 948.2 mm. The rain falls mainly in two periods, the long-rains season in March to June and the short-rains

season is in October/November. The dry season usually lasts from December to February often bringing strong winds in the caldera

with frequent fire outbreaks. Average monthly temperatures range from 15C-21C over the year.

2.2 Precipitation Sampler and Sampling Procedure

Precipitation samples were collected from within and around the Menengai caldera geothermal field a rural area approximately

175km to the North of the capital of Kenya, Nairobi. Precipitation sampling locations were set up in five different areas. The

Menengai caldera area had four sampling sites, one at Menengai geothermal well number 2 (MW-02), one at Mlima Punda area

(MPA), one at Menengai View point area (MVPT) and another site at Menengai Weather station (MTS) (Figure 1). Except for

Nakuru town/municipality located just south of the caldera the surrounding area is essentially rural, consisting of open areas with

scattered houses at distances varying from 100 to 200 m and agricultural fields.

The other sampling sites were situated some distance away from the Menengai caldera, one at Rigogo approximate 6km north of

the caldera, one at the Kericho High School (KRC) approximately 140 km west of Menengai, one at the Egerton University(EU) in

Njoro approximately 30km southwest of Menengai and one at Kinangop (KINGGO) approximately 110km southeast of Menengai.

For this study, we used site EU as a reference point as it had more rainfall events compared to far away sites like KRC and

KINGGO. These three sites are located in rural areas, remote from heavy industrial activities and significant population centers.

There was some light industrial activity in the vicinity of the MW-02, MPA and MTS sites, mainly geothermal well drilling and

well discharge testing. The sampling sites at EU, MTS and MPA are equipped with meteorological monitoring equipment (manual

and/or automatic weather stations).

Event-based bulk precipitation samples were collected using improvised precipitation samplers mounted on the top of a 1.5 m

sampling platform located at the sampling sites (Figure 2). Event based sampling was used due to the fact that they could help

establish any correlation between variables being considered and activities in the study area. The decision to deploy precipitation

collectors was based on the weekly rainfall forecast from the Kenya Meteorological Department and direct observation of the

prevailing weather conditions in the project study area. The purpose of the 1.5 m platform was to limit the influence of locally

generated soil splash during rainfall. The samplers consisted of four polyethylene terephthalate (PET) components: (1) a funnel - 15

cm diameter at the top, (2) a connecting tube, acting as a capillary to prevent the diffusion of the precipitation sample as well as the

volatilization of volatile elements from sample, (3) a sampling bottle/jar (500 ml volume), and a plastic enclosure inform of a

colorless plastic bucket (Figure 2).

Before deploying all components of the sampler were acid cleaned to prevent possible contamination. Cleaning was conducted

according to trace metal cleaning protocols. All funnels, tubes and bottles were cleaned rigorously by dipping in dilute acid (0.1N

HNO3), rinsing with ultrapure deionized water and then placed in separate plastic bags, stored in plastic boxes until use. The bulk

deposition sampling funnel was left uncovered for the entire deployment period. The funnels with a 176 cm2 collection area are

commercially available and are equipped with a fine mesh filtering disk to prevent leaves and insects falling into the sample. The

receiving bottles/jars were further shielded from UV radiation during deployment by the colourless plastic buckets.

Samples were collected on event based basis, and the sampling components were cleaned after every sampling before redeployment

for subsequent sampling during the entire sample period from December 2011 to end of June 2012. At the same time, the

precipitation samples were collected immediately after each rainfall event within a 24 hr period i.e. at 0900 hrs when the sun is not

too warm, thus preventing sample evaporation. Samples collected at each site were immediately filtered (using disposable Syringes

Filters (CA) with 0.20µm pore size membrane Grade - 25 CS020AS and pressure limit 0.51 mpa) and poured into two 100 ml PET

bottles, then transported to the laboratory and stored in a refrigerator until analysis. One full 100 ml PET bottle was used for

trace/major elements analysis while the other sample was analysed for physico-chemical parameters such as pH and conductivity.

Although we recognize that there may have been evaporative losses after sampling, we assumed losses to be insignificant. A total

of 70 rainwater samples were collected during the study period.


