Proceedings of the International Academy of Ecology and Environmental Sciences, 2014, Vol. 4, Iss. 4

Proceedings of the International Academy of

Ecology and Environmental Sciences

Vol. 4, No. 4, 1 December 2014

International Academy of Ecology and Environmental Sciences

Proceedings of the International Academy of Ecology and Environmental Sciences ISSN 2220-8860 ∣ CODEN PIAEBW Volume 4, Number 4, 1 December 2014

Editor-in-Chief WenJun Zhang Sun Yat-sen University, China International Academy of Ecology and Environmental Sciences, Hong Kong E-mail: [email protected], [email protected] Editorial Board Taicheng An (Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, China) Jayanath Ananda (La Trobe University, Australia) Ronaldo Angelini (The Federal University of Rio Grande do Norte, Brazil) Nabin Baral (Virginia Polytechnic Institute and State University, USA) Andre Bianconi (Sao Paulo State University (Unesp), Brazil) Iris Bohnet (CSIRO, James Cook University, Australia) Goutam Chandra (Burdwan University, India) Daniela Cianelli (University of Naples Parthenope, Italy) Alessandro Ferrarini (University of Parma, Italy) Marcello Iriti (Milan State University, Italy) Vladimir Krivtsov (Heriot-Watt University, UK) Suyash Kumar (Govt. PG Science College, India) Frank Lemckert (Industry and Investment NSW, Australia) Bryan F. J. Manly (Western EcoSystems Technology Inc. and University of Wyoming, USA) T.N. Manohara (Rain Forest Research Institute, India) Ioannis M. Meliadis (Forest Research Institute, Greece) Lev V. Nedorezov (University of Nova Gorica, Slovenia) George P. Petropoulos (Institute of Applied and Computational Mathematics, Greece) Edoardo Puglisi (Università Cattolica del Sacro Cuore, Italy) Zeyuan Qiu (New Jersey Institute of Technology, USA) Mohammad Hossein Sayadi Anari (University of Birjand, Iran) Mohammed Rafi G. Sayyed (Poona College, India) R.N. Tiwari (Govt. P.G.Science College, India) Editorial Office: [email protected] Publisher: International Academy of Ecology and Environmental Sciences Address: Flat C, 23/F, Lucky Plaza, 315-321 Lockhart Road, Wanchai, Hong Kong Tel: 00852-6555 7188; Fax: 00852-3177 9906 Website: http://www.iaees.org/ E-mail: [email protected]

Proceedings of the International Academy of Ecology and Environmental Sciences, 2014, 4(4): 134-147

IAEES www.iaees.org

Article

Assessment of aerosol-cloud-rainfall interactions in Northern Thailand

V. Tuankrua1,2, Piyapong Tongdeenog3, Nipon Tangtham4, Prasert Aungsuratana5, Pongsak Witthawatchuetikul6

1Graduate School, Kasetsart University, 50 Phaholyothin rd., Chatuchak, Bangkok, 10900, Thailand 2Center for Advanced Studies in Tropical Natural Resources, Kasetsart University, 50 Phaholyothin rd., Chatuchak, Bangkok,

10900, Thailand 3Department of Conservation, Kasetsart University, 50 Phaholyothin rd., Chatuchak, Bangkok, 10900, Thailand 4Forestry Research Center, Kasetsart University, 50 Phaholyothin rd., Chatuchak, Bangkok, 10900, Thailand 5Bureau of Royal Rainmaking and Agricultural Aviation, BangKhen, Bangkok, 10900, Thailand 6Watershed Conservation and Management Office, Department of Natural Parks, Wildlife and Plant Conservation, BangKhen,

Bangkok, 10900, Thailand E-mail: [email protected]

Received 1 July 2014; Accepted 8 August 2014; Published online 1 December 2014

Abstract

Biomass burning in the northern Thailand probably provides strong input of aerosols into the atmosphere, with

potential effects on cloud and rainfall, over an entire burning season. This research was focus on effect of

biomass burning aerosols on clouds and rainfall using multiple regression analysis and AOT for indicating

aerosol concentrations from satellite MODIS (Terra / Aqua) and AERONET station since 2003-2012. The

results indicated that average AOT of the Northern Thailand showed the highest value in pre-monsoon season

especially in March with 0.5 unit less and decreased in June to July. It corresponded with hotspot data were

mostly occurring in pre-monsoon season. Furthermore, almost all of the aerosols that were found during

monsoon season as the big particles, caused by salt spray combine with water vapor. In the other hand, almost

all of the aerosols during pre-monsoon were the small particles which come from the black carbon caused by

biomass burning. There was high positive relationship with rainfall with cloud water content (CWC) and cloud

fraction (CF), but it was found that were negative relationship with aerosol optical thickness (AOT) and

hotspot (HP). There was moderate relationship between rainfall amount with AOT, cloud fraction (CF), cloud

water content (CWC) and hotspot (HP) in all provinces of the northern Thailand. It was noticed that in any

year there were the high biomass burning aerosols which caused rain later than usual about 1-2 months.

Keywords aerosols; AOT; cloud; rainfall; northern Thailand.

1 Introduction

Proceedings of the International Academy of Ecology and Environmental Sciences ISSN 22208860 URL: http://www.iaees.org/publications/journals/piaees/onlineversion.asp RSS: http://www.iaees.org/publications/journals/piaees/rss.xml Email: [email protected] EditorinChief: WenJun Zhang Publisher: International Academy of Ecology and Environmental Sciences


IAEES www.iaees.org

1 Introduction

Aerosols actually act as cloud condensation nuclei (CCN) for cloud water droplets. The changes in aerosol

concentrations have been observed as having significant impacts on the corresponding cloud properties

(Ackerman et al., 2000; Rosenfeld, 2000; Andreae et al., 2004; Kaufman et al., 2005). An increase in aerosol

concentration may lead to an increase in CCN, with an associated decrease in cloud droplet size for a given

cloud liquid water content (Kaufman et al., 1997; Ramanathan et al., 2001). As mentioned (Rogers and Yau,

1989), clouds that form in air containing relatively low concentrations of CCN tend to have a broader droplet

size distribution and a few large drops. Provided the latter drops are large enough (diameter ~ 40 m) they

can grow by collision-coalescence to form raindrops. It is for this reason that marine clouds tend to precipitate

more efficiently than continental clouds. This is the theoretical basis for attempts to increase precipitation by

seeding with large hygroscopic nuclei, which can provide the seeds upon which precipitable particles can grow.

Smaller droplet sizes may then lead to a reduction in precipitation efficiency and an increase in cloud lifetimes

(Rosenfeld, 1999, 2000). However, these effects are highly dependent on the aerosol concentration, aerosol

species, and the meteorological conditions.

In Thailand, there have been more aerosols emissions to atmosphere found in each region and their effects

on livelihood of people, particularly on cloud and rainfall. Hence, the different sources of aerosol such as

biomass burning, soot and salt from sea spray caused different effects on cloud and rainfall characteristics. It

is known that rainfall play essential role on agriculture and industry of Thailand. Aerosols may disperse

rainfall and caused drought in some area of Thailand. Recently, it is still needed to clarify the effects of

aerosols on clouds and rainfall. Consequently, the objectives of this paper are an attempt to particularly explain

the spatial and temporal aerosol characteristics and analyze trend of temporal change on cloud and rainfall

characteristics in Northern Thailand using observed by MODIS data. The specific objectives are following as

(1) To investigate the spatial and temporal aerosol characteristics in northern Thailand (2) To detect

observational evidence of aerosol effects on the spatial and temporal cloud and rainfall characteristics in

northern Thailand (3) To formulate numerical relationships between aerosols with cloud and rainfall

characteristics in northern Thailand.

2 Study area and Methodology

2.1 Study site

The upper northern Thailand was selected as the study area which had high the aerosol source emissions from

forest fires and biomasses burning from agriculture area leading to produce smog problem during the past 5-10

years. Forest fire in this area constantly increased, especially pre-monsoon season or summer season, a cause

of changing in the cloud physics and rainfall. This research covered 9 provinces covering Mae Hong Son,

Chiang Mai, Chiang Rai, Lamphun, Lampang, Phayao, Phrae, Nan and Uttaradit. The area has co-ordinates

with latitude, 17.5 to 20 N and longitude 98 to 101 E.

2.2 Data collection

2.2.1 Aerosol Optical Thickness (AOT) and fire count (hotspot numbers) were collected from Terra/Aqua

MODIS and fire count activity information. The collected data were used to study the variability of aerosol

loading in relation with the enhanced pre-monsoon biomass burning activity as that employed by Levy et al.

(2007). Globally gridded daily and monthly mean MODIS products, with spatial resolution of were obtained

for the period 2003-2012 from the NASA LAADS web portal.

2.2.2 Aerosol particles sizes from The Aerosol Robotic NETwork stations (AERONET) at Chiang Mai during

year 2007 to 2012.

135


IAEES www.iaees.org

2.2.3 Daily and monthly cloud fraction (CF), cloud water content (CWC) and rainfall amount (RF) from

TRMM data during year 2003 to 2012.

2.2.4 Daily particle Matter (PM10) from Air and Noise pollution monitoring stations of from Pollution Control

Department during year 2005 to 2012.

2.3 Analysis data

2.3.1 To determine temporal variation of aerosol using time series analysis. They were shown in monthly

variations and seasonal variations were winter season (Nov-Jan), pre-monsoon season (Feb-Apr) and rainy

season (May-Oct).

2.3.2 Two modes of aerosol size (< 1 micron as fine mode and > 1 micron as coarse mode) were analyzed

determining aerosol particles size distribution using data collected from AERONET station.

2.3.3 To analyzed spatial and temporal aerosols characteristics from MODIS data (Terra/Aqua) using aerosol

parameters to indicate aerosol concentration was Aerosol Optical Thickness (AOT).

Fire counts or Hotspot number from MODIS data during 2003 to 2012 were used for identifying amount and

location of forest fire or biomass burning area.

2.3.4 Seasonal variation of rainfall and cloud water content (CWC) and cloud fraction (CF) derived from

TRMM satellite data during year 2003 to 2012 were analyzed into 2 patterns, (1) Normal day (non-hazy day)

and (2) burning day (Hazy day). Normal day (non-hazy day) was no has hotspot and PM10 less than 120 µg/m3

but burning day (hazy day) was defined by Hotspot day and PM10 data (> 120 µg/m3).

2.3.5 Relationships between aerosol, cloud and rainfall for determining suitable model using multiple

correlations and multiple regression analysis as following are

R = f {AOT, APS, Hot, CF, CWC}

where AOT = Aerosol Optical Thickness (unitless)

APS = aerosol particle size (µm)

R = rainfall amount (mm)

Hot = hot spot (point)

CF = cloud fraction (unitless)

CWC = cloud water content (g/cm2)

3 Results and Discussion

3.1 Spatial and seasonal variation of aerosols in the upper Northern Thailand

This result was separated into 2 parts were 1) variation of aerosol in different areas and 2) variation of aerosol

in different seasons. The details are following;

3.1.1 Spatial variation of aerosols in the upper Northern Thailand

The spatial distribution of monthly mean AOT has been demonstrated for the period from 2003 to 2012 (Table

1, Fig. 1). The average annual AOTs of Northern Thailand were about 0.18-0.32 which implies that moderate

aerosol concentration. In March, almost all provinces in Northern Thailand are found the high AOT (0.63)

especially at Chiang Rai showed the highest AOT in Table 1 (0.79). The high AOT value (>0.4) are found

over the northern areas by caused intense anthropogenic activity and burning areas.

136


IAEES www.iaees.org

Table 1 Average monthly AOT in Northern Thailand (during year 2003 to 2012).

Provinces

Average monthly AOT (during 2003 to 2012) Average Annual Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mae Hong Son 0.07 0.16 0.50 0.50 0.22 0.21 0.11 0.14 0.20 0.15 0.06 0.07 0.20

Chiang Mai 0.05 0.15 0.47 0.46 0.23 0.28 0.00 0.10 0.22 0.14 0.05 0.07 0.18

Lamphun 0.12 0.23 0.52 0.51 0.25 0.30 0.08 0.12 0.22 0.18 0.10 0.13 0.23

Chiang Rai 0.14 0.36 0.79 0.65 0.25 0.10 0.21 0.06 0.17 0.21 0.11 0.14 0.27

Lampang 0.19 0.42 0.66 0.50 0.21 0.22 0.06 0.13 0.20 0.23 0.13 0.15 0.26

Phayao 0.12 0.28 0.77 0.64 0.25 0.22 0.12 0.21 0.20 0.20 0.11 0.14 0.27

Phrae 0.15 0.34 0.77 0.67 0.28 0.25 0.32 0.28 0.26 0.24 0.13 0.16 0.32

Nan 0.13 0.30 0.62 0.55 0.22 0.24 0.08 0.10 0.28 0.22 0.11 0.14 0.25

Uttaradit 0.17 0.35 0.57 0.51 0.18 0.23 0.04 0.13 0.12 0.17 0.11 0.15 0.23 Mean of Northern Thailand 0.13 0.29 0.63 0.55 0.23 0.23 0.11 0.14 0.21 0.19 0.10 0.13 0.25

NanChiangmai

Lampang

Chiangrai

Phrae

Maehongson

Phayao

Uttharadit

Lamphun

101°0'0"E

101°0'0"E

100°30'0"E

100°30'0"E

100°0'0"E

100°0'0"E

99°30'0"E

99°30'0"E

99°0'0"E

99°0'0"E

98°30'0"E

98°30'0"E

98°0'0"E

98°0'0"E

97°30'0"E

97°30'0"E

20°3

0'0"

N

20°3

0'0"

N

20°0

'0"

N

20°0

'0"

N

19°3

0'0"

N

19°3

0'0"

N

19°0

'0"N

19°0

'0"N

18°3

0'0"

N

18°3

0'0"

N

18°0

'0"

N

18°0

'0"

N

17°3

0'0"

N

17°3

0'0"

N

Average annual AOT

ValueHigh : 1

Low : 0

NanChiangmai

Lampang

Chiangrai

Phrae

Maehongson

Phayao

Uttharadit

Lamphun

101°0'0"E

101°0'0"E

100°30'0"E

100°30'0"E

100°0'0"E

100°0'0"E

99°30'0"E

99°30'0"E

99°0'0"E

99°0'0"E

98°30'0"E

98°30'0"E

98°0'0"E

98°0'0"E

97°30'0"E

97°30'0"E20

°30'

0"N

20°3

0'0"

N

20°0

'0"N

20°0

'0"N

19°3

0'0"

N

19°3

0'0"

N

19°0

'0"N

19°0

'0"N

18°3

0'0"

N

18°3

0'0"

N

18°0

'0"N

18°0

'0"N

17°3

0'0"

N

17°3

0'0"

N

AOT in pre-monsoon

ValueHigh : 1

Low : 0

(a) (b)

NanChiangmai

Lampang

Chiangrai

Phrae

Maehongson

Phayao

Uttharadit

Lamphun

101°0'0"E

101°0'0"E

100°30'0"E

100°30'0"E

100°0'0"E

100°0'0"E

99°30'0"E

99°30'0"E

99°0'0"E

99°0'0"E

98°30'0"E

98°30'0"E

98°0'0"E

98°0'0"E

97°30'0"E

97°30'0"E

20°3

0'0"

N

20°3

0'0"

N

20°0

'0"N

20°0

'0"N

19°3

0'0"

N

19°3

0'0"

N

19°0

'0"

N

19°0

'0"

N

18°3

0'0"

N

18°3

0'0"

N

18°0

'0"N

18°0

'0"N

17°3

0'0"

N

17°3

0'0"

N

AOT in monsoon

ValueHigh : 1

Low : 0

NanChiangmai

Lampang

Chiangrai

Phrae

Maehongson

Phayao

Uttharadit

Lamphun

101°0'0"E

101°0'0"E

100°30'0"E

100°30'0"E

100°0'0"E

100°0'0"E

99°30'0"E

99°30'0"E

99°0'0"E

99°0'0"E

98°30'0"E

98°30'0"E

98°0'0"E

98°0'0"E

97°30'0"E

97°30'0"E

20°3

0'0"

N

20°3

0'0"

N

20°0

'0"

N

20°0

'0"

N

19°3

0'0"

N

19°3

0'0"

N

19°0

'0"

N

19°0

'0"

N

18°3

0'0"

N

18°3

0'0"

N

18°0

'0"

N

18°0

'0"

N

17°3

0'0"

N

17°3

0'0"

N

AOT in winter

ValueHigh : 1

Low : 0

(c) (d)

Fig. 1 Distribution of average Aerosol Optical Thickness (AOT) in Northern Thailand during 2003-2012 (a) annual AOT (b) AOT in pre-monsoon (c) AOT in monsoon and (d) AOT in winter.