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Figure 1: A map showing the location of the Menengai caldera and (above) the locations of the precipitation monitoring

sites: Menengai geothermal well (MW-02); Mlima Punda area (MPA); Menengai View point area (MVPT);

Menengai Weather station (MTS); Rigogo (RIGGO); Kericho High School (KRC); Egerton University (EU) ;

Kinangop (KINGGO) and Nakuru Meteorogical station (NMTS). The respective site coordinates are also provided.

Figure 2: Precipitation chemistry sampling apparatus. Sampling platform top measuring 30cmx30cm and a stand of 1.5m

above the ground


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2.3 Analytical Methods

The pH, Temperature, TDS and Conductivity values of the collected samples were measured for unacidified samples using the

Hanna HI 255 pH, Conductivity/Temperatures Meter equipped with a combination glass electrode using a reference (KCl and glass

electrodes standardized with a pH of 4.0 and 10.0 reference buffers before pH determination). Unacidified samples were also

analyzed for Cl- and SO42- by UV-visible spectrophotometry using a UV- Shimadzu1800.

Inductively coupled plasma mass spectrometry (ICP-MS, Perkin Elmer, Model Elan-6000) was used to analyze trace metals.

Detection limits for the respective parameters were <0.01µg/L for Hg and Cd, <0.1 µg/L for Ni, Se ad Ba; <0.02 µg/L for As, <0.03

µg/L for Mo, <0.05 µg/L for Cr, <0.08 µg/L for Pb, <0.4 µg/L for Cu, <0.7 µg/L for Zn, <1.0 µg/L for Al, and < 10.0 µg/L for B.

While detection limits for the respective major elements were <0.3mg/L for Ca, <0.2 mg/L for Na, <0.02 mg/L for K, <0.01 mg/L

for Mg and < 1.0 µg/L for Al. The samples were analyzed at the Geochemistry Laboratory Facility of British Geological Survey

(BGS), (Nottingham, United Kingdom) for THg, trace and major metals.

2.4 Quality Control and Assurance

Precautions were taken to avoid contamination of samples in both field and laboratory, and to ensure the reliability of our

determinations. Prior to installation, the funnel and collection bottles were carefully cleaned and dried in a clean laboratory. An

analysis of blanks (deionized water) passed through the funnel to the collection bottles showed that there was negligible

contamination. After each precipitation event the collection bottles were removed and replaced with others that had undergone the

washing procedure.

2.5 Statistical Analysis

ANOVA test was used as a data analysis tool to compare differences of means between different sampling sites and months

(groups) especially comparing precipitation samples between control station (EU) and Menengai area sampling sites during the

study. Pearson product-moment correlation coefficient, also known as r, R, or Pearson's r, a measure of the strength and direction of

the linear relationship was also used to as a measure of the degree of linear dependence between trace and major elements

concentrations in Menengai area precipitation.

3 RESULTS

3.1 Precipitation pH and Conductivity

The pH of the rainwater ranged from 5.89 to 7.17 (average 6.58 with a 95% Confidence Limit of 0.065), which is higher than the

pH of typical natural rainwater (5.6) (Seinfeld and Pandis, 2006) (Table 1). With only 1% of the precipitation recorded can be

categorized as slightly alkaline (5.6-6.0). Samples with very alkaline (>7.0) of about 24% was observed, indicating the presence of

strong influence of precipitation by CaCO3 basic species, notably Ca2+. These results indicate a strong influence of alkaline soil

dust on rainwater in the Menengai area resulting in Mean and Median pH of 6.58, slightly much higher than pH 5.6 of normal

rainwater (Charlson and Rhode, 1982). Statistical analysis revealed no spatial (P value = 0.950114) and temporal (P value =

0.524773) variation in the pH of the rain samples.

Table 1: pH frequency and percentage distribution for the rainfall events.

pH Frequency Percentage composition (%)

5.6 0 0

5.8 0 0

6 1 1

6.2 7 10

6.4 13 19

6.6 16 23

6.8 16 23

7 14 20

7.2 3 4

Conductivity in Menengai area ranged from 3.49 to 59.5 µS/cm, with an average value of 16.79 µS/cm. For sampling site further

away from Menengai area, the conductivity ranged from 4.26 to 27.5 µS/cm, 3.26 to 5.14 µS/cm and 6.33 µS/cm for Egerton

University (EU), Kericho High School (KRC) and Kinangop (KINAGGO) sites respectively. The average precipitation

conductivity for Egerton University (EU) that had more precipitation events samples far away site Kericho High School (KRC) and

Kinangop (KINAGGO) was 11.54 µS/cm. There was no spatial (P value = 0.369056) and temporal (P value = 0.118249) variability

in conductivity values of the rain samples

3.4 Trace element concentration in the precipitation

The summary statistics of concentrations of the 13 trace elements determined in the 57 rainwater samples is presented in Table 2.

There was no significant variation in trace element concentration across the five precipitation sampling sites and the control site EU

for Al, Hg, Mn, Zn, Cd except for As (p value = 0.03) that had high maximum concentrations. Site MPA has highest average As

concentration of 0.38 µg/L over the monitoring period. Significant trace element concentration variation evident across the months

for Mn (p value = 2.8E-07), As (p value = 0.0023), Zn (p value = 0.003), Cd (p value = 0.0008), and Mo (p value = 0.01). The

variation of concentration of these metals can be explained by the scavenging of pollutants. The variation in the concentrations of


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trace metals between different rainfall events could be related to the source of pollutant emissions. The high concentrations of

metals were measured in rainfalls after periods of dryness in April 2012, while low concentrations of metals were measured when

rain continued for several days. Gratz et al 2009 study also observed that the highest concentrations were typically observed with

low precipitation amounts, suggesting that most trace elements are removed during the onset of precipitation, and additional

precipitation acted to dilute samples throughout the remainder of the event.

Table 2: Levels of trace elements concentration in rainwater (µg/L) around Menengai geothermal field

Trace Element Mean Median Minimum Maximum

Sample Size

(n=57)

Al 36.79 10.17 0.09 456.25* 57

Hg 0.16 0.16 0.06 0.45 57

B 29.19 29.33 0.6 171.5 57

Cr 0.26 0.18 0.003 1.13 57

Mn 19.72 6.39 0.05 374.63** 57

Ni 0.49 0.35 0.1 1.94 57

Cu 1.27 0.76 0.05 6.92 57

Zn 9304.51 42.56 4.28 116046*** 57

As 0.18 0.1 0.001 1.58**** 57

Se 0.11 0.08 0.03 0.38 57

Mo 0.18 0.09 0.001 1.3 57

Cd 4.34 0.01 0.002 50.29 57

Pb 0.31 0.22 0.01 0.9 57

*MPA had highest maxima with Al at 3 other different sites range being 121.2-300 µg/L; ** One sample at site RIGGO had this

maxima with all other samples at other site being <100 µg/L

*** All sites had highest Zn maxima

**** Site MPA has 2 samples with range 1.44-1.58 µg/L

3.6 Trace Element Enrichment Factor Calculation

In the absence of anthropogenic sources, observed concentrations of metals and ions should be explained by natural sources.

However, emissions from the various man-made sources have drastically increased concentrations of metals in our environment.

Enrichment factors (EF) are normally applied to identify the origin of an element in precipitation (Keene et al., 1986; Ahmed et al.,

1990; Singer et al., 1993; Okay et al., 2002; Safai et al., 2004; Zhang et al., 2007). Enrichment factors (EF) can be calculated to

show the degree of enrichment of a given element compared to the relative abundance of that element in crustal material.

Enrichment factors (Duce et al., 1975) are very good indicators of natural or anthropogenic pollution (Taylor and McLennan,

1985). Therefore we decided to calculate EF for the elements and ions measured in this study thereby obtaining a measure of

enrichment levels in and around the Menengai geothermal area. EF values that surpass contamination levels might also be very

informative, e.g. in identifying potential sources of the element in question. Al and Ca are two typical lithophilic elements normally

used as reference elements for continental crust. Since there was measurement of both Al and Ca elements in the rainwater

samples, Ca and Al were chosen as reference elements for major and trace elements respectively to determine their continental

origin for this study (Safai et al., 2004; Das et al., 2005; Zhang et al., 2007). Using Ca2+ and Al as reference element for soil

source, enrichment factors were calculated as shown in equation 1 (Safai et al., 2004; Das et al., 2005; Zhang et al., 2007):

EF for soil = [X Ca2+or Al⁄ ]rainwater

/[X Ca2+ or Al⁄ ]soil

(1)

where X was the concentration of the target element, X/Ca or Al ratio was from crust composition (Taylor, 1964). Mason’s (1966)

compilation for crustal abundances was used in crustal enrichment factor calculation. Al and Ca were used to calculate EF for trace

and major elements respectively. Total concentrations were used for EF calculations.