137


IAEES www.iaees.org

3.1.2 Seasonal variation of aerosols in the Upper Northern Thailand

Seasonal variations of AOTs were shown in Table 2. The mean, maximum and minimum AOT during 2003 to

2012 were calculated as shown in Table 1. Table 2 was showed that mean AOT was highest during pre-

monsoon season (February to April) are at about 0.5 especially in march over Chiang Rai, Phayao and Phrae. It

was noticed that during this period the low pressure over the upper northern Thailand lifted the warm air

masses above the Earth's surface. Aerosol particles were taken up in the low atmosphere causing haze. Aerosol

concentrations during the pre-monsoon season (March to April) are typically at peak associated with biomass

burning activity and contribute significantly to the regional emissions (Carmichael et al., 2003; Janjai et al.,

2009; Streets et al., 2009). The seasonal emissions peak occurs prior to the onset of the Asian summer

monsoon rains and is prevalent over the forested regions of the peninsula including Myanmar and northern

Thailand. Smoke plumes due to biomass burning, accompanied by anthropogenic emissions, result in dense

haze conditions, with episodic pollution levels at surface (e.g., PM10, Total Suspended Particulate, etc.) far

exceeding the regional air quality standards during the pre-monsoon season (Chew et al., 2008; Pengchai et al.,

2009). In addition to air quality effects, aerosols from this region, mostly fine-mode smoke plumes, have been

shown to have potential climate impacts by altering cloud microphysics (Ramanathan et al., 2001) and

perturbing regional radiation budget (Hsu et al., 2003) during pre-monsoon season. In rainy season (May to

October), mean AOT value was less at about 0.2 because it was washed out by rain (Table 2). The annual

average AOT indicated at about 0.1 in the winter (November to January).

Table 2 Seasonal average AOT in upper Northern Thailand (during 2003 to 2012).

Provinces

Pre-monsoon (Feb-Apr) Monsoon (May-Oct) Winter (Nov-Jan)

Mean Max Min Mean Max Min Mean Max Min

Mae Hong Son 0.387 0.531 0.206 0.173 0.312 0.069 0.066 0.111 0.025

Chiang Mai 0.356 0.481 0.182 0.161 0.299 0.063 0.059 0.108 0.014

Lamphun 0.421 0.612 0.211 0.193 0.364 0.044 0.119 0.201 0.045

Chiang Rai 0.604 0.790 0.297 0.167 0.340 0.059 0.129 0.197 0.068

Lampang 0.526 0.700 0.298 0.175 0.333 0.051 0.154 0.246 0.080

Phayao 0.563 0.765 0.311 0.202 0.344 0.072 0.123 0.202 0.045

Phrae 0.591 0.776 0.327 0.273 0.527 0.104 0.147 0.216 0.073

Nan 0.490 0.653 0.269 0.190 0.354 0.069 0.127 0.208 0.053

Uttaradit 0.476 0.622 0.327 0.143 0.325 0.028 0.143 0.241 0.051

Mean of Northern Thailand 0.491 0.659 0.270 0.186 0.356 0.062 0.118 0.192 0.050

3.1.3 Aerosol particle size distribution

Analysis of aerosols size distribution was used ground based data of AERONET station at Chiang Mai since

year 2007 to 2012. Fig. 2 shows that almost all of total Aerosol Optical Depth (Total AOD) in pre-monsoon

are the small size of aerosol (Fine mode) but in rainy season almost all of aerosol in the atmosphere are big

size of aerosol (Coarse Mode). So the small aerosol particles played more influence in pre-monsoon than the

big size of aerosol particles (Table 3).

138


IAEES www.iaees.org

Fig. 2 Aerosol size characteristics (a) fine mode (small size) (b) coarse mode (big size) found during year 2007 to 2012 from AERONET station at Chiang Mai.

Table 3 Fine mode and coarse mode AOT in different seasons based on AERONET station during 2007 to 2012.

Year

Fine Mode AOT Coarse Mode AOT

Pre-Monsoon Rainy season

Winter season Pre-Monsoon

Rainy season

Winter season

2008 0.666 0.153 0.293 0.097 0.329 0.051

2009 0.763 0.128 0.333 0.103 0.230 0.050

2010 0.832 0.142 0.294 0.110 0.260 0.049

2011 0.519 0.132 0.344 0.069 0.199 0.039

2012 1.088 0.178 0.211 0.074 0.184 0.035

3.1.4 Aerosol types and aerosol sources classification

In Northern Thailand, annual aerosol size distribution shows a bimodal distribution with a fine mode peak at

0.14 micron and coarse mode peak near 5 micron (Fig. 3b). This distribution agreed with Gautam et al. (2012)

who found the bimodal distribution in pre-monsoon small aerosol emission from urban and agricultural area

over Indochina zone. When compared Fig. 3a with the diagram as shown in Fig. 3a by Jaenicke (1993), it is

found that small aerosol particles were about 0.08-0.5 microns similar ranges of Black carbon size and smoke

particles. Then, big size of aerosol were about 1.3-8.7 microns could be emitted from sea spray, pollen, fly ash,

cloud droplet and rain droplet (Fig. 3a).

Pre monsoon

Pre monsoon

monsoon

monsoon

monsoonPre monsoon

Pre monsoon

Pre monsoon

Pre monsoonmonsoon

monsoonmonsoon

(a)

(b)

139


IAEES www.iaees.org

(a) Aerosol size distribution by Jaenicke (1993).

(b) Annual mean aerosol optical depths (AOD) over AERONET station from 2007 to 2012.

Fig. 3 Comparing aerosol size distribution by Jaenicke (1993) and aerosol particles size distribution observed over AERONET station during 2007 to 2012.

When considering the size distribution of the chemical composition from the study of Li et al. (2013)

described the basic elements that come from the soil such as Ca2+, Na+ and Mg2+ were found in coarse

aerosols or large aerosols particle (Fig. 4). It also found that K+ was found prominently in small aerosols

particle and were released from biomass burning. Source of Potassium in rainfall was aerosols from sea salt,

biomass burning and fertilizer production processes. O'Neill (1993) stated that in the seawater intake

Potassium 0.39 g dm-3, it ranked 4th of available cations in sea. Berner and Berner (1996) stated that the source

of Potassium in the atmosphere above the ground emitted from five sources, namely: 1) the melting of dust 2)

fertilizer containing Potassium in soil 3) pollens and seeds 4) biogenic aerosols and 5) forest fires that the

major problems in the tropics. Basing on Fig. 3b and Fig. 4 it could be assumed that small aerosols which were

in the range of 0.08 to 0.5, it may be the main component is a water-soluble K+ which comes from the burning

of biomass and may be compounds with SO42- was suspended in the atmosphere about 5-12 days and spread to

Coarse Mode/Big size Fine Mode/Small size

140


IAEES www.iaees.org

far up to 1,000 kilometers or NH4+ compounds can float in the atmosphere about 6 days and fly as far as 5,000

kilometers, mostly resulting from the burning coal or fuel.

Fig. 4 The average size distributions of different species. Source: Li et al. (2013).

3.1.5 Hotspot variation for defining burning aerosol

Mean hotspot numbers in Northern Thailand were detected by MODIS during year 2003 to 2012 appeared at

about 675 points. The highest hotspot number in year 2007 was found especially in Mae Hong Son. Table 4

shows the highest frequency of hotspot number appearing in March. The highest biomass burning was

occurred in 2004 but dramatically reduced in year 2011 because of policy about biomass burning events

reducing.

Table 4 Average monthly Hotspot (HP) during 2003 to 2012 in the different provinces (analyzed based on MODIS data).

Province

Average monthly hotspot (HP) during 2003 to 2012

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mae Hong Son 14 235 1472 589 5 1 1 0 0 1 1 8

Chiang Mai 58 421 1227 356 7 1 0 1 1 1 5 46

Lamphun 49 201 136 12 2 1 1 1 1 1 1 8

Chiang Rai 81 286 797 263 6 1 1 1 2 1 5 59

Lampang 68 294 337 58 3 2 1 1 1 1 4 22

Phayao 36 108 226 79 4 1 1 0 0 1 1 18

Phrae 27 158 317 89 5 1 1 0 1 1 3 33

Nan 17 241 1352 331 5 1 0 0 1 0 3 7

Uttaradit 58 139 211 74 10 1 1 1 1 2 20 77

141


IAEES www.iaees.org

3.2 Variation of cloud and rainfall in upper Northern Thailand

3.2.1 Clouds water content (CWC)

Cloud water content (CWC) can be expressed either in g/m3 or mm. Monthly cloud water content during year

2003 to 2012 in the Northern Thailand (Table 5a) indicated the highest CWC in August with 196g/m3. There

was high fluctuation of CWC in February and Mar. Cloud water content anomalies in March and April were

subnormal at about -2.58 and -1.66 respectively. It corresponded with highest AOT in the same period.

Table 5 Average monthly Cloud Water Content (CWC), average monthly Cloud Fraction (CF) and average monthly rainfall (RF) during 2003 to 2012 analyzed based on MODIS and TRMMM data.

Province

(a) Cloud Water Content (CWC); g/cm2

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mae Hong Son 149.7 112.8 98.9 153.4 168.0 175.4 195.7 196.1 162.5 148.8 165.6 89.0

Chiang Mai 130.2 104.8 103.2 193.8 200.2 182.9 208.2 208.8 190.7 196.0 125.3 100.7

Lamphun 156.4 146.2 109.5 192.5 211.8 165.6 164.0 175.8 198.8 189.2 166.3 80.1

Chiang Rai 145.8 83.2 124.7 119.8 173.8 186.1 192.5 202.3 196.9 140.6 116.7 82.3

Lampang 80.5 108.4 112.5 114.5 179.6 143.7 166.7 183.6 174.2 195.6 106.5 71.9

Phayao 150.1 87.6 95.4 136.7 194.0 192.0 210.5 225.7 235.3 158.6 124.9 103.6

Phrae 101.6 98.9 161.9 105.4 172.2 168.7 207.9 220.1 212.8 165.0 147.2 86.6

Nan 115.5 135.1 95.5 125.6 191.6 156.1 168.7 178.9 186.9 190.7 118.2 113.6

Uttaradit 78.7 76.6 119.9 141.7 163.4 154.7 151.6 172.8 170.1 205.5 102.4 82.7

Province

(b) Cloud Fraction (CF)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mae Hong Son 0.12 0.10 0.26 0.46 0.62 0.68 0.68 0.68 0.64 0.43 0.22 0.16

Chiang Mai 0.12 0.13 0.27 0.43 0.62 0.68 0.68 0.68 0.63 0.48 0.28 0.20

Lamphun 0.12 0.15 0.28 0.51 0.68 0.72 0.70 0.68 0.64 0.49 0.28 0.21

Chiang Rai 0.20 0.28 0.44 0.62 0.68 0.68 0.69 0.67 0.59 0.44 0.26 0.19

Lampang 0.20 0.32 0.44 0.61 0.70 0.72 0.68 0.68 0.61 0.47 0.27 0.19

Phayao 0.18 0.23 0.39 0.53 0.64 0.71 0.69 0.68 0.60 0.45 0.26 0.21

Phrae 0.23 0.25 0.40 0.59 0.68 0.68 0.70 0.68 0.61 0.49 0.29 0.24

Nan 0.16 0.20 0.32 0.50 0.65 0.73 0.69 0.67 0.64 0.49 0.29 0.21

Uttaradit 0.21 0.29 0.39 0.64 0.71 0.75 0.68 0.67 0.63 0.54 0.33 0.22 (c) Rainfall amount (RF)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mae Hong Son 11.57 3.29 29.84 74.70 236.39 285.16 326.79 344.10 301.84 166.24 32.03 10.42Chiang Mai 12.70 5.17 34.68 90.73 229.83 224.41 262.00 288.77 299.22 146.65 28.87 10.97

Lamphun 9.03 5.52 35.22 82.35 228.08 208.86 204.95 253.10 290.43 137.77 27.92 8.82 Chiang Rai 16.31 12.31 53.75 145.05 226.27 222.17 281.51 351.05 302.81 98.28 24.78 11.35

Lampang 11.27 11.21 40.75 110.62 235.38 238.83 219.80 268.72 308.34 119.13 21.02 9.25

Phayao 17.60 12.59 51.80 134.63 240.76 209.55 323.12 353.51 336.08 138.51 31.85 15.05

Phrae 17.50 11.23 53.79 133.26 232.62 203.90 288.47 345.16 325.92 131.63 29.92 11.44

Nan 10.64 8.43 41.03 104.65 232.00 182.86 203.48 254.99 304.79 128.46 27.42 10.31

Uttaradit 10.96 10.27 39.84 107.64 242.38 203.21 195.71 240.92 303.30 115.50 23.99 9.68

142


IAEES www.iaees.org

3.2.2 Cloud cover or Cloud fraction

All the clouds that covered the sky visible to the eye were in the range of 0-1. It was found that cloud covered

in the upper northern Thailand were maximum in June at about 0.71 or 71% of the sky areas, especially over

Uttaradit. Minimum clouds covered were found in January at about 0.12 or 12% especially in Mae Hong Son,

Chiang Mai and Lamphun (Table 5b).

3.2.3 Rainfall amount

In the Northern Thailand, average annual rainfall amount was approximately 1,661 mm and average monthly

rainfall was highest in August about 300 mm especially in Phayao and Phrae (Table 5c).

3.3 Relationship between aerosols, cloud and rainfall

The relationship between aerosols, clouds and rainfall based on Aerosol Optical Thickness (AOT) Clouds

Water Content (CWC), Clouds Fraction (CF), Rainfall (R) and Hotspot number (HP) using multiple

correlation and multiple regression analysis are the details as follows;

3.3.1 Relationships between aerosols, cloud and rainfall using Multiple Correlation

Multiple correlation analyzed relationships between two parameters based on Correlation Coefficient (Cohen,

1988). It could be indicated that Rainfall (R) was high positive relationship with Clouds Fraction (CF) and

Clouds Water Content (CWC). In the other hand, rainfall amount was high negative relationship with Aerosol

Optical Thickness (AOT) and Hotspot (HP) (Table 6). In Chiang Mai, Mae Hong Son, Lamphun and Uttaradit,

rainfall amount was mostly related with Clouds Fraction (r=0.359 to 0.508). In Chiang Rai, Lampang, Phayao,

Phrae and Nan, it was found that the rainfall amount (R) was most closely associated with Clouds Water

Content (r = 0.284 to 0.639).

Table 6 Pearson correlation coefficient for Aerosol Optical Thickness (AOT), cloud fraction (CF), rainfall, cloud water content (CWC) and hotspot (HP) factors during 2003 to 2012.