Based on the hypothesis that crustal material is the only source of Al and Ca, a value of EFc near 1 for an element indicates that

crustal material is a major source of this particular element. However, EFc values in the range of 1-10 are considered to be not

enriched due to the differences between chemical composition of local soil and reference crustal composition as per Mason's (1966)

compilation. Elements of EFc between 10 and 100 are considered moderately enriched, indicating greater concentrations of a

particular element in the rainwater than would be expected from the crustal material. Finally, EFc greater than 100 show highly

enriched conditions, indicating a severe contamination due to human activities (Duce et al., 1975). Table 3 shows the calculated

enrichment factors of trace and major elements in rainwater samples around Menengai area.

3.7 Pearson Correlation coefficients of measured elements

Pearson’s correlation was applied to the total element concentration analyzed during the study. This was performed to distinguish

the possible common sources of the constituents. The matrix correlations are shown in Table 4 for both trace and major elements;

the significant correlations are marked in bold letters. Common correlating element groups are: Cr-Hg-Al, Ni-Al-Cr, Se-Cr-Mn-As,

Mo-Hg-Cr-Mn-Se, Pb-Al-Cr-Ni, and Cd-Zn. Two trace elements with strong correlation can be selected from these measurement


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data: Cd-Zn (correlation coefficient 0.95). Probably, the above-mentioned groups are typical of the entire Menengai area indicating

their origin from similar sources caused by long-range transfer of air masses. It is also important to note that no correlation was

observed among the trace metals and the SO2-4.

Table 3: Enrichment factors of measured trace elements in precipitation in relation to crustal composition

Trace Element This Study -Mean (ppm) Earth Crust (ppm) Enrichment Factor (EF) Sample Size

(n=57)

Al 0.00078 81300 1 57

Hg 0.00016 0.067 5277.23 57

B 0.02919 8.7 7414.39 57

Cr 0.00026 100 5.75 57

Mn 0.01972 1000 43.58 57

Ni 0.00049 80 13.54 57

Cu 0.00127 100 28.06 57

Zn 9.30451 75 274153.00 57

As 0.00018 2.1 189.41 57

Se 0.00011 0.05 4861.65 57

Mo 0.00018 1.1 361.61 57

Cd 0.00434 0.15 63938.03 57

Pb 0.00031 14 48.93 57

Al 0.00078 81300 0.0002 57

Table 4: Pearson’s correlation among the trace and major metals measured in precipitation around Menengai area.

Hg Al Cr Mn Ni Zn As Se Mo Cd Pb Mg K Ca

Hg 1

Al 0.12 1

Cr 0.56 0.50 1

Mn 0.17 0.07 0.24 1

Ni 0.19 0.48 0.63 0.35 1

Zn -0.17 -0.19 -0.38 -0.09 -0.26 1

As 0.16 0.03 0.39 0.16 0.16 -0.14 1

Se 0.40 0.07 0.57 0.61 0.44 -0.26 0.46 1

Mo 0.50 0.01 0.47 0.60 0.29 -0.20 0.20 0.66 1

Cd -0.10 -0.20 -0.42 -0.11 -0.30 0.95 -0.17 -0.29 -0.23 1

Pb 0.13 0.51 0.57 0.38 0.46 -0.25 0.20 0.39 0.34 -0.32 1

Mg -0.06 0.15 0.12 -0.01 0.10 -0.18 -0.07 -0.02 -0.01 -0.19 0.11 1

K -0.12 0.06 0.07 0.15 0.16 -0.13 -0.04 0.01 0.11 -0.14 0.15 0.81 1

Ca 0.04 0.16 0.25 -0.02 0.08 -0.21 -0.05 0.06 -0.02 -0.22 0.23 0.71 0.36 1

4. DISCUSSION

4.1 Precipitation pH and Conductivity

In view of the rainfall events sampled around Menengai area, there is no rainwater acidification in Menengai area unlike areas

such as Mangdang Mountain in eastern China (Mean pH of 4.8 and 80 % of precipitation events having a pH<5.6, Cheng et al.,

2011); Beijing, China (Mean pH 5.12 and pH range 4.02-7.62; Xu and Han, 2009); Loess plateau, northwest China (Mean pH 7.7

and pH range 6.82-8.28; Xu et al., 2009); Guangzhou, south China (Mean pH 4.49 and pH range 3.52-6.28; Cao et al., 2009);

eastern Uttar Pradesh, India (Mean pH 6.3 and pH range 6.1-6.8, Tiwari et al., 2006); Danum Valley, Malaysia (Mean pH 5.17 and

pH range 4.89-5.90; Sumari et al., 2009); a mining area in Ghana (Mean pH 4.67 and pH range 4.0-5.6; Akoto et al., 2011); on the

summit of an active volcano Mt Etna, Italy (pH range 2.45-5.57; Aiuppa et al., 2006); but similar to that in northeastern Mexico

with mean pH of 6.58 (Lara et al., 2010). Statistical analysis also revealed no spatial and temporal variation in the pH of the rain

samples around Menengai area. The pH of natural precipitation is controlled by dissolved CO2, due to the interaction between water


8

droplets and carbon dioxide. Precipitation pH is modified by the addition of both acidic and alkaline components (Topҫu et al.,

2002; Singh and Mondal, 2008).

Conductivity of precipitation in the Menengai area was much higher than the range and average conductivity of the precipitation in

areas such as at the global atmospheric watch station in Danum Valley Malaysia (Range 3-21 µS/cm, Mean 8.30 µS/cm; Sumari et

al., 2009) but lower than that in areas such as Mining region in Ghana (Range 7.15-59.6 µS/cm; Akoto et al., 2011); Beijing and

Loess plateau, Northwest China (Range 13-257 µS/cm; Xu and Han, 2009; Xu et al., 2009). Precipitation conductivity is mainly

contributed by water-soluble ions, the value being related to the total sum of anions and cations in the rainwater. Low precipitation

conductivity is an indicator of good atmospheric environmental quality.

4.2 Trace element concentration in the precipitation and worldwide comparison

In the Menengai area, significant trace and major element concentration variation was evident across the sampling months (March-

June 2012) for As, Cd, Zn, Mn, Mo, Mg, K and Ca. The variation of concentration of these elements can be explained by the

scavenging of pollutants. The variation in the concentrations of trace and major elements between different rainfall events could be

related to the source of pollutant emissions. The high element concentrations were measured in rainfalls after periods of dryness in

April 2012, while low concentrations of elements were measured when rain continued for several days. Gratz et al 2009 study also

observed that the highest concentrations were typically observed with low precipitation amounts, suggesting that most trace and

major elements are removed during the onset of precipitation, and additional precipitation acted to dilute samples throughout the

remainder of the event. SO42-, Ca, Na and K were the dominant species accounting for 98% of the total species, which were similar

to the results in similar studies in China (Zhang et al., 2007). SO42− was the highest concentration anion and constituted

approximately 50.0% of the major species which can be attributed to fossil fuel consumption in automobiles and industries as no

correlation existed with other major crustal elements.

Galloway et al. (1982) and Jickells and Knap (1984) summarized trace metals concentrations in precipitations collected worldwide

and Bermuda respectively. Dasch and Wolff (1989), Baeyens et al. (1990), Kaya and Tuncel (1997), Ayodele and Abubakar

(1998), Freydier et al. (1998), Takeda et al. (2000), Kanellopoulou (2001), Al-Momani (2003), Deboudt et al. (2004) and

Migliavacca et al. (2004) summarized more recent values for those elements commonly analyzed in precipitation. For the most part,

our data were in good agreement with the preceeding cited authors, with few exceptions. The concentration of Cd, Cu, Zn, Mn and

Al were higher than those reported Kano, Nigeria (Ayodele and Abubakar, 1998), Bermuda (Jickells and Knap, 1984), Hiroshima-

Japan (Takeda et al., 2000) and Northern Jordan (Al-Momani, 2003). Zn and Mn concentration was higher in Menengai

precipitation compared to all reviewed literature. However, concentrations of some measured species were lower than those

reported by other authors. For example, Cd concentration was much lower than levels reported in North Sea (Baeyens et al., 1990).