Province Factors Cloud Fraction Rainfall CWC AOT Hotspot

Chiang Mai

Cloud Fraction 1 .474** .382** -.117 -.216*

Rainfall .474** 1 .471** -.354** -.385**

CWC .382** .471** 1 -.125 -.254**

AOT -.117 -.354** -.125 1 .724**

Hotspot -.216* -.385** -.254** .724** 1

Mae Hong Son

Cloud Fraction 1 .508** .224* -0.133 -0.152

Rainfall .508** 1 .320** -.471** -.349**

CWC .224* .320** 1 -.229* -.214*

AOT -0.133 -.471** -.229* 1 .733**

Hotspot -0.152 -.349** -.214* .733** 1

Province Factors Cloud

Fraction Rainfall CWC AOT Hotspot

Chiang Rai Cloud Fraction 1 .417** .325** .026 -.098

Rainfall .417** 1 .599** -.331** -.355**

CWC .325** .599** 1 -.266** -.296**

AOT .026 -.331** -.266** 1 .803**

Hotspot -.098 -.355** -.296** .803** 1

Lamphun

Cloud Fraction 1 .456** .183 -.061 -.317**

Rainfall .456** 1 .284** -.324** -.429**

CWC .183 .284** 1 -.058 -.235*

143


IAEES www.iaees.org

AOT -.061 -.324** -.058 1 .441**

Hotspot -.317** -.429** -.235* .441** 1

Lampang

Cloud Fraction 1 .399** .329** -.017 -.155

Rainfall .399** 1 .548** -.389** -.443**

CWC .329** .548** 1 -.174 -.244**

AOT -.017 -.389** -.174 1 .654**

Hotspot -.155 -.443** -.244** .654** 1

Phayao

Cloud Fraction 1 .463** .348** .021 -.153

Rainfall .463** 1 .639** -.261** -.402**

CWC .348** .639** 1 -.317** -.497**

AOT .021 -.261** -.317** 1 .713**

Hotspot -.153 -.402** -.497** .713** 1

Phrae

Cloud Fraction 1 .437** .286** -.152 -.157

Rainfall .437** 1 .528** -.413** -.406**

CWC .286** .528** 1 -.320** -.196*

AOT -.152 -.413** -.320** 1 .728**

Hotspot -.157 -.406** -.196* .728** 1

Nan

Cloud Fraction 1 .430** .307** -.119 -.132

Rainfall .430** 1 .454** -.362** -.284**

CWC .307** .454** 1 -.294** -.337**

AOT -.119 -.362** -.294** 1 .783**

Hotspot -.132 -.284** -.337** .783** 1

Uttaradit

Cloud Fraction 1 .359** .181 -.061 -.268**

Rainfall .359** 1 .353** -.418** -.511**

CWC .181 .353** 1 -.109 -.237*

AOT -.061 -.418** -.109 1 .731**

Hotspot -.268** -.511** -.237* .731** 1

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

AOT = Aerosol Optical Thickness (unitless) R = rainfall amount (mm) Hot = hotspot (point) CF = cloud fraction (unitless) CWC = cloud water content (g/cm2)

Fig. 5a showed the spatial correlation between average annual AOT and annual cloud fraction. It was

noticed that there were high correlations between AOT and cloud fraction over Chiang Rai, some area in

Phayao and Chiang Mai. In the other hand, spatial correlation between AOT and rainfall amount showed low.

144


IAEES www.iaees.org

NanChiangmai

Lampang

Chiangrai

Phrae

Maehongson

Phayao

Uttharadit

Lamphun

101°0'0"E

101°0'0"E

100°30'0"E

100°30'0"E

100°0'0"E

100°0'0"E

99°30'0"E

99°30'0"E

99°0'0"E

99°0'0"E

98°30'0"E

98°30'0"E

98°0'0"E

98°0'0"E

97°30'0"E

97°30'0"E

20°3

0'0"

N

20°3

0'0"

N

20°0

'0"N

20°0

'0"N

19°3

0'0"

N

19°3

0'0"

N

19°0

'0"N

19°0

'0"N

18°3

0'0"

N

18°3

0'0"

N

18°0

'0"N

18°0

'0"N

17°3

0'0"

N

17°3

0'0"

N

AOT vs Cloud Fraction

ValueHigh : 1

Low : 0

NanChiangmai

Lampang

Chiangrai

Phrae

Maehongson

Phayao

Uttharadit

Lamphun

101°0'0"E

101°0'0"E

100°30'0"E

100°30'0"E

100°0'0"E

100°0'0"E

99°30'0"E

99°30'0"E

99°0'0"E

99°0'0"E

98°30'0"E

98°30'0"E

98°0'0"E

98°0'0"E

97°30'0"E

97°30'0"E

20°3

0'0"

N

20°3

0'0"

N

20°0

'0"N

20°0

'0"N

19°3

0'0"

N

19°3

0'0"

N

19°0

'0"N

19°0

'0"N

18°3

0'0"

N

18°3

0'0"

N

18°0

'0"N

18°0

'0"N

17°3

0'0"

N

17°3

0'0"

N

AOT vs Rainfall

ValueHigh : 1

Low : 0

(a) (b)

Fig. 5 Spatial correlation of (a) average annual AOT and cloud fraction and (b) average annual AOT and rainfall in Northern Thailand.

3.3.2 Relationship equations between aerosols, cloud and rainfall using multiple regression analysis

The interaction between aerosol and rainfall amount is shown in Table 6. When there was less aerosol amount

(less AOT), rainfall amount would be high. In the other hand, the highest AOT in dry season would cause the

less rainfall. It was found that there were many factors influencing cloud and rainfall amount rather than

aerosol concentration. So, in order to get better relationships between aerosol with cloud and rainfall, one

should consider including the other criteria (i.e., relative humidity, polluted cloud, atmospheric stability, etc.).

Results of the stepwise multiple regression analysis to select the factors influencing the average monthly

rainfall indicated that in almost all cloud water content (CWC) and cloud cover influence on increasing rainfall

as CWC and Cloud cover are also a contributory factor to increase water droplets in clouds, including

increased reflectivity in cloud. On contrary, aerosols effect on the rainfall decline was exception in Phayao

Province where the AOT was also high. This increase may be due to other factors such as weather conditions

played more influence than the aerosol factor. In general, all relationship equations indicated variability of

aerosol and cloud could be moderately explained changing of rainfall (Table 7).

Table 7 Relationships between AOT, cloud water content, cloud fraction and rainfall amount using multiple regressions.

Province Equations (Relationships) r

Mae Hong Son R = 124.92 -297.323AOT+167.671CF 0.636

Chiang Mai R = 41.086 +0.469CWC -171.315AOT+105.994CF 0.625

Chiang Rai R = -7.269 +0.873CWC -111.503AOT+106.352CF 0.675

Phrae R = 35.112 +0.016CWC -169.391AOT+107.556CF 0.643

Phayao R = -47.78 +0.958CWC +107.89CF 0.688

Uttaradit R = 91.034 +0.325CWC -219.898AOT+86.67CF 0.578

Nan R = 33.678 +0.480CWC -108.367AOT+97.083CF 0.580

Lampang R = 24.988 +0.765CWC -185.200AOT+86.136CF 0.665

Lamphun R = 77.378 +0.100CWC -195.406AOT+117.317CF 0.537 AOT = Aerosol Optical Thickness (unitless); R = rainfall amount (mm)

CF = cloud fraction (unitless); CWC = cloud water content (g/cm2)

145


IAEES www.iaees.org

4 Conclusions

In Northern Thailand, it was found that almost all provinces showed the highest average annual AOT in March

especially over Chiang Rai with 0.79. Fine mode of aerosol (small size) could be dominated in dry period.

While the big size of aerosol was dominated in wet period. In dry season, black carbon from on-site biomass

burning were the main types of aerosol, which they actually affected on cloud and rainfall in early rainy season.

Cloud water content anomaly show that in March and April are subnormal at about -2.576 g/m3 and -1.658

g/m3 respectively. It corresponds with highest AOT in the same period in the Northern Thailand. Average

monthly rainfall amount in the Northern Thailand showed the highest amount in September with 308.08 mm

Aerosol concentration was high effect on decreasing cloud and rainfall in dry season, but it showed that the

size of aerosols play more influence than aerosol concentration in rainy season because there was high water

vapors in the atmosphere. In the Northern Thailand, there was moderate relationship between rainfall amount

with AOT and Cloud Fraction (CF). It could be said that increased aerosol loading induced to decrease rainfall

amount.

Acknowledgement

The authors are gratefully acknowledging the Center for Advanced Studies in Tropical Natural Resources for

supporting this research grant. The authors acknowledge MODIS and TRMM for providing the dataset.

References

Berner EK, Berner RA. Global Environment: Water, Air and Geochemical Cycles. Prentice Hall, New Jersey,

1996

Carmichael GR, Ferm M, Thongboonchoo N, et al. 2003. Measurements of sulfur dioxide, ozone and ammonia

concentrations in Asia, Africa, and South America using passive samplers. Atmospheric Environment,

37:1293-1308

Chew BN, Chang CW, Liew SC, et al. 2008. Remote sensing measurements of aerosol optical thickness and

correlation with in-situ air quality parameters during a biomass burning episode in Southeast Asia. In:

Proceedings of the 29th Asian Conference on Remote Sensing (ACRS2008) 10-14 November 2008,

Colombo, Sri Lanka, Paper no. TS25.4

Cohen J. 1988. Statistical Power Analysis for The Behavioral Sciences (2nd ed). Hillsdale, Lawrence Earlbaum

Associates, New Jersey, USA

Gautam R, Hsu NC, Eck TF, et al. 2012. Characterization of aerosols over the Indochina peninsula from

satellite-surface observations during biomass burning pre-monsoon season. Atmospheric Environment

(DOI:10.1016/j.atmosenv. 2012.05.038)

Hsu NC, Herman JR, Say TS. 2003. Radiative impacts from biomass burning in the presence of clouds during

boreal spring in Southeast Asia. Geophysical Research Letters, 30(5): 1224

Jaenicke R. 1993. Tropospheric aerosols in Aerosol-Cloud-Climate Interactions. P.V. Hobbs, Academic Press,

San Diego, USA

Janjai S, Suntaropas S, Nunez M, 2009. Investigation of aerosol optical properties in Bangkok and suburbs.

Theoretical and Applied Climatology, 96: 221-233

Kaufman YJ, Koren I, Remer LA, et al. 2005. The effect of smoke, dust, and pollution, aerosol on shallow

cloud development over the Atlantic Ocean. Proceedings of the National Academy of Sciences of USA,

102: 11207-11212

146


IAEES www.iaees.org

Kaufman YJ, Tanré D, Remer LA, et al. 1997. Operational remote sensing of tropospheric aerosol over land

from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research, 102(D14):

17051-17068

Levy RC, Remer LA, Dubovik O. 2007. Global aerosol optical properties and application to Moderate

Resolution Imaging Spectroradiometer aerosol retrieval over land. Journal of Geophysical Research, 112:

D13210

Li C, Tsay SC, Hsu NV, et al. 2013. Characteristics and composition of atmospheric aerosols in Phimai,

Central Thailand during BASE-ASIA. Atmospheric Environment, 78: 60-71

O'Neill P. 1993. Environmental Chemistry (2nd ed). Chapman and Hall, London, UK

Pengchai P, Chantara S, Sopajaree K, et al. 2009. Seasonal variation, risk assessment and source estimation of

PM10 and PM10-bound PAHs in the ambient air of Chiang Mai and Lamphun, Thailand. Environmental

Monitoring and Assessment, 154: 197-218

Rogers RR, Yau MK. 1989. A Short Course in Cloud Physic (Third edition). Burlington. M.S. Thesis,

Butterworth-Heinemann, UK

Ramanathan V, Crutzen PJ, Kiehl JT, et al. 2001. Aerosols, climate, and the hydrological cycle. Science, 294

(5549): 2119-2124

Rosenfeld D. 1999. TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall.

Geophysical Research Letters, 26: 3105-3108

Rosenfeld D. 2000. Suppression of rain and snow by urban and industrial air pollution. Science, 287: 1793-

1796

Rosenfeld D, Rudich Y, Lahav R. 2001. Desert dust suppressing precipitation: A possible desertification

feedback loop. Proceedings of the National Academy of Sciences of USA, 98: 5975-5980

Streets DG, Yan F, Chin M, et al. 2009. Anthropogenic and natural contributions to regional trends in aerosol

optical depth, 1980-2006. Journal of Geophysical Research, 114(D10): D00D18

147


IAEES www.iaees.org

Article

Detected foraging strategies and consequent conservation policies of

the Lesser Kestrel Falco naumanni in Southern Italy

Marco Gustin1, Alessandro Ferrarini 2, Giuseppe Giglio1, Stefania Caterina Pellegrino1, Annagrazia Frassanito3 1LIPU (Lega Italiana Protezione Uccelli) - BirdLife International, Conservation Department, Via Udine 3, I-43100 Parma, Italy 2Department of Evolutionary and Functional Biology, University of Parma, Via G. Saragat 4, I-43100 Parma, Italy 3Alta Murgia National Park, via Firenze 10, 70024, Gravina in Puglia, Bari, Italy

E-mail: [email protected], [email protected]

Received 6 June 2014; Accepted 10 July 2014; Published online 1 December 2014

Abstract

The reduction in both the extent and quality of foraging habitats is considered the primary cause of the Lesser

Kestrel Falco naumanni population decline. A proper knowledge of Lesser Kestrel’s foraging habitat selection

at local scale is necessary for its conservation. Using accurate GPS devices, we investigated the patterns of

local movements and land-cover type selection of 9 Lesser Kestrels in the main colony in Italy (Alta Murgia

National Park, Gravina in Puglia and the surrounding rural areas) during the hatching period. The goals of our

work were to individuate: 1) the preferred foraging habitats, 2) the potential sexual divergences in foraging

movements and in 3) foraging habitat selection, 4) the relationship between foraging movements and the

spatial arrangement of land codes. We detected significant sexual divergences in foraging movements and

habitat selection. Lesser Kestrels preferred pseudo-steppes and significantly avoided ligneous crops and

forested areas. While males selected positively pseudo-steppes, females used both pseudo-steppes and cereals

in proportion to their availability. Foraging selection was influenced by the interplay between the spatial

arrangement of land codes and the sexual divergences in foraging strategies. On the basis of our results, we

have been able to propose suitable local-scale conservation actions to the Alta Murgia National Park and to the

local administrations: a) the enlargements of the park’s boundaries; b) the purchasing of land parcels; c) the

provision of suitable nesting sites near the higher quality areas; d) the optimal timing for harvesting. Our study

is the first contribution to the assessment of the foraging strategies and the necessary conservation policies of

the Lesser Kestrel in Southern Italy.

Keywords Alta Murgia National Park; data-loggers; foraging movements; hatching period; sexual divergences;

special protection area.

1 Introduction



IAEES www.iaees.org

1 Introduction

Resource selection studies are commonly conducted because it is generally assumed that if animals select

habitat disproportionately to their availability, that habitat improves their fitness, reproduction and survival

(Stephens and Krebs, 1986). From foraging theory strategy, it’s also known that if land cover change causes

impoverishment and/or loss of preferred hunting habitats, a species would obtain a lesser hunting yields, with

direct implications for its conservation.

Changes in land-use, both with concurrent aspects of global change, have a strong impact on the structure

of biological communities (Gil-Tena et al., 2009). Many species of conservation interest in Europe are

considered associated with traditional farm landscapes and the semi-natural habitats they produce and maintain

(Tucker and Evans, 1997). Land abandonment has been an important land-use change in recent decades

(Ostermann, 1998). The decrease in farming mainly affected the least productive agricultural land, and

activated the recovery of semi-natural vegetation (Sirami et al., 2008). In most of the Mediterranean region,

land abandonment has occurred during the last century, leading to the naturalization and vegetation closure of

many areas, thus favouring the spread of forests (Debussche et al., 1999). This caused a decrease of open

grassland-like habitats and an increase in shrubland and, on the long-term, woodland cover (Romero-

Calcerrada and Perry, 2004), thus determining a decline of species tied to open habitats (Suárez-Seoane et al.,

2002; Sirami et al., 2007), in particular migrant species associated with open farmland habitats (Sirami et al.,

2008). On the other hand, agricultural intensification and abandonment of traditional farming had dramatic

impacts on farmland birds (Donald et al., 2001), in particular on the quality of foraging patches and food

availability (Donázar et al., 1993), thus affecting species’ fitness components such as the number of offspring

that parents are able to raise (Tella et al., 1998).

In the past, the reduction in quality and extent of foraging habitats has been the primary cause of decline

for Lesser Kestrel (Negro, 1997; Peet and Gallo-Orsi, 2000). Extensive cereal fields, fallows, pasturelands and

field margins in agricultural areas are usually considered the main habitats used by this species for foraging

(Cramp and Simmons, 1980; Donázar et al., 1993; Tella et al., 1998). Arthropod abundance is usually higher

in these types of land-use (Martínez, 1994; Moreira, 1999). On the other hand, for hunters such as the Lesser

Kestrel, access to prey is also affected by vegetation structure (Shrubb, 1980; Toland, 1987), in particular by

land cover offering shelter to prey, and height which obstructs hunting manoeuvres. This may explain why

they usually avoid hunting in habitat patches with taller vegetation cover, such as abandoned crop fields or

shrublands (Tella et al., 1998). In addition, the species has declined markedly in the last decades also because

of agricultural intensification and pesticide use, which affected their foraging habitats and food availability

(Parr et al., 1995; Bustamante, 1997; Tella et al., 1998; BirdLife International, 2004).