Menengai precipitation Cr concentration was lower than those reported for North Jordan (Al-Momani, 2003), Ankara Turkey (Kaya

& Tuncel, 1997) and in worldwide review (Galloway et al. 1982). Cu and Ni concentration in the precipitation was lower than

those reported for Intertropical Africa (Freydier et al., 1998), North Sea (Baeyens et al., 1990), Northern Jordan(Al-Momani, 2003),

Athens Greece (Kanellopoulou, 2001), South Brazil (Migliavacca et al., 2004) and in worldwide review (Galloway et al. 1982). Pb

and Al concentration in Menengai precipitation was much lower than all other areas cited in the reviewed literature.

4.3 Trace Element Enrichment in Precipitation

Enrichment factors (EF) are normally applied to identify the origin of an element in precipitation (Keene et al., 1986; Ahmed et al.,

1990; Singer et al., 1993; Okay et al., 2002; Safai et al., 2004; Zhang et al., 2007). Enrichment factors (EF) can be calculated to

show the degree of enrichment of a given element compared to the relative abundance of that element in crustal material.

Enrichment factors (Duce et al., 1975) are very good indicators of natural or anthropogenic pollution (Taylor and McLennan,

1985). It is possible to explain this high trace metal enrichment in precipitation of Menengai area compared to other areas. These

trace elements may be derived from soil as materials blown from the ground, volcanic degassing, industrial and domestic pollution.

In this area, the most important emission sources could be the industrial zones of Nakuru town, situated at the north-east, upwind

from the study; which seems to indicate that the transport of contaminants may come from the industrial activities of Nakuru town,

(Pacyna, 1984). The sources of the trace metals could be from the numerous industries located in Nakuru. They include animal

feed processing plants, dairy/food processing plant, cooking oil and soap manufacturing plants, hide and skins tanneries, fuel

oilfired industrial boilers, vehicles motor assembly, paint plants, cleaning products manufacturing, dry and wet battery production

and disposal plant and disposal of lead batteries and many others industries that may contribute to the emissions of heavy metals.

Trace elements Al and Cr were not enriched to higher than 10 indicating that they have mainly originated from the airborne dust.

Although Cr can also have a significant contribution from anthropogenic sources.

The elements B, Mn, Ni, Cu and Pb are moderately enriched compared to the mean crustal composition indicating the presence of

other sources contributing to these elements. The potential sources could be particles from automobile emissions, vehicle wear, and

road dust as they contain significant concentrations of Cu, Pb and Zn (Shahin et al., 2000; Rose et al., 2001; Weckwerth, 2001;

Martuzevicius et al., 2004). Nickel and Mn may also be released from oil-fired furnaces and ferroalloys (Szefer and Szefer, 1986).

Where there is consumption of Li, Co and Ni for use in rechargeable cells, it may imply that Ni in the used cells are not recycled or

recovered hence its potential to contaminate the environment. As a result of the wide uses of copper and lead products, they were

they were present in all the rainwater sampled. Environmental contamination by Pb is widespread, the major anthropogenic source

of this element being the combustion of leaded gasoline (Dimuchowski and Bytnerowicz, 1995; Ghauri et al., 1994). Although its

use has been discontinued in some countries, the use of tetra alkyl lead compound as antiknock additive remains the largest

application of organolead compounds. Vehicular emissions of tetraalkylead are subject to atmospheric breakdown to triakyl- and

dialkylead and all the three forms are scavenged from the atmosphere by rainfall (Hewitt and Harrison, 1986).

The high values of EFc found for Hg, Zn, As, Se and Cd show that these metals in rainwater are non-crustal, because elements such

Cd and Hg are associated with fine particles (<1 µm), which are generated as high-temperature combustion condensates and

infected by smokestacks into the boundary layer (Lindberg, 1982; Scudlark et al., 1994). As for Cd emissions, it is known that in


9

motor vehicles several of their components are Cd alloys, manufacturing and disposal of Cd batteries and the wear of vehicles tires

could also be a source of Cd in precipitation. Natural sources of these elements (besides soils) are volcanoes (Hinkley et al., 1999),

wild forest fires (Chankina et al., 2001; Dennis et al., 2002) and/or biogenic sources (Beauford et al., 1975; Hong et al., 1996).

Local emissions and long-range transport are considered to be the source of the enriched elements. Na, Mg and K had an EF of less

1 suggesting major influence of crustal source.