Despite the urgent need for the conservation of this species, at present little is known about foraging

habitat selection of Lesser Kestrels in Italy (Sarà 2010). Due to this reason, in this paper we investigate the

patterns of land-cover type selection of Lesser Kestrels in the main colony in Italy (Alta Murgia National Park,

Gravina in Puglia and the surrounding rural areas) during the hatching period. The goals of our work were to

individuate preferred foraging habitats within and outside the Alta Murgia National Park, and explore the

hypothesis of potential foraging divergences with regard to sex. In fact, sexual differences in foraging habitat

selection can be hypothesized to arise as a consequence of two necessities for females during the hatching

period, i.e. spending as much time as possible in parental care and limiting energy requirements for foraging

movements. No studies focus on this topic for Lesser Kestrels yet, but sexual divergences in foraging selection

might have important consequences on conservation strategies for this species.

149


IAEES www.iaees.org

We also explored if the relationship between the Lesser Kestrel’s foraging movements and the spatial

arrangement of habitats may influence the foraging habitat selection. Based on our results, several

management policies are proposed for the conservation of this important species in Italy.

2 Materials and Methods

2.1 Study area

The study area (Fig. 1) corresponds to the Alta Murgia National Park and the SPA (Special Protection Area)

“Murgia Alta” IT9120007 (Apulia, Southern Italy) and is included within the IBA (Important Bird Area)

“Murge” (Heath and Evans, 2000). It comprehends the main colony of Lesser Kestrels in Italy (Bux et al. 2008,

Gustin et al., 2014), i.e. the town of Gravina in Puglia and the surrounding rural areas.

2.2 Study species

Lesser Kestrel is a migratory, colonial, small (body length 29–32 cm, wingspan 58–72 cm) falcon breeding

mainly in holes and crevices in large historic buildings within towns and villages, or often in abandoned farm

houses scattered across the countryside (Negro, 1997). The Lesser Kestrel is primarily insectivorous, feeding

mainly on beetles, myriapods and grasshoppers (Franco and Andrada, 1977; Kok et al., 2000). It inhabits

steppe-like ecosystems around the Mediterranean and central Asia. In Western Europe it is mainly a summer

visitor, migrating to Africa in winter (Rodríguez et al., 2009). Today Lesser Kestrel is considered a “least

concern” species (BirdLife International, 2013; Gustin et al., 2014).

Fig. 1 Study area (Gravina in Puglia and Alta Murgia National Park; Apulia, Italy), nests and roosts. The study area corresponds to the SPA (Special Protection Area) “Murgia Alta” IT9120007 and is included within the IBA (Important Bird Area) “Murge”.

2.3 Data sampling

Nine individuals (4 males and 5 females) were surveyed in a period of 20 days from June 20th to July 9th 2012

in the colony of Gravina in Puglia. Surveys were conducted using TechnoSmart GiPSy-4 data-loggers

(backpack harness; 23x15x6 mm; total weight: 1.8 g plus 3.2 g battery), that provided for each GPS point

information about date (dd/mm/yyyy), local time (hh:mm:ss), latitude (degrees-minutes-seconds), longitude

(degrees-minutes-seconds), altitude (meters above mean sea level) and instantaneous speed (km/h). Data

150


IAEES www.iaees.org

acquisition occurred every 5 minutes during two time periods: day (08:00-19:00 H local time) and night

(02:00-06:00 H local time). In situ surveys allowed us to locate nests and roosts used by the observed

individuals. Birds were captured at their nest boxes when they were delivering food to their nestlings and fitted

with data-loggers. To download the data from the data-loggers, birds were recaptured at their nest boxes after

batteries were exhausted three days later.

2.4 Data analyses

GPS data were imported into the GIS GRASS (Neteler and Mitasova, 2008). Layers used for the subsequent

analyses were: a) boundaries of the Alta Murgia National Park, b) land cover at 1:10,000 scale (provided by

the Apulia Region), c) digital terrain model (DTM) of the study area (digitized at 1:10,000 scale by the authors

from available topographic maps of Apulia Region), d) nest and roost locations.

We estimated home-ranges using fixed kernel estimators (Worton, 1989) at 95% isopleth, which were

calculated with least-squares cross-validation and adjusted to extreme locations (Worton, 1989). The 95%

isopleth (HR95 from now on) is most widely used in the literature and represented the full home range.

Foraging points (FP) have been individuated using two steps. First, for each GPS point we achieved flight

height above ground level (a.g.l. hereafter) by subtracting terrain elevation (indicated by DTM) from altitude

a.s.l. (provided by data-loggers). Second, among the GPS points having flight height a.g.l. equal to 0, we chose

only those ones having an instantaneous speed (provided by GPS) equal to 0. We privileged this conservative

approach rather than using also GPS points with low instantaneous speed (e.g., less than 1 or 2 km/h) because

we preferred to miss some FP rather than being at risk of including also non-foraging points. These two steps

allowed us to detect locations of the study area where Lesser Kestrels remained motionless at ground level (i.e.,

instantaneous speed and flight height a.g.l. equal to 0). Detected FP thence represented strike attempts (i.e.,

strikes in which the bird landed on the ground), not necessarily successful captures. For the purposes of this

work we considered that strike attempts were a type of foraging habitat selection.

Foraging habitat selection was investigated at the following levels:

a) FP as compared to habitat availability in HR95;

b) male FP as compared to habitat availability in HR95;

c) female FP as compared to habitat availability in HR95.

Thomas and Taylor (1990) distinguished three types of use-availability design used in the studies of habitat

and resource selection. In design I studies, the animals are not identified; the habitat use and availability are

measured at the scale of the population. In design II studies, the animals are identified and the use is measured

for each one, however, the availability is measured at the scale of population. In design III studies, the animals

are identified and both the use and the availability are measured for each one. The choice of the proper use-

availability design can be evaluated only in reference to a specific data set and a specified model (Hurlbert,

1984). In order to decide which type of design to use, we applied the pairwise test of multiple associations

(Janson and Vegelius, 1981; Ludwig and Reynolds, 1988). The pairwise test is based on a chi-squared test

between all possible pairs of point patterns selected for comparisons. Yates correction factor has been

calculated to account for bias resulting from cases of low cell frequencies. These tests were applied to both the

whole set of GPS points (for testing association in space use, and thence in resource availability) and FP (for

testing association in resource use during foraging activities).

Last, in order to evaluate forage habitat selection in relation to availability (i.e., the disproportionate use of

some foraging areas over others when compared to what was available), we used a chi-square goodness-of-fit

test with Bonferroni simultaneous confidence intervals (Neu et al., 1974; Byers et al., 1984). We avoided

compositional analysis (Aebischer et al., 1993) because it is preferable when the number of individuals is at

least equal to the number of habitat classes (Cherry, 1996).

151


IAEES www.iaees.org

All the statistical analyses were considered significant for P<0.05.

3 Results

The monitoring period amounted to 311 hours (3726 GPS locations), of which 116 hours (1389 GPS points)

for females and 195 (2337 GPS points) for males. Lesser Kestrels flew 3674.2 km in total, of which 966.5 due

to females and 2707.7 to males. A total of 329 FP were detected and considered for successive analyses, of

which 179 belonged to females (mean±SD: 35.80 ± 3.56) and 150 to males (mean ± SD: 37.50 ± 4.04).

Pairwise tests of multiple associations on space use (Table 1) suggested to measure the availability at the

scale of population. In fact, all the 36 pairwise comparisons resulted positive, and 19 out of 36 were

statistically significant (P<0.05). Instead, pairwise tests on feeding sites suggested to consider resource use

separately for each Lesser Kestrel (Table 1). In fact, only 5 significant (P<0.05) positive associations remained,

and many associations resulted negative (of which 5 were significant; P<0.05). Hence, a design II study (i.e.,

the use is measured for each animal, however the availability is measured at the scale of population) resulted

most appropriate for our case study.

HR95 (35,503.07 hectares; Fig. 2) resulted prevalently composed of non-irrigated arable lands (AL;

24,852.8 hectares, 70.00% of HR95; Table 2) and pseudo-steppes (PS; 3936.1 hectares, 11.09%). Human

settlements (1041.5 ha) cover about 3% of HR95.

The type of land-use most frequently utilized by foraging Lesser Kestrels (Table 3) was AL (214 FP;

65.05%), followed by PS (97 FP; 29.48%), NG (5 FP; 1.52%), HS (4 FP; 1.22%) and, to a lesser extent, the

remaining codes. These differences between the number of foraging attempts in relation to land-use types were

statistically significant (χ2 = 940.89, d.f. = 9, P<0.001).

Table 1 Results of the pairwise tests of multiple associations on space use (3726 GPS points) and resource use (i.e., foraging sites; 329 GPS points) for the 9 surveyed lesser kestrels. The first column indicates the sex of the 9 individuals (F: female; M: male).

Sex 1 2 3 4 5 6 7 8 9

F ++ ++ ++ ++ + + + + F ++ + ++ + + ++ + F ++ ++ + ++ + + Space use F ++ + + + ++ F + ++ ++ + M ++ ++ + M ++ ++ M ++ M F + + ++ + + - -- - F ++ + + - + - -- Resource use F + ++ - -- - + F + - - + -- F -- + + - M + ++ + M + + M ++ M

++ positive association (P<0.05); + positive association (P >0.05); - negative association (P >0.05); -- negative association (P <0.05).

152


IAEES www.iaees.org

Table 2 Description of the landcover types present in the lesser kestrel’s home-range (35,503.07 hectares). For each code, the extent and the percentage with regard to the home-range are given.

Code Description Hectares % HS continuous and discontinuous urban fabric, agricultural farms,

mineral extraction sites 1041.5 2.93

AL non-irrigated arable lands (cereals in particular, but also legumes, fodder crops, root crops, fallow land)

24,852.8 70.00

LC ligneous crops (vineyards, fruit trees, olive groves) 1824.8 5.14 PS pseudo-steppes (dry grassland grazed extensively by livestock herds) 3936.1 11.09FO broad-leaved forests, coniferous forests, mixed forests 3119.2 8.79 NG natural grasslands 333.0 0.94 SV sclerophylous vegetation 47.5 0.13 TR transitional woodland/shrubs 274.1 0.77 BR bare rocks 69.1 0.19 WA water bodies and courses (including banks) 5.0 0.01

Table 3 Resulting foraging land use (number of feeding sites in the different landcover types) separately for the 4 male and the 5 female lesser kestrels under study. See Table 2 for the explanation of landcover codes.

ID Sex HS AL LC PS FO NG SV TR BR WA 1 F 1 29 0 8 0 1 0 0 0 0 2 F 0 23 1 5 0 1 0 0 0 0 3 F 1 28 0 7 0 0 1 0 0 0 4 F 1 24 0 8 0 1 0 0 1 0 5 F 0 29 1 6 1 0 0 1 0 0 6 M 0 21 0 12 0 1 0 0 0 0 7 M 1 21 0 19 0 0 0 1 0 1 8 M 0 20 0 18 0 0 0 0 0 0 9 M 0 19 0 14 0 1 0 0 0 1

Considering all the individuals under study, 7 land codes were used in proportion to their availability (HS,

AL, NG, SV, TR, BR, WA; Table 4), while breeding kestrels positively selected PS (P < 0.001), and

significantly avoided LC (P < 0.05) and FO (P < 0.05). When considering only female Lesser Kestrels, 8 land

codes were used in proportion to their availability (HS, AL, PS, NG, SV, TR, BR, WA; Table 4), while LC (P

< 0.05) and FO (P < 0.05) were significantly avoided. When considering only male Lesser Kestrels, 5 land

codes were significantly avoided (P < 0.05; HS, AL, LC, FO, BR; Table 4), PS were positively selected (P <

0.001) while the remaining codes were used in proportion to their availability.

153


IAEES www.iaees.org

Fig. 2 The Lesser Kestrel’s home range (95% isopleth; 35,503.07 hectares) and the detected 329 foraging points are shown. A design II study (i.e., the use is measured for each animal, however the availability is measured at the scale of population) resulted most appropriate for our case study.

Table 4 Foraging land code selection measured through Bonferroni simultaneous confidence intervals for: a) all individuals, b) only females, c) only males.

All Land code Lower Upper Available Selection df Prob HS 0.000 0.034 0.029 AL 0.574 0.721 0.700 LC 0.000 0.018 0.051 avoid 9 P < 0.05 PS 0.224 0.365 0.111 prefer 9 P < 0.001 FO 0.000 0.012 0.088 avoid 9 P < 0.05 NG 0.000 0.034 0.009 SV 0.000 0.018 0.001 TR 0.000 0.018 0.008 BR 0.000 0.012 0.002 WA 0.000 0.012 0.000 Females Land code Lower Upper Available Selection df Prob HS 0.000 0.044 0.029 AL 0.651 0.835 0.700 LC 0.000 0.033 0.051 avoid 9 P < 0.05 PS 0.108 0.272 0.111 FO 0.000 0.021 0.088 avoid 9 P < 0.05 NG 0.000 0.044 0.009 SV 0.000 0.021 0.001 TR 0.000 0.021 0.008 BR 0.000 0.021 0.002 WA 0.000 0.000 0.000

154


IAEES www.iaees.org

Males Land code Lower Upper Available Selection df Prob HS 0.000 0.025 0.029 avoid 9 P < 0.05 AL 0.426 0.654 0.700 avoid 9 P < 0.05 LC 0.000 0.000 0.051 avoid 9 P < 0.05 PS 0.307 0.533 0.111 prefer 9 P < 0.001 FO 0.000 0.000 0.088 avoid 9 P < 0.05 NG 0.000 0.040 0.009 SV 0.000 0.025 0.001 TR 0.000 0.025 0.008 BR 0.000 0.000 0.002 avoid 9 P < 0.05 WA 0.000 0.025 0.000

The two most important cover types for Lesser Kestrels’ foraging requirements (PS and AL) have a

different spatial configuration within HR95 (Fig. 3). AL present fewer patches (274 vs. 346) with larger

extension (mean ± SD: 90.70 ha ± 938.56 vs. 11.37 ha ± 66.21, t = 2.160, P < 0.05) and lower distance from

the colony (mean ± SD: 7245 m ± 3200 vs. 11,278 m ± 3350, t = -15.182, P < 0.001).

Fig. 3 Boxplots of distances (in m) from Lesser Kestrels’ colony of: a) patches (GIS polygons) of non-irrigated arable land (AL), b) patches of pseudo-steppes (PS), c) male foraging points FP (M), d) female foraging points FP (F).

During the monitoring period, distance from nest (measured on 2337 GPS points for males and on 1389

GPS points for females, using a repeated measures ANOVA) resulted significantly higher for males than for

females (mean ± SD: 6.209 km ± 5.085 vs. 2.752 km ± 3.234, F = 21.674, P < 0.01). When considering only

the 329 FP (150 for males and 179 for females), using a repeated measures ANOVA distance from nest

resulted significantly higher for males than for females (mean ± SD: 6.972 km ± 6.522 vs. 2.496 km ± 3.328, F

= 7.837, P < 0.01; Fig. 3).

155


IAEES www.iaees.org

4 Discussion

4.1 Sexual divergences in Lesser Kestrels’ movements

Comparisons between home-ranges and distances travelled by female and male Lesser Kestrels are present in

other studies as well (Catry et al., 2013; Tella et al., 1998). These studies have presented contradictory findings,

in some cases females showed smaller home-ranges but not in all cases.

We have found clear sexual divergences in Lesser Kestrels’ movements during the monitoring period.

Distance from nest was about 2.25 times higher for male than for females. When considering only foraging

points, distance from nest was about 2.8 higher for males than for females. GPS data also revealed a higher

amount of total movements for males (2707.7 km vs. 966.5 km). Hence, male movements have been more

frequent and more long-range than female ones. This is likely due to the fact that we focussed our study on the

hatching period, and not on the whole nestling one.

Owing to budgetary requirements of time and energy for reproduction and parental care, an upper limit to

female flight activities during the hatching period was expected as a consequence of two necessities: a)

spending as much time as possible in parental care, b) limiting energy requirements for resource acquisition. In

fact, although among Lesser Kestrels males and females both feed the chicks along the chick rearing period

until chick emancipation, in the first days after hatching the female stays longer periods with the chicks.

During 2012, in the study area 77 nests surveyed by the authors had a clutch of 3.79 ± 0.82 eggs. We might

expect that any sex divergence in Lesser Kestrel’s foraging behaviour was the product of their respective ways

to optimize the relationship between resource acquisition and reproductive activity (Emlen and Oring, 1977).

Sexual divergences in reproductive role were expected to translate into significant divergences in movements

patterns and resource use between males and females, at least during the hatching period.

The distances travelled by Lesser Kestrels also suggest that in the study area the foraging habitat is not

good. In fact, several authors have found values of less than 3 km away from colony when agriculture in the

surround of the colony of Lesser Kestrel was non-intensive (Bustamante, 1997; Tella et al., 1998). When

favourable habitat is available in the surroundings of the colony, foraging distances are small and males and

females may probably use the same fields to hunt (Catry et al., 2013; Tella et al., 1998). If the preferred habitat

around the colony is scarce, birds are expected to move further distances, and this is the case when differences

between males and females arise.

4.2 Lesser Kestrel’s foraging habitat selection

In our study area, Lesser Kestrels seem to prefer PS for foraging activities, suggesting that preys are more

accessible or more frequent in this land-use category. In the study area, these dry grasslands with scant trees

and flat relief present extensive cereal crop cultivation with harvested field that remain uncultivated for one or

more years (short-medium fallow), and are grazed by livestock herds. Livestock produces optimal conditions

for Lesser Kestrels’ breeding activity by making vegetation shorter and less dense, hence facilitating the access

to prey for Lesser Kestrels.

We also found that AL (cereals to a great extent, but also legumes, fodder crops, root crops and fallow land)

were used in proportion to their availability by Lesser Kestrels, and avoided by males. Avoidance of cereals

was also found by Ursúa et al. (2005) in the Ebro valley (North-East Spain). Therefore, our results confirm this

behaviour as a general pattern in the species. One possible explanation is that vegetation structure of cereals

makes foraging in this habitat complex at this time of year, since AL are denser and taller than PS, and they

might offer shelter to prey and/or obstruct hunting manoeuvres (Shrubb, 1980; Toland, 1987) hence reducing

access to prey for kestrels. In addition, in our study area, the use of biocides and fertilizers within AL is

common, which could have a negative affect on the abundance of insect prey, making this habitat less suitable

as hunting grounds for kestrels (BirdLife International, 2004; BirdLife International, 2013).

156


IAEES www.iaees.org

Avoidance of LC (olive groves, vineyards and fruit trees) and FO by foraging birds could be easily

expected for an open-habitat raptor such us the Lesser Kestrel, as it has been previously shown (e.g., Tella et

al., 1998). The 4 FP detected in HS were due to agricultural farms, as we were able to control on the digital

orthophotos of the study area provided by the Apulia Region.

The spatial distribution of PS and AL around the colony of Gravina is very dissimilar, with AL patches

significantly closer that PS ones. Although PS represent about 11% of HR95, they are almost exclusively

within the boundaries of the Alta Murgia National Park (2876.7 hectares out of 3936.1; Fig. 4) that is more

than 5 km distant from the colony.

In the remaining portion of the Lesser Kestrel’s HR95, PS have been almost completely replaced by AL,

FO and LC in the recent past. Agricultural expansion, that determined the increase of AL and LC, both with

the abandonment of marginally cultivated areas, that led to the progressive colonization by natural vegetation

(NG, SV and TR firstly, and FO secondly), have strongly reduced PS in the study area.

This has influenced the foraging selection by females (Fig. 3), since PS are almost completely absent, or at

least rather rare, in the smaller area surveyed by females (Fig. 4). In fact, 50% of female FP are less than 650

m distant from the colony, and only 19 female FP have been detected at a distance greater than 5 km. This

suggests that females, because of their greater effort in hatching activities, must be content with the kind of

foraging habitat they can accomplish within a reasonable distance from the colony. Males, instead, are less

limited in their foraging efforts during the hatching period, thus they can select more distant habitats. Hence

male Lesser Kestrels reveal the kind of more suitable habitat for foraging, independently of further limitations.

Fig. 4 Spatial configuration of the pseudo-steppes in the lesser kestrel’s home range. Although pseudo-steppes represent about 11% of the home-range, they are almost exclusively within the boundaries of the Alta Murgia National Park (2876.7 hectares out of 3936.1) that is more than 5 km distant from the colony. In the remaining portion of lesser kestrel’s home-range, pseudo-steppes have been almost completely replaced by arable lands, forests and ligneous crops in the recent past.

157


IAEES www.iaees.org

4.3 Implications for conservation

The first consequence of our results for management is that, during the hatching period, females are more

vulnerable than males, due to time-consuming parental care to offspring. Thus, conservation policies in the

study area should prevalently boost females rather than males. Since 50% of the FP for females were found

within 650 meters from the colony of Gravina (hence outside the Alta Murgia National Park), it follows that

conservation measures need to be more restrictive within that radius, and must consist of two main actions: a)

preserving all the patches at PS present, b) maintain non-intensive agriculture in AL as much as possible. The

first objective can be achieved by purchasing the few land parcels at PS using funds from the Apulia Regional

Plan for Rural Development 2014-2020. The cost of land parcels at PS is very low in the study area (about

1700 EURs per hectare; source: Apulia Regional Plan for Rural Development 2007-2013), therefore this

management policy is highly feasible. The second objective is less easy to accomplish, but it can be achieved

using incentives to farmers through funds from the Apulia Regional Plan for Rural Development 2014-2020 or

from the European Union.

The second implication is that PS are the most important habitat for the maintenance of the species in the

study area. PS can be found almost exclusively within the National Park, this demonstrating the importance of

this institution for the preservation of the Lesser Kestrel. However, many other patches at PS are within a

radius of few hundred meters from the boundary of the National Park. Hence, few small enlargements of the

park’s boundaries would result in the automatic preservation of hundreds of hectares at PS included in

colony’s HR95. This management option has already been indicated by the authors to the managers of the Alta

Murgia National Park, who in turn are discussing this topic with the municipality of Gravina and the Apulia

Region. It's clear that a further action is required to fully preserve the existing PS outside the park, and in

particular in the home range of the species.

The third implication for conservation planning is that Lesser Kestrels are forced to fly even 17 km away

from the colony to find food. This reveals that in the neighbourhood of the colony, intensive agriculture is

present that makes AL less attractive for foraging, as confirmed by our field surveys. As it seems unfeasible

from an economic viewpoint the distribution of incentives to maintain traditional agriculture over all the home

range (about 35,000 ha), the only feasible solution seems to be the creation of an ecological network of small

patches at PS in the Lesser Kestrel’s home range, in order to maintain suitable areas for foraging at distances

not too prohibitive for females, and energetically favourable for males. The most suitable land codes for this

kind of conversion to PS are NG and SV, which together total around 380 hectares, and whose acquisition cost

in the study area is rather low (about 1000 EURs per hectare; source: Apulia Regional Plan for Rural

Development 2007-2013). Furthermore, about 25 hectares out of 380 are within 650 m from the colony, hence

being of particular interest for the conservation of female Lesser Kestrels. A further useful conservation action

is the provision of suitable nesting sites near the higher quality areas (i.e. PS) individuated in this study (Pérez

et al., 2011).

Last, the pattern of cereal rotation means that the landscape around the colony is modified every breeding

season, influencing individual foraging decisions and patch use. Several authors (Donázar et al., 1993; Catry et

al., 2011) have highlighted the differences in foraging opportunities presented by each of the three cereal

stages (cereal, fields being harvested and stubble) and its impact on breeding success. During harvest, cereals

become a quality foraging habitat owing to an increase in prey accessibility caused by the sudden removal of

vegetation cover. The sequence in which patches are harvested influences the total amount of food delivered to

chicks and annual breeding success. The considerable impact of the timing in which cereal patches are

harvested highlights the interacting effect of spatial and temporal resource dynamics, which are likely to affect

158


IAEES www.iaees.org

the foraging and breeding success of Lesser Kestrels. Thus, ideally the cereals in the study area should be

harvested on a rotation covering the period of chick growth to maximise food abundance.

5 Concluding Remarks

In this study, focussed on the hatching period, we detected: a) the preferred foraging habitats for the Lesser

Kestrel within and outside the Alta Murgia National Park in Italy, b) the sexual divergences in foraging

movements and in c) foraging habitat selection, d) the relationship between foraging movements and the

spatial arrangement of habitats in the study area.

Although the detected Lesser Kestrel’s behaviour resulted rather clear, it was achieved in a specific period

(i.e., 20 days of the hatching period when the species is particularly vulnerable) when female Lesser Kestrels

remain a long time brooding the chicks, thus this could not be representative of the whole nestling period. For

this reason, in accord with the Alta Murgia National park we have already planned to extend our monitoring

efforts to the whole nestling period in 2014 and 2015. In order to detect if sexual divergences in foraging

selection of Lesser Kestrels are a prerogative of the reproductive period, we are planning to extend our surveys

to the pre-reproductive period as well.

Despite these limitations, our study is the first contribution to the assessment of the foraging strategies and

the necessary conservation policies for the Lesser Kestrel in Southern Italy.

Acknowledgements

We are grateful to the Alta Murgia National Park that funded this study.

References

Aebischer NJ, Robertson PA, Kenward R.E. 1993. Compositional analysis of habitat use from animal radio-

tracking data. Ecology, 74: 1313-1325

BirdLife International 2004. Birds in Europe: Population Estimates, Trends and Conservation Status. BirdLife

International, Cambridge, UK

BirdLife International 2013. Species factsheet: Falco naumanni. <http://www.birdlife.org>

Bustamante J. 1997. Predictive models for lesser kestrel Falco naumanni distribution, abundance and

extinction in southern Spain. Biological Conservation, 80:153-160

Bux M, Giglio G, Gustin M. 2008. Nest box provision for lesser kestrel Falco naumanni populations in the

Apulia region of southern Italy. Conservation Evidence, 5: 58-61

Byers CR, Steinhorst RK, Krausman P.R. 1984. Clarification of a technique for analysis of utilization-

availability data. Journal of Wildlife Management, 48:1050-1053

Catry I, Amano T, Franco AMA, Sutherland W.J. 2012. Influence of spatial and temporal dynamics of

agricultural practices on the lesser kestrel. Journal of Applied Ecology, 49: 99-108

Catry I, Franco AMA, Rocha P, Alcazar R, Reis S, Cordeiro A, Ventim R, Teodósio J, Moreira F. 2013.

Foraging habitat quality constrains effectiveness of artificial nest site provisioning in reversing population

declines in a colonial cavity nester. PLoS ONE, 8: e58320

Cherry S. 1996. A comparison of confidence interval methods for habitat use-availability studies. Journal of

Wildlife Management, 60: 653-658

Cramp S, Simmons KEL. 1980. The Birds of the Western Palearctic. Oxford University Press, Oxford, UK

159


IAEES www.iaees.org

Debussche M, Lepart J, Dervieux A. 1999. Mediterranean landscape changes: evidence from old postcards.

Global Ecology and Biogeography, 8: 3-15

Donázar J, Negro J, Hiraldo F. 1993. Foraging habitat selection, land-use changes and population decline in

the lesser kestrel Falco naumanni. Journal of Applied Ecology, 30: 515-522

Donald PF, Green RE, Heath M. F. 2001. Agricultural intensification and the collapse of Europe’s farmland

bird populations. Proceedings of the Royal Society of London B Biology, 268: 25-29

Emlen S, Oring L. 1977. Ecology, sexual selection and the evolution of mating systems. Science, 197: 215-223

Franco A, Andrada J. 1977. Alimentación y selección de presa en Falco naumanni. Ardeola, 23: 137-187

Gil-Tena A, Brotons L, Saura S. 2009. Mediterranean forest dynamics and forest bird distribution changes in

the late 20th century. Global Change Biology, 15: 474-485

Gustin M, Ferrarini A, Giglio G, Pellegrino SC, Frassanito A. 2014. First evidences of sexual divergences in

flight behaviour and space use of lesser kestrel Falco naumanni. Environmental Skeptics and Critics, 3(1):

1-7

Heath MF, Evans MI. 2000. Important bird areas in Europe: priority sites for conservation. Cambridge:

BirdLife International, Cambridge, UK

Hurlbert S.H. 1984. Pseudoreplication and the design of ecological field experiments. Ecological Monograph

54: 187-211

Janson S, Vegelius J. 1981. Measures of ecological association. Oecologia, 49: 371-376

Kok OB, Kok AC, Van Ee C. A. 2000. Diet of the migrant lesser kestrel Falco naumanni in their winter

quarters in South Africa. Acta Ornithologica, 35:147-151

Ludwig JA, Reynolds J F. 1988. Statistical ecology. John Wiley and Sons, New York, USA

Martínez C. 1994. Habitat selection by the Little Bustard Tetrax tetrax in cultivated areas of central Spain.

Biological Conservation, 67:125-128

Moreira F. 1999. Relationships between vegetation structure and breeding bird densities in fallow cereal

steppes in Castro Verde, Portugal. Bird Study 46:309–318

Negro J. J. 1997. Falco naumanni lesser kestrel. Birds of Western Palearctic Update, 1: 49-56

Neteler M, Mitasova H. 2008. Open Source GIS: A GRASS GIS Approach. Springer, New York, USA

Neu CW, Byers CR, Peek J. M. 1974. A technique for analysis of utilization-availability data. Journal of


Ostermann O.P. 1998. The need for management of nature conservation sites designated under Natura 2000.

Journal of Applied Ecology, 35: 968-973

Parr S, Collin P, Silk S, Wilbraham J, Williams NP, Yarar M. 1995. A baseline survey of lesser kestrels Falco

naumanni in central Turkey. Biological Conservation, 72: 45-53

Peet NB, Gallo-Orsi U. 2000. Action plan for the lesser kestrel Falco naumanni. Council of Europe and

BirdLife International, Cambridge, UK

Pérez I, Noguera JC, Mínguez E. 2011. Is there enough habitat for reintroduced populations of the lesser

kestrel? A case study in eastern Spain. Bird Conservation International 21: 228-239

Rodríguez A, Negro JJ, Bustamante J, Fox JW, Afanasyev V. 2009. Geolocators map the wintering grounds of

threatened lesser kestrels in Africa. Diversity and Distribution, 15: 1010-1016

Romero-Calcerrada R, Perry G. L. W. 2004. The role of land abandonment in landscape dynamics in the SPA

‘Encinares del rio Alberche y Cofio’, Central Spain, 1984–1999. Landscape and Urban Planning, 66: 217-

232

Sarà G. 2010. Climate and land-use changes as determinants of lesser kestrel abundance in Mediterranean

cereal steppes. Ardeola, 57: 3-22

160


IAEES www.iaees.org

Shrubb M. 1980. Farming influences on the food and hunting of kestrels. Bird Study, 27: 109-115

Sirami C, Brotons L, Martin JL. 2007. Vegetation and songbird response to land abandonment: from landscape

to census plot. Diversity and Distribution, 13: 42-52

Sirami C, Brotons L, Burfield I, Fonderflick J, Martin JL. 2008. Is land abandonment having an impact on

biodiversity? A meta-analytical approach to bird distribution changes in the north-western Mediterranean.