4.4 Correlation coefficients of measured elements

In the Menengai area, Cd-Zn (correlation coefficient 0.95) was revealed as trace element group with strong correlation from the

precipitation measurement data. It is also important to note that no correlation was observed among the trace metals and the SO2-4.

Halstead et al. (2000) also found that Cd and Zn were enriched in rainwater relative to crustal material and significant contribution

of this element was suggested. Although correlation between species are frequently used to know about the sources of pollutants, it

is necessary to be cautious in the interpretation of correlation coefficients in rainwater data, because the concentration may vary by

scavenging of particles from the atmosphere. Scavenging of elements from the atmosphere by rain depends on sizes and

hydroscopic properties of particles with which elements are associated (Galloway et al., 1993). Therefore, elements which are

emitted from totally different sources may show correlations due to similar physical properties of particles that carry them.

However, source information can be extracted from correlation data bearing these uncertainties in mind.

4.5 Suitability of Precipitation Chemistry for Potable Rainwater harvesting in the area

The Kenya (e.g. Menengai area) just like otherk2 parts of world is currently undergoing water crises and rainwater harvesting can

augment water supply in all sectors. Rainwater harvesting increases food production and hence forms the foundation of many

development projects that promote agriculture and land management (Lundgren, 1993; Hurni and Tato, 1992; WOCAT, 1997). For

instance, rainwater harvesting reduces women's burden of collecting water for domestic use, leaving time for other productive

activities. It also gives opportunity for the girl child to attend school and provides a relatively safe and clean source of drinking

water, minimizing incidences of water borne diseases (Baron and Rockstrom 2003).

Although rainwater harvesting is widespread in the project area and many parts of the world, its quality has been an issue that is

given relatively little importance. This should not be the case especially if this water is to be included in integrated water resources

management. The harvesting of rainwater can either be directly from the atmosphere - in which case the quality is influenced by the

atmospheric conditions, or via runoff in which case the quality becomes a major issue of concern and quality parameters like pH,

Al, Hg, B, Cr, Mn, Ni, Cu, Zn, As, Se, Mo, Cd, and Pb that were investigated in study are reported on the suitability of the

rainwater human use as per the World Health Organization Drinking Water Quality Guidelines (Figure 3, WHO, 1998).

The results indicate that except for Cd, the quality of rainwater around Menengai area conforms to World Health Organization

drinking water quality guidelines and hence acceptable as an alternative source of water for industrial - and - for domestic use.

With Menengai area precipitation being potable, harvesting rainwater will go a long way in providing water for areas whose other

sources are too distant or too costly, or where sinking wells are not practical because of unfavourable geology or excessive drilling

costs. The potential for developing new water supplies by means of rainwater harvesting is in the project area is thus tremendous

and harvesting is practically suited to supplying water for small villages, schools, households, livestock and wildlife in the area.

Figure 3: Comparison of Trace element concentration in rainwater around Menengai area and recommended World Health

Organization drinking water quality guideline.

5. CONCLUSIONS

Trace element concentrations were measured in precipitation collected from five sites in the Menengai geothermal area and a

control station 30 km from study area at Egerton University Meteorological station,in central Kenyan rift region between November

2011 and June 2012. All samples were analyzed for trace elements Al, Hg, Cd, Cr, Cu, Zn, Ni, Pb, Mn, B, As, Se and Mo. The

majority of the rain samples collected had pH values higher than 6.2. The mean, median and mode pH was 6.58. High values of pH

were attributed to the neutralization by natural alkaline local dusts which contain large fractions of calcite. Concentrations of

measured trace elements species were within similar concentration range as those reported for other sites worldwide. Elements of

anthropogenic origins (Hg, Zn, As, Se, Mo and Cd) were highly enriched with respect to crustal composition indicating the

influence of anthropogenic input for these metals. The concentrations of trace metals that were determined in the rainwater samples


10

were relatively low compared to World Health Organization drinking water quality guidelines hence potable for domestic use

through rainwater harvesting thus precluding any effects of current exploratory geothermal drilling and well discharge testing in the

Menengai geothermal area.

ACKNOWLEDGEMENTS

This study was funded by Geothermal Development Company through its Environmental Monitoring Programs Fund. Sincere

thanks go to Henry (Environmental Technologist), Geothermal Development Company, Environment and Central Rift Department

for helping with bulk precipitation sampling.

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Trace Elements in Rainfall Collected Around Menengai Area Kenya

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