Biological Conservation, 141: 450-459

Stephens D W, Krebs JR. 1986. Foraging Theory. Princeton University Press, London, UK

Suárez-Seoane S, Osborne PE, Baudry J. 2002. Responses of birds of different biogeographic origins and

habitat requirements to agricultural land abandonment in northern Spain. Biological Conservation, 105:

332-344

Tella JL, Forero MG, Hiraldo F, Donázar JA. 1998. Conflicts between lesser kestrel conservation and

European agricultural policies as identified by habitat use analyses. Conservation Biology, 12: 593-604

Thomas D, Taylor E. 1990. Study designs and tests for comparing resource use and availability. Journal of


Toland BR. 1987. The effect of vegetative cover on foraging strategies, hunting success and nesting

distribution of American kestrels in central Missouri. Journal of Raptor Research, 21: 14-20

Tucker GM, Evans MI. 1997. Habitats for Birds in Europe: A Conservation Strategy for the Wider

Environment. Birdlife International, Cambridge, UK

Ursúa E, Serrano D, Tella JL. 2005. Does land irrigation actually reduce foraging habitat for breeding lesser

kestrels? The role of crop types. Biological Conservation, 122: 643-648

Worton B. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology, 70:

164-168

161


IAEES www.iaees.org

Article

Dynamics of 35 trace elements throughout plant organs in the

subalpine broad leaf evergreen shrub Rhododendron ferrugineum

Charles Marty1,2, André Pornon1, Thierry Lamaze2, Jérome Viers3 1Laboratoire Évolution et Diversité Biologique (CNRS-UMR 5174), Université Toulouse III, Bât. 4 R3, 118 route de Narbonne,

F-31062 Toulouse cedex 04, France 2Centre d’études spatiales de la biosphère (UMR CNES-CNRS-UPS-IRD 5126), 18 Av. Ed. Belin bpi 2801, 31401 Toulouse

cedex, France 3Géosciences Environnement Toulouse, Observatoire Midi Pyrénées CNRS – IRD-Université Paul Sabatier, 14 avenue Edouard

Belin, 31400 Toulouse, France

E-mail: [email protected]

Received 14 July 2014; Accepted 20 August 2014; Published online 1 December 2014

Abstract

Increased atmospheric deposition and climate change might affect soil biogeochemical processes and release

potentially toxic trace elements in the soil solution. The dynamics and the distribution among plant organs of

many trace elements are nevertheless still poorly documented, especially in evergreen species. Here,we

measured the concentration of 35 trace elements in roots, stems, as well as in current, 1 yr-old and 2 yr-old

leaves (respectively L0, L1 and L2) of the subalpine evergreen shrub Rhododendron ferrugineum. In every

plant compartment, concentrations decreased with increasing atomic number. Based on a PCA analysis and the

distribution of elements among the different plant compartments at least two groups of elements could be

distinguished: i) elements with a high retention factor (RF) in the root compartment and accumulating in

leaves with leaf aging, resulting in concentrations decreasing in the order Roots >> Stems > L2 > L1 > L0; and

ii) elements with a low RF resulting in leaf concentrations higher or close to those in roots and stems. However,

in contrast with elements from the first group, the dynamics in the leaf compartment of elements from the

second group was erratic, with concentrations increasing, decreasing or remaining constant with leaf aging.

Keywords Rhododendron ferrugineum; trace elements; retention factor; translocation; subalpine heathland.

1 Introduction

1 Introduction

Global warming and increased nitrogen and sulphur atmospheric depositions are known to alter

biogeochemical cycles by impacting physical, chemical and biological processes occurring in soils (Campbell

et al., 2009; Zhang and Liu, 2012). Atmospheric deposition has resulted in increasing soil acidity which can



IAEES www.iaees.org

increase the release and the mobility in the soil solution of metals potentially toxic like Al, Pb or Cd (Blake

and Goulding, 2002; Ediagbonya et al., 2013). The fate of these elements depends on several factors such as

soil properties and environmental conditions, which control their mobility in the soil and their

phytoavailability (e.g., pH, cation exchange capacity, clay and organic matter contents) (Greger, 2004). It also

depends on the ability of plants to take up and then translocate these elements from roots to the other plant

compartments (Perronnet et al., 2003; Anim et al., 2012). The uptake, translocation or accumulation of trace

elements across the different plant organs partly depend on their physicochemical properties (e.g. solubility,

mobility in the phloem) and roles in plant physiology, as well as on plant genetics and environment (Greger,

2004).

Numerous studies have focused on the uptake, translocation and storage of major elements (e.g. N, P, Ca)

in both agro- and ecosystems, but much less is known about trace elements (Watanabe et al., 2007). Yet, their

dynamics in the plants (e.g. uptake, translocation, storage) can have an impact on their cycling rate and on the

cycling of major elements representing large proportions of plant biomass (e.g., C, N, Ca). The uptake of some

key elements can actually impact the growth or the longevity of plant organs, particularly leaves and roots. For

instance, it has been shown that N plant uptake affected both the kinetics of leaf N resorption and leaf life span

in Rhododendron ferrugineum (Marty et al., 2009), that high Al accumulation in roots accelerated root

turnover in Abies amabilis (Vogt et al., 1987a, 1987b), that Cd decreased leaf longevity in wheat (Ouzounidou

et al., 1997), and that Cd and Zn inclusions in the soil stimulated root proliferation in Thlaspi caerulescens

(Schwartz et al., 2003), while other heavy metals can inhibit root elongation and growth of herbaceous species

(Baker et al., 1994; Bakkaus et al., 2005). It is therefore important to investigate element dynamics in the

plants in order to predict their cycling in the soil-plant system. Translocation of toxic elements from roots to

aboveground biomass could for instance result in their accumulation in the upper soil horizons through leaf

shedding and their dispersion through the food chain through herbivory.

Here, we measured the concentration of 35 trace elements in different plant compartments (roots, stems

and current, 1 yr-old and 2 yr-old leaves) during the growing season of a widespread evergreen plant in the

Pyrenean subalpine belt, Rhododendron ferrugineum. This species dominates most ericaceous heathlands of

European mountains and contrary to most evergreen species of high altitude habitats R. ferrugineum is

characterized by broad leaves. These oligotrophic ecosystems are particularly at risk because the parental

material is depleted in base cations and as a consequence have a low buffering power. Soils may therefore

acidify in response to even low atmospheric deposition. The aim of this study was to describe and compare the

distribution and dynamics of these 35 elements in the different plant organs and try to group them according to

their distribution patterns among plant organs.


2.1 Study site

The study was conducted in the central French Pyrenees in the vale of Estaragne. This valley (42° 48’ N; 0°9’ E)

is oriented North-east /South-west (opening to the north) and stretches over 3 km between 1850 and 2500 m

a.s.l. The vegetation is composed of a mosaic of meadows, shrubs and trees (Pinus uncinata Ram.) with long

heathland/meadow ecotones. Heathlands are mainly composed of Rhododendron ferrugineum L. and Vaccinium

myrtillus L. (Ericaceae). Nardus stricta L. and Festuca eskia Ram. are the main dominating species (Poaceae) of

the meadows.The subalpine climate prevailing in the site is relatively mild due to Ibero-Mediterranean

influences. Snow cover usually persists from late October till early June. The average annual precipitation

amounts to 1500 mm. The geological substrate is mainly granite, amphibole and schist. Soils are acidic (pH =

4.7 ± 0.1, SD; total N: 0.5% ± 0.044, SD; bulk density: 0.65 ± 0.099, SD). This site has been intensively studied

163


IAEES www.iaees.org

(Marty et al., 2009, 2010; Pornon and Lamaze, 2007). Soil field capacities and organic matter contents are

respectively 0.8 ± 0.1 g g-1 DW and 11.7 ± 1.3 %.

2.2 Vegetation sampling

The species studied, Rhododendron ferrugineum, is an evergreen shrub, with well-branched trailing stems that

reaches a height of 70-80 cm. It is widely distributed in the Alps and the Pyrenees between 1600 and 2200 m

a.s.l. (Ozenda, 1985)where it can dominate plant communities especially in areas where grazing pressure has

subsided. Chemical analyses were conducted on five compartments: roots (R), stems (S) and current, 1 yr-old

and 2 yr-old leaves (respectively L0, L1 and L2). Every compartment was collected three times in the

vegetation period on mature shrubs: mid-June, mid-August and end-October. These periods match with the

beginning of the vegetation period, the end of shoot growth and the end of the vegetation period respectively.

Ten sub-areas (50 m2 each) were delimited. Inside each sub-area, plant compartment samples were collected on

four shrubs and pooled together so that we obtained ten replicates of each compartment from forty individuals

three times during the whole vegetation period. Samples were immediately refrigerated before they were

meticulously rinsed with ultrapure water (Milli-Q integral system) in the laboratory. Then, they were oven-dried

for 72h at 60°C and ground in fine powder (Ø<10µm) in an agate mortar.

2.3 Multi-elementary analysis

For each plant sample, series of oxidizing acid attacks (bidistilled HNO3, HF and HCl) were conducted on 100

mg powder in Teflon reactors (Savillex®). Details about the whole procedure are elsewhere (Viers et al.,

2007). The dry residual was then weighted and diluted in bi-distilled nitric acid (2%) for multi-elementary

analyses.

Trace elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS; 7500 CE,

Agilent Technologies).

2.4 Calculations and statistics

Differences in elements concentrations among plant compartments were tested with one-way ANOVA

followed by a Turkey post hoc analysis (R core Development Core Team, 2009). A principal component

analysis (PCA) was performed to analyze data on the concentrations of 35 elements in the different plant

compartments using R package ade4 (Dray and Dufour, 2007). For each element, a retention factor (RF) was

calculated as the ratio between the concentration in roots and that in stems (R-S), 2-yr old leaves (R-L2), 1-yr

old (L1) and current-year leaves (R-L0).

Annual translocation from roots to the foliage (AT) was estimated for each element. Some elements like

barium have been shown to accumulate in R. ferrugineum’s leaves with leaf aging while others like rubidium

are partly resorbed from leaves over their life span (Marty et al., 2014). Therefore, for elements with

decreasing concentrations with leaf age (concentrations decreasing in the order L0>L1>L2), AT was estimated

as the amount of the element in the L0 cohort at the end of the growing season minus the amount of the

element resorbed from the L1 and L2 cohorts (eqn 1). In contrast, for elements with increasing concentrations

with leaf age (concentrations decreasing in the order L0<L1<L2), the amount of element accumulated in the

remaining L1 and L2 leaves must be accounted and AT was estimated as shown in eqn 2.

ATj mL0 j CL0 j mL0 CL0 j CL1 j mL1 CL1 j CL2 j (1)

ATj mL0 CL0 j mL1 CL1 j CL0 j mL2 CL2 j CL1 j (2)

164


IAEES www.iaees.org

where, ATj is the annual translocation to leaf biomass (µg m-2) of the element j, mL0 and mL1 are the

biomasses of the L0 and L1 cohorts (g DW) at the end of the growth period respectively, and CL0j, CL1j and

CL2j (µg g-1) are the concentrations of the element j in L0, L1 and L2 cohorts, respectively.

Leaf biomass values for the L0, L1 and L2 cohorts (mL0, mL1 and mL2, respectively) were estimated as

the product of leaf surface area for L0 (S, m2) with the LMA and the proportion (pi) of leaves from the L0

cohort still attached after respectively one and two years provided in Marty et al. (2009):

mLi SL 0 LMAi pi (3)

The AT values were multiplied by branch density (number of branches m-2 of shrub) to have a value of AT by

m-2 of shrub. Annual leaf surface area production (S, m2) and leaf mass area (LMA; g m-2) for L0 as well as

leaf shedding patterns data were extracted from Marty et al. (2009) and Marty et al. (2010). Branch density

(number of branches m-2) was measured on 40 shrubs within a frame of 35cm x 35cm.

3 Results

Concentrations in plant tissues decreased with increasing element atomic numbers in all plant compartments

(Fig. 1). The correlation was slightly stronger for leaves (especially current-year leaves, L0) than for roots and

stems.

The two first axes of a principal component analysis (PCA) explained about 70% of the total variation in

elemental concentrations (Fig 2 top panel). On the very left side of the first axis (PC1 values<-0.92), was

found a group of 16 elements including all analyzed lanthanides with the exception of Eu (La, Ce, Pr, Nd, Sm,

Gd, Tb, Dy, Ho, Er and Yb), as well as Al, Ti, V, Fe and U. Between PC1=-0.91 and PC1=-0.56, was found

another group including Th, Eu, Pb, Co, Cr and Sb. For PC1>-0.55, the 13 other elements were spread on the

first axis and no group could be clearly distinguished. On the second axis, most elements were included in the

interval -0.25<PC2<0.25. However, Ba, Sr, Mn and Ni had PC2 values>0.5 while Cu and Rb had PC2

values<-0.5.

When plant compartments were projected on the principal component axes, roots (R) were located on the

left side of the PC1 axis whereas the three leaf cohorts (L0, L1 and L2) were located at the right extremity of

the PC1 axis, and stems (S) in between these two compartments (Fig. 2 bottom panel). Current year leaves (L0)

were opposite to older leaves (L1 and L2) on the PC2 axis.

Elements were sorted according to their PC1 values and their concentrations in the different plant

compartments are displayed on Fig. 3. For the 22 elements with the lowest PC1 values and europium (Eu),

concentrations decreased in the order: Roots > Stems > L2 > L1 > L0. In contrast, the 12 remaining elements

displayed various patterns. However, for most of them concentrations in the different leaf cohorts were higher

or close to those in roots and stems (e.g. Li, B, Ni, and Zn). The four most abundant elements among analyzed

plant compartments were Mn, Al, Fe and Ba. In stems, L2, L1 and L0, Mn was the most abundant element,

followed by Al, Fe and Ba (Supplementary material). In roots, Al was the most abundant element (1040 µg g-1)

followed by Mn (675 µg g-1) and Fe (650 µg g-1). Barium concentration in roots was comparatively very low

(53 µg g-1). In comparison, other elements had very low concentrations, most of them with concentrations

lower than 1 µg g-1(Supplementary material).

The retention factor (RF), i.e. the ratio between the concentration in roots and that in the other

compartments, widely varied among elements and compartments (Fig. 5). Between roots and stems, RF ranged

from 0.8 for boron (B) and 11.1 for cobalt (Co). However, the value for Co was excessively high compared to

the other elements since the second highest value reached only 5.2, and 75% of the values were included in the

interval 0.8-3.8. This RF was not correlated with elements’ atomic numbers (R=0.18; P=0.29).

165


IAEES www.iaees.org

Fig. 1 Relationship between log-transformed concentrations of 35 trace elements in R. ferrugineum tissues and their atomic numbers. For each element, value is the mean of 30 analyses (10 sub-areas × 3 sampling periods). See materials and methods for details.

166


IAEES www.iaees.org

Fig. 2 Principal component analysis (PCA) of concentrations of 35 elements in plant compartments. Top: Projection of the different elements analyzed in the two principal axes space. Bottom: Projection of the different plant compartments in the two principal axes space. Each ellipse is a graphic résumé of the point cloud, the centre of which representing the gravity centre.

167


IAEES www.iaees.org

Fig. 3 Mean concentrations (µg g-1) of the 35 analyzed trace elements in R. ferrugineum’s compartments. Elements are sorted as a function of their coordinate on the first axis of the PCA presented on Fig 2. PC1 values increase from right to left and from top to bottom. Errors bars are SE.

168


IAEES www.iaees.org

Fig. 4 Mean concentrations (µg g-1) of the 35 analyzed trace elements in R. ferrugineum’s roots, stems, 2-yr old leaves (L2), 1-yr old leaves (L1) and current-yr leaves (L0). Errors bars are SE.

169


IAEES www.iaees.org

Fig. 5 Retention factor (RF) between roots and stems (R-S), 2-yr old leaves (R-L2), one yr-old leaves (R-L1) and current-year leaves (R-L0) for 35 trace elements. The retention factor is calculated as the ratio between the concentration in roots and that in the other compartments. Elements’ atomic number increases from the left to the right of the x-axis.

Fig. 6 Mean Retention Factor (+ SE) for the 35 analyzed elements. The group of elements characterized by a low PC1 value as well as by concentrations decreasing in the order R>S>L2>L1>L0 (Group 1) is shown.

170


IAEES www.iaees.org

Fig. 7 Annual translocation to the foliage (µg m-2 yr-1) for each analyzed element.

The RF between roots and all leaf cohorts was high and similar for all elements with an atomic number

higher than 57 (at the right of lanthanum on Fig. 5) with the exception of europium (Eu) for which RF for all

leaf cohorts was only slightly higher than for stems. The RF between roots and leaves was particularly high for

lead (Pb), yttrium (Y) and cobalt (Co) with RFR-L0 averaging 46, 30 and 24, respectively. For most elements,

RF decreased with leaf age (R-L0>R-L1>R-L2) with the exception of 12 elements: Li, B, Mn, Ni, Cu, Zn, Ga,

Rb, Sr, Mo, Cs and Ba. For these elements, the RF for leaves was similar, slightly higher or even lower than

for stems. The RF for L0, L1 and L2 was positively correlated with elemental atomic number (R=0.42, P<0.05;

R=0.35, P<0.05; R=0.44, P<0.01, respectively).

The amount of element annually translocated from roots to the foliage varied by several orders of

magnitude (Fig. 7), from ~0.5 µg m-2 yr-1 for homium (Ho) and terbium (Tb) to ~100 mg m-2 yr-1 for

manganese (Mn). Only ten elements had annual translocation higher than 1 mg m-2 yr-1 (Ni < Ti <Sr<Rb< B <

Zn <Ba< Fe < Al <Mn). Most elements had an annual translocation to the foliage <10 µg m-2 yr-1.

4 Discussion

4.1 Elements retention in the root compartment

As expected we found a large discrepancy in concentrations among elements and plant compartments. In every

compartment, concentrations decreased with elements’ atomic number which is in line with Watanabe et al.

(2007) who observed the same patterns on 670 species of terrestrial plants, sampled from 29 sites in four

countries. The fact that roots and the three leaf cohorts were opposite on the PC1 axis reflected the markedly

different chemical composition of these compartments. Concentrations were generally higher in roots than in

leaves, especially for the 22 elements with the lowest PC1 coordinate (Fig. 3). These elements with yttrium (Y)

could be assembled in one group (hereafter Group 1) characterized by high retention factors (RF) (Fig. 6) and

a slight accumulation in leaves as they aged which resulted in the following concentration pattern:

R>>S>L2>L1>L0 (Fig. 3). For this group of elements, concentration was on average i) 3.8 times higher in

171


IAEES www.iaees.org

roots than in stems with a range of 1.7 times for chromium (Cr) to 11.1 times for cobalt (Co); ii) 14.1times

higher in roots than in L0 with a range of 3.3 to 46.3 for europium (Eu) and lead (Pb), respectively; iii) 2.6

times higher in L2 than in L0 with a range of 1.2 to 3.4 for antimony (Sb) and lead (Pb), respectively. The 12

remaining elements (hereafter Group 2) including molybdenum (Mo), copper (Cu), manganese (Mn), cesium

(Cs), lithium (Li), gallium (Ga), zinc (Zn), barium (Ba), strontium (Sr), nickel (Ni), rubidium (Rb) and boron

(B) showed various concentration patterns but were all characterized by low RFs (Fig. 6). This generally

resulted in relatively high concentrations in leaf cohorts compared to elements from Group 1.

Although RF tended to increase with atomic number some elements with a relatively low atomic number

had a high RF (e.g., Al, V, Co) and some with a high atomic number had a low RF (e.g., Ba, Rb, Sr, Eu).

Elements like aluminum (Al) and cobalt (Co) are known to be toxic for plants. High retention of these

elements in the roots corroborates the hypothesis that in acidic soils, root biomass plays an important role in

sequestration of toxic trace elements (Vogt et al., 1987b). This accumulation followed by root senescence and

death could actually avoid the toxicity to propagate in all the biological components. However, despite its

toxicity and its high RF, Al was the second most abundant trace element in the foliage after Mn

(Supplementary material). This might result from its high concentration in the soil solution in phytoavailable

form (see below). In contrast, barium (Ba) and strontium (Sr), two alkaline earth elements, as well as rubidium

(Rb), an alkali element, had a low RF. This probably resulted from their physico-chemical properties (Aberg et

al., 1990), which are similar to those of calcium (Ca) for the two firsts and potassium (K) for the latter, and

which confer them similar dynamics in the plant-soil system (see below).

Trace elements that are known to be essential for plants are boron (B), iron (Fe), zinc (Zn), copper (Cu),

manganese (Mn), cobalt (Co), nickel (Ni) and molybdenum (Mo) (Graham and Stangoulis, 2003; Marschner,

1995). With the exception of Fe and particularly Co that had a very high RF and were characterized by the

pattern R>>S>L2>L1>L0, all these essential elements were weakly retained in the roots (RF ranging from 0.4

for B to 2.3 for Mo). However, despite its high RF, Fe concentrations in the leaves were very high compared to

the other essential elements (Fig. 3), suggesting that their high retention in the roots resulted from high element

availability compared to leaf requirements. In contrast, Co concentrations in the roots and leaves were very

low suggesting that the high retention of this element in the roots resulted from the very small amounts

required by the plants and from its toxicity at high concentrations. Cobalt concentrations in plants are actually

generally as low as 0.1–10 μgg-1 (Palit et al., 1994) although concentrations can quickly increase in some crops,

especially in roots, with increasing concentrations in the soil solution (Bakkaus et al., 2005). Concentrations of

these essential elements were generally higher in roots than in the other plant compartments, with the

exception of Zn, Ni and B (Fig. 3). In many soils, B can be taken up as a neutral molecule, which is highly

permeable across cellular membranes (Stangoulis et al., 2001). Its transport throughout the plant is facilitated

when external concentrations are low like in acidic alpine soils. Its passive uptake and its high transport ability

could explain why B concentration is significantly higher in leaves than in stems and roots, which was not the

case for most of the studied trace elements.

4.2 Elements dynamics in the leaf compartment

As mentioned above, concentration in leaves increased with leaf age for all elements from Group 1. This trend

was also observed for some elements from Group 2: Mn, Ga, Ba, Sr and Ni. In contrast, concentrations in Cu,

Zn, Cs and Rb tended to decline with leaf age. These different dynamics probably contributed to the opposite

locations of these two groups of elements on the second axis of the PCA (Fig. 2). Indeed, Mn, Na, Sr and Ni

had high positive PC2 values, while Cu, Zn and Rb had low negative PC2 values. These different patterns

might result from elements’ physico-chemical properties (e.g. lack of mobility in the phloem) and their

physiological roles in the plant. For instance, root uptake is thought to not distinguish between Rb and K

172


IAEES www.iaees.org

because of close ionic radii and similar valences (Marschner, 1995). These similarities in atomic properties

could explain Rb high concentrations in leaves since K+ optimal cytosolic concentration is in the range of 100

mM (Ashley et al., 2006), which makes K+ the most abundant cation in the cytosol (Véry and Sentenac, 2003).

In addition, K high solubility in the cytosol makes its retranslocation from senescent leaves through the

phloem possible (Véry and Sentenac, 2003). These similar physico-chemical properties might explain the very

close dynamics of these two elements in R. ferrugineum (Marty et al., 2014). The retranslocation process

might therefore explain the decrease in the concentration of Rb and other elements like Cu and Zn with leaf

age, as the latter are also known to be efficiently retranslocated from leaves, at least in crops (Marschner,

1995). As it has been shown for nitrogen, this resorption process might contribute to annual growth in R.

ferrugineum (Marty et al., 2009, 2010; Pasche et al., 2002). In contrast, Sr and Ba accumulated in leaves with

time and were shown to have the same dynamics as Ca in R. ferrugineum (Marty et al., 2014), resulting from

similar physico-chemical properties. Strontium’s ionic radius is close to that of Ca (1.00 and 1.18 Å for Ca and

Sr, respectively), which can give these elements similar dynamics in the soil-plant system (Aberg et al., 1990;

Poszwa et al., 2000). Barium, another alkaline-earth with a close ionic radius (1.35 Å), can also have a similar

dynamics in soil-plant system (Suwa et al., 2008). Although Ca is one of the most abundant elements in plant

tissues, cytosolic concentration is maintained at submicromolar levels because of a low solubility (White and

Broadley, 2003). These properties prevent Ca and hence Sr and Ba from being translocated from leaves to

other organs via the phloem and result in Ca, Sr and Ba accumulation with leaf age. Such low solubility in the

cytosol and therefore low mobility in the phloem could be responsible for the accumulation of all elements

from group 1.

Annual translocation to the foliage strongly varied among the analyzed trace elements. The four elements

with the highest concentrations in the analyzed plant compartments (Mn, Al, Fe and Ba) were also the

elements with the highest annual translocation to the foliage, which ranged from 10 to 100 mg m-2 yr-1. The

annual translocation to the foliage of Sr, Rb, B and Zn was >1 mg m-2 yr-1. With the exception of Al, all these

elements are either essential elements (B, Zn, Fe and Mn) or analogs of essential elements (Sr, Rb and Ba).

Aluminum can be abundant in the parental rock and occur in the soil solution in nonphytotoxic forms, which

might explain the high concentrations in roots (Fig 3). Nevertheless, the high translocation of Al is surprising

since Al is known to be highly toxic for plants even at low concentrations (Delhaize and Ryan, 1995). This

annual translocation to the foliage might result in the incorporation of a similar amount of element into the top

layer of the soil through litter production. The amount of Al returning to the soil annually through litter (65 mg

m-2 yr-1) is nevertheless very low compared to major elements. Indeed, using concentrations values provided in

Marty et al. (2014), we estimated that Ca and K annual translocation to the foliage was 1.9 g m-2 yr-1 and 0.9 g

m-2 yr-1, respectively. Therefore, it takes approximately 20 times longer to bring up a given amount of Al from

the mineral soil to the top layer compared to Ca through this process.

5 Conclusion

The present data allowed us to distinguish at least two groups of elements among the 35 analyzed based on

their distribution throughout the plant organs. The first group included 23 elements characterized by a high

retention in the roots and accumulation in leaves with leaf aging. There was a positive relationship between

retention in the roots and atomic number but this latter did not appear to be the only determinant factor since

some elements with low atomic number (e.g., Al, Co) were highly retained in the roots. This retention might

be an adaptation to impede the propagation of toxic elements to other plant organs. On the other hand, we

found elements, most of them being essential for plants, which were weakly retained in the root compartment

and distributed to the leaves where they either accumulated or were resorbed with leaf aging. More research

173


IAEES www.iaees.org

should be conducted to verify whether the observed patterns in R. ferrugineum are universal or related to

phylogenetic, functional or ecologic characteristics as it has been previously shown for leaf element

composition (Watanabe et al., 2007). In addition, the different dynamics of the two groups of elements may

impact element distribution throughout the soil profile. Concentrations of elements with high retention in the

roots might remain very low in the upper horizons while those of elements with low retention could

accumulate in the upper horizons through litter inputs.

References

Aberg G, Jacks G, Wickman T, Hamilton PJ. 1990. Strontium isotopes in trees as an indicator for calcium

availability. Catena, 17: 1-11

Anim AK, Laar C, Osei J, et al. 2012. Trace metals quality of some herbal medicines sold in Accra, Ghana.

Proceedings of the International Academy of Ecology and Environmental Sciences, 2(2): 111-117

Ashley MK, Grant M, Grabov A. 2006. Plant responses to potassium deficiencies: a role for potassium

transport proteins. Journal of Experimental Botany, 57(2): 425-436

Baker A, Reeves R, Hajar A. 1994. Heavy metal accumulation and tolerance in British populations of the

metallophyte Thlaspi caerulescens J .& C. Presl. New Phytologist, 127: 61-68

Bakkaus E, Gouget B, Gallien JP, et al. 2005. Concentration and distribution of cobalt in higher plants: The

use of micro-PIXE spectroscopy. Nuclear Instruments and Methods in Physics Research B, 231(1-4): 350-

356

Blake L, Goulding KWT. 2002. Effects of atmospheric deposition , soil pH and acidification on heavy metal

contents in soils and vegetation of semi-natural ecosystems at Rothamsted Experimental Station, UK. Plant

and Soil, 240: 235-251

Campbell JL, Rustad LE, Boyer EW, et al. 2009. Consequences of climate change for biogeochemical cycling

in forests of northeastern North America. Canadian Journal of Forest Research-Revue Canadienne De

Recherche Forestiere, 39: 264-284

Delhaize E, Ryan PR. 1995. Aluminum Toxicity and Tolerance in Plants. Plant Physiology, 107(2): 315-321

Dray S, Dufour AB. 2007. The ade4 package: implementing the duality diagram for ecologists. Journal of

Statistical Software, 22(4): 1-20

Ediagbonya TF, Ukpebor EE, Okieimen FE. 2013. Heavy metal in inhalable and respirable particles in urban

atmosphere. Environmental Skeptics and Critics, 2(3): 108-117

Graham RD, Stangoulis JCR. 2003. Comparative trace element nutrition -trace element uptake and distribution

in plants. Journal of Nutrition, 1502-1505

Greger M. 2004. Metal availability, uptake, transport and accumulation in plants. In: Heavy Metal Stress in

Plants-From Biomolecules to Ecosystems (Prasad M, ed). Springer-Verlag, Berlin Heidelberg, Germany

Marschner H. 1995. Mineral Nutrition of Higher Plants. Academic Press London, UK

Marty C, Lamaze T, Pornon A. 2009. Endogenous sink-source interactions and soil nitrogen regulate leaf life-

span in an evergreen shrub. New Phytologist, 183(4): 1114-1123

Marty C, Lamaze T, Pornon A. 2010. Leaf life span optimizes annual biomass production rather than plant

photosynthetic capacity in an evergreen shrub. New Phytologist, 187: 407-416

Marty C, Pornon A, Lamaze T, Viers J. 2014. Calcium and potassium dynamics and biopurification in two

populations of the subalpine evergreen shrub Rhododendron ferrugineum. Proceedings of the International

Academy of Ecology and Environmental Sciences, 4(2): 50-61

174


IAEES www.iaees.org

Ouzounidou G, Moustakas,M, Eleftheriou EP. 1997. Physiological and ultrastructural effects of cadmium on

wheat (Triticumaestivum L.) leaves. Archives of Environmental Contamination and Toxicology, 32(2):

154-160

Ozenda, P. 1985. La végétation de la chaine Alpine dansl’espacemontagnardeuropéen. Masson, Paris, France

Palit S, Sharma A, Talukder G. 1994. Effects of cobalt in plants.The Botanical Review, 60(2): 149-181

Pasche F, Pornon A, Lamaze T. 2002. Do mature leaves provide a net source of nitrogen supporting shoot

growth in Rhododendron ferrugineum? New Phytologist, 154: 99-105

Perronnet K, Schwartz C, Morel JL. 2003. Distribution of cadmium and zinc in the

hyperaccumulatorThlaspicaerulescens grown on multicontaminated soil. Plant and Soil, 249: 19-25

Pornon A, Lamaze T. 2007. Nitrogen resorption and photosynthetic activity over leaf life span in an evergreen

shrub, Rhododendron ferrugineum, in a subalpine environment. New Phytologist, 175: 301-310

Poszwa A, Dambrine E, Pollier B, Atteia O. 2000. A comparison between Ca and Sr cycling in forest

ecosystems. Plant and Soil, 225: 299-310

Schwartz C, Echevarria G, Morel JL. 2003. Phytoextraction of cadmium with Thlaspicaerulescens. Plant and

Soil, 249: 27-35

Stangoulis JCR, Reid RJ, Brown PH, Graham RD. 2001. Micronutrient nutrition of plants. Critical Reviews in

Plant Sciences, 14: 49-82

Suwa R, Jayachandran K, Nguyen NT, et al. 2008. Barium toxicity effects in soybean plants. Archives of

Environmental Contamination and Toxicology, 55: 397: 403

Véry AA, Sentenac H. 2003. Molecular mechanisms and regulation of K+ transport in higher plants. Annual

Review of Plant Biology, 54: 575-603

Viers J, Oliva P, Sonke J, et al. 2007. Evidence of Zn isotopic fractionation in a soil-plant-system of a pristine

tropical watershed (Nsimi, south Cameroon). Chemical Geology, 239: 124-137

Vogt K, Dahlgren R, Ugloni F, et al. 1987a. Aluminum, Fe, Ca, Mg, K, Mn, Cu, Zn and P in above- and

belowground biomass. I. Abiesamabilis and Tsugamertensiana. Biogeochemistry, 4: 277-294

Vogt K, Dahlgren R, Ugloni F, et al. 1987b. Aluminum, Fe, Ca, Mg, K, Mn, Cu, Zn and P in above- and

belowground biomass. II. Pools and circulation in a subalpine Abiesamabilis stand. Biogeochemistry, 4:

295-311

Watanabe T, Broadley MR, Jansen S, et al. 2007. Evolutionary control of leaf element composition in plants.

New Phytologist, 174: 516-523

White PJ, Broadley MR. 2003. Calcium in plants. Annals of Botany, 92(4): 487-511

Zhang WJ, Liu CH. 2012. Some thoughts on global climate change: will it get warmer and warmer?

Environmental Skeptics and Critics, 1(1): 1-7

175


IAEES www.iaees.org

Article

Assessment of metal bioaccumulation in Clarias batrachus and

exposure evaluation in human

Mayank Pandey1, Ashutosh Kumar Pandey1, Ashutosh Mishra1, B. D. Tripathi2 1Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, 221005, India 2Department of Botany, Banaras Hindu University, Varanasi, 221005, India


Received 27 June 2014; Accepted 1 August 2014; Published online 1 December 2014

Abstract

The present work was conducted for heavy metal (Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd and Pb) quantification in the

river Ganga water and their bioaccumulation in vital organ tissues of Clarius batrachus. Heavy metal

characterization in vital organ tissues (gills, liver and muscle) and comparison with FAO permissible

guidelines revealed that Cd and Pb were hyper-accumulated which may lead to metal toxicity in fish and its

consumers. High metal pollution index (MPI) was recorded for organ tissues of exposed samples (liver 6.05;

gills 22.95; muscle 44.48) as compared to unexposed samples (liver 4.5; gills 18.8; muscle 36.76). Effective

ingestive dose (EID) was calculated to assess the exposure threat to the human which may occur through

dietary inputs. Results revealed that EID for Cr, Co, Cd and Pb was found significantly higher than the dose

concentration prescribed by USEPA.

Keywords Clarius batrachus; river Ganga; metal; bioaccumulation; ingestion dose.

1 Introduction

1 Introduction

Metals discharged from the anthropogenic sources (industries, mining, tannery etc.) have become potential

threat for aquatic ecosystem. Metals are believed to be potent toxic substances due to their slow degradation

rate and long half-life period (Chabukdhara and Nema, 2012; Jain, 2004; Kelepertzis et al., 2012; Prajapati et

al., 2012). Through various paths, metals enter the food chain/web and ultimately cause adverse physical and

physiological effects on biotic elements of earth and get accumulated in flora and fauna, which is called

bioaccumulation. Being at the top of the trophic level, human gets most affected by bioaccumulation and bio

magnification (Chi et al., 2007; Mishra and Mohanty, 2008; Alhashemi et al., 2011; Garg et al., 2014;

Goodyear and McNeill, 1999). Minamata disease (mercury), itai-itai (cadmium), lead leprosy, metal fume

fever, arsenic poisoning in South East Asia are few examples to depict the catastrophic effects of metals at

higher concentration.



IAEES www.iaees.org

Fish is considered an excellent biomarker of metal pollution in aquatic ecosystem for many reasons; it

occupies higher trophic level in an aquatic ecosystem, metal toxicity adversely affects the physical and

physiological behavior of the fish and it is an important constituent of non-vegetarian diet of human. High

quality of protein, amino acid (lysine, sulfur containing amino acids etc.), iodine, calcium, trace elements (Mn,

Fe, Cu, Zn, Se etc.), vitamins and omega-3-polyunsaturated fatty acid are obtained from fish (Ashwani and

Ashok, 2006; Mishra and Mohanty, 2008; Rani and Sivaraj, 2010). Therefore, human consumption of fish as

dietary component may lead to severe health hazards to the mankind. Minamata disaster is an example for the

same (McCurry, 2006).

Gills are respiratory and osmo-regulatory organs having large surface area which interacts with dissolved

metal in the surrounding for maximum period. Also, the detoxification system of gills is not as strong as that of

liver. High metal concentration in gills reflects high metal concentration in dissolved phase. High absorption

rate of toxic elements by gills causes high toxic concentration in gills (Pandey et al., 2008). High metal

concentration in liver indicates dietary input of metal as liver is the storage organ. Muscle tissue is an inactive

site of metal accumulation (Alhashemi et al., 2012). Bioaccumulation Factor (BAF) and Metal Pollution Index

(MPI) are few indices for the assessment of degree of metal accumulation (Vaseem and Banerjee, 2013).

Workers have assessed effective ingestive dose (EID) for heavy metals in human by the consumption of metal

accumulated fish (Alhashemi et al., 2012).

River Ganga pollution is a challenge to the scientific community and policy makers in India.

Anthropogenic factors (discharge from agriculture, domestic and industrial sectors) are the prime cause of

river pollution. A descriptive study on metal bioaccumulation in C. batrachus in river Ganga is not been done

so far. Therefore, the main objective of the present study was to assess metal concentration in river water vis-à-

vis their accumulation in vital organ tissues of fish. Ingestion dose of metals through diet (fish) by human

beings was also determined as described elsewhere (Alhashemi et al., 2012).


2.1 Study region

Varanasi (25016´55ʺN 82057´23ʺE, 76m amsl) is situated at the left bank of Ganga, the national river of India.

Varanasi is known for its dense population and large number of industries (locomotive and metal works;

textile and dye industries, glass) which discharge effluent having high concentration of heavy metals.

Industrial effluents directly get mixed into city sewage and ultimately discharged into the river due to

unavailability of metal removing technologies (Tripathi et al., 1991).

2.2 Sampling and analysis

River water sampling (PTFE bottles; prewashed and acidified) was done from sixteen points between

Shooltankeshwar (S0) and Ganga-Varuna confluence (S15), following standard protocols (APHA, 2005).

Reference site (S0) was chosen at upstream while remaining sampling points (S1-S15) were along the

downstream. Length of entire river stretch was c.a. 20 km (S0-S1 10km; S1-S15 10 km). Water samples were

carried immediately after sampling to the laboratory at 40C.

Seven samples of Clarius batrachus (Mangur) were caught by using traditional net method with the help of

professional fishermen from reference and downstream stretch. It was tried to maintain homogeneity in weight

and length of samples. Fish samples were kept and transferred immediately to the laboratory in river water.

Fish were anaesthetized and dissected to get liver, gill and muscle tissue (mid abdomen region). Organ tissues

were acid digested (3HNO3:1HCl:1HClO4 v/v) in Teflon vessels for 60-120 minutes to get a clear solution was

obtained. Volume of digested samples was made up to 100 ml using Millipore water and metal analysis was

done by flame Atomic Absorption Spectrophotometer (AAS) (AAnalyst 800-Perkin Elmer). Calibration of

177


IAEES www.iaees.org

AAS was done with metal standard solutions after every 15 samples. Statistical significance of the results was

assessed by applying independent sample t test.

3 Results and Discussion

Heavy metal concentration was considerably higher from S1-S15 than at reference site (S0) (Table 2).

Anthropogenic sources (industrial, domestic and agricultural effluents) are the principal cause of high metal

concentration in downstream river water. Large drain confluence (S1 and S15), dead body cremation (S7 and

S9) and mass bathing (S8) are also chief pollutant sources to the river Ganga. There are reports suggesting that

developmental stages of fish are negatively affected by higher metal concentration in river water. It has been

established that there is a negative linear regression between metal concentration and size of fish (Canli and

Atli, 2003).

The objective of the present work was to assess the degree of metal accumulation (wet weight; mgkg-1) in

vital organ tissues of studied fish samples. Simultaneous quantification of heavy metals in river water and fish

tissues was done. Metal concentration (wet weight, mgKg-1) in tissues (Gill, Liver and Muscle) of downstream

fish samples were compared with samples of reference site and FAO permissible limit (FAO, 1983). Metal

accumulation indices like bioaccumulation factor (BAF) and metal pollution index (MPI) were calculated as

discussed below (Vaseem and Banerjee, 2013):

BAF = Cf/Cw

where, Cf is concentration of metal in fish organ (mgkg-1) and Cw is metal concentration in river water (mgl-1).

MPI = (Cf1 x Cf2 x Cf3 x… Cfn)1/n

where Cfn is concentration of metal n in the given sample

Organosomatic index (OSI) was calculated to assess the proportional size change in the gills (gill somatic

index) and liver (hepato somatic index) of exposed and unexposed fish samples. The hepato-somatic index

percentage (HSI %) was found almost same for exposed (0.48 ± 0.02 %) and reference (0.48 ± 0.01 %)

samples. However, gill somatic index (GSI %) of exposed sample was found higher in exposed sample (1.6 ±

0.02 %) as compared to reference sample (1.51 ± 0.02 %), indicating metal accumulation in gills.

Metal pollution index (MPI) for gills (22.95), liver (6.05) and muscle (44.48) of exposed sample was found

significantly higher than in gills (18.79), liver (4.5) and muscle (36.76) of unexposed samples. Highest bio-

accumulation factor (BAF) was observed in muscle tissue of exposed fish samples for all heavy metals except

Cr, Mn and Fe which were higher in gill tissues (Fig. 1).

Higher concentration of Cr was found in exposed fish samples. The order of Cr concentration in exposed

fish organ was gills (43.6 ± 1.85 mgkg-1), muscle (27.1 ± 1.63 mgkg-1) and liver (13.17 ± 3 mgkg-1). Similar

trend was observed for unexposed samples (gills 39 ± 1.68 mgkg-1; muscle 21.35 ± 0.93 mgkg-1; liver 7.4 ±

1.11 mgkg-1) (Table 1). It has been reported that higher Cr concentration may adversely affect the amino acid

content in fish (Rani and Sivaraj, 2010). Chromium concentration in exposed and unexposed samples was well

above the permissible guidelines of FAO (1 mgkg-1) (Table 1; Hong Kong FAO, 1983).

Highest concentration of Mn in exposed samples was found in gills (16.7 ± 1.35 mgkg-1), followed by

muscle (6.6 ± 1.5 mgkg-1) and least in liver (1.61 ± 0.2 mgkg-1). Also in unexposed samples, gills were highly

accumulated (14.05 ± 1.12 mgkg-1) while liver was least accumulated (1.1 ± 0.06 mgkg-1) with Mn. Similar

trend was followed by Fe in gills (334.4 ± 12.01 mgkg-1), muscle (156.2 ± 6.27 mgkg-1) and liver tissues

(79.10 ± 6.2 mgkg-1) of exposed samples. No permissible guidelines have been provided by FAO for Mn and

Fe concentration in fish tissues (Table 1; FAO, 1983).

Co and Ni accumulation showed similar behaviour in studied samples. Highest concentration of Co was

found in muscle (41.5 ± 2.78 mgkg-1) followed by gills (17.7 ± 1.16 mgkg-1) and least in liver tissues (5.0 ± 0.7

178


IAEES www.iaees.org

mgkg-1) of exposed samples. Co concentration followed similar trend in unexposed samples, but concentration

was well below the exposed samples. Co is regarded as essential nutrient required in trace concentration. It is

indispensible part of vitamin B12 but may prove itself hazardous in higher concentration which may in turn

cause physiological diseases like polycythemia (Gal et al., 2008). Highest concentration of Ni was observed in

muscle tissues of exposed (21.5 ± 2.15 mgkg-1) and unexposed (18.26 ± 0.73 mgkg-1) samples. Almost similar

concentration of Ni was found in liver tissues of exposed (1.05 ± 0.2 mgkg-1) and unexposed fish (0.9 ± 0.13

mgkg-1). Ni concentration in muscle tissues of exposed sample (10 ± 1.76 mgkg-1) was significantly higher

than in unexposed sample (7.8 ± 0.96 mgkg-1).

Table 1 Metal concentration in Clarias batrachus.

Fish Length (cm) Weight (g) HSI% GSI% Reference 24.76 ±1.39 54.81 ± 1.66 0.48 ± 0.01 1.51 ± 0.02 Exposed 22.8 ± 3.4 52.14 ± 2.17 0.48 ± 0.02 1.6 ± 0.02

Concentration of Metals in Fish (Mean ± SD wet weight mgKg-1) Liver Cr Mn Fe Co* Ni* Cu Zn* Cd* Pb

Reference 7.4 ± 1.11

1.1 ± 0.06

71.7 ± 3.42

3.5 ± 0.66

0.9 ± 0.13

3.4 ± 0.38

8.4 ± 0.74

1.3 ± 0.44

11.4 ± 0.93

Exposed 13.17 ±

3 1.61 ±

0.2 79.10 ±

6.2 5.0 ± 0.7

1.05 ± 0.2

4.86 ± 0.6

9.73 ± 1.2

1.89 ± 0.5

13.77 ± 1.3

Gill Cr* Mn Fe Co* Ni Cu Zn Cd Pb*

Reference 38.9 ± 1.68

14.05 ± 1.12

326.97 ± 3.92

15.33 ± 1.17

7.8 ± 0.96

1.62 ± 0.37

47.54 ± 1.45

3.75 ± 1.12

47.41 ± 2.78

Exposed 43.6 ± 1.85

16.7 ± 1.35

334.4 ± 12.01

17.7 ± 1.16

10 ± 1.76

2.6 ± 0.9

53.5 ± 2.26

5.9 ± 1.67

51.4 ± 2.14

Muscle Cr Mn Fe Co Ni Cu Zn* Cd Pb

Reference 21.35 ±

0.93 4.29 ± 0.43

137.26 ± 1.12

36.87 ± 0.83

18.26 ± 0.73

78.41 ± 0.79

79.41 ± 2.31

19.78 ± 0.98

117.46 ± 1.11

Exposed 27.1 ± 1.63

6.6 ± 1.5

156.2 ± 6.27

41.5 ± 2.78

21.5 ± 2.15

90.8 ± 1.83

94.0 ± 2.28

25.9 ± 1.87

122.9 ± 1.84

India (FAO, 1983)

NA NA NA NA NA 10 50 NA 5

Australia (FAO, 1983)

NA NA NA NA NA 10 150 0.2 1.5

Hong Kong (FAO, 1983)

1 NA NA NA NA NA NA 2 6

HSI-Hepatosomatic index; GSI-Gill Somatic Index; FAO- Food and Agriculture Organization; NA-Not Available. * Independent sample t test significance > 0.6

Muscle tissues of the exposed fish samples (90.8 ± 1.83 mgkg-1) were highly accumulated with Cu as

compared to the unexposed samples (78.41 ± 0.79 mgkg-1). Cu concentration in muscle was significantly

higher than that of permissible limits given by FAO (10 mgkg-1) (Table 1; FAO, 1983). Concentration of Cu in

liver in exposed and unexposed samples was found 4.86 ± 0.6 mgkg-1 and 3.4 ± 0.38 mgkg-1 respectively. Least

concentration of Cu was found in gills (Table 1). Higher concentration of Cu in liver indicates Cu ingestion in

the form of diet rather than accumulation from surrounding water. Higher concentration of Cu may lead to

digestive complications in the organism (Clearwater et al., 2002).

Order of Zn accumulation in different organ tissues of exposed and unexposed fish samples was as follows:

highest in muscle (94.0 ± 2.28 mgkg-1 and 79.41 ± 2.31 mgkg-1) followed by gills (53.5 ± 2.26 mgkg-1 and

47.54 ± 1.45 mgkg-1) and least in liver (9.73 ± 1.2 mgkg-1 and 8.4 ± 0.74 mgkg-1). Higher concentration of Zn

179


IAEES www.iaees.org

in gills indicates accumulation of metal from surrounding water. Zn concentration found in fish samples were

well under the permissible guidelines of FAO (Table 1; FAO, 1983).

Fig. 1 Bioaccumulation factor of metals in fish.

Fig. 2 Effective ingestive dose.

Highest concentration of Cd in exposed samples was found in muscle tissues (25.9 ± 1.87 mgkg-1)

followed by gill (5.9 ± 1.67 mgkg-1) and least in liver (1.89 ± 0.5 mgkg-1). Similar trend were found for

unexposed samples. Cd concentration in the present study has significantly crossed the permissible guidelines

of FAO (Table 1; FAO, 1983). Oxygen uptake efficiency is considerably affected by Cd exposure (acute and

chronic). However, it can be counterbalanced by consumption of Zn, Se or ascorbic acid (Sastry and Shukla,

1994). Cd toxicity may cause carcinogenic, mutagenic and teratogenic effects on its consumers (Bellinger et al.,

180


IAEES www.iaees.org

2004). Kumar et al. (2007) suggested the selective removal of hyper-metal accumulated tissue in the fish

before the consumption which may reduce the exposure concentration.

Pb concentration in exposed and unexposed fish samples was found in the order: muscle (122.9 ± 1.84

mgkg-1 and 117.46 ± 1.11 mgkg-1), gills (51.4 ± 2.14 mgkg-1 and 47.41 ± 2.78 mgkg-1) and liver (13.77 ± 1.3

mgkg-1 and 11.4 ± 0.93 mgkg-1) (Table 1). Hyper accumulation of Pb in gills suggested that Pb might have

introduced into the body by osmo-regulation through river water containing higher Pb concentration. However,

human population is most exposed to Pb as fish is the major constituent of human diet and fish muscle is the

chief edible part. Similar results were observed by Gupta et al., 2009. In another study, Pb concentration in

edible muscle of C. batrachus, collected from river Gomti (India), was reported 1.133 ± 0.391 µgg-1 (Agarwal

et al., 2007). Higher Pb concentration may lead to muscle degeneration in fish (Su et al., 2013).

Table 2 Heavy metal concentration in river water (µgl-1).

Site Cr Mn Fe Co Ni Cu Zn Cd Pb S0 (N25012´42.6"

E82057´13.7") 6 59 123 17 2 0 10 2 14

S1 (N25016´26.34" E83000´52.84")

23 73 191 43 5 15 21 6 43

S2 (N25016´51.09" E83000´38.45")

35 116 709 36 8 24 160 9 72

S3 (N25017´13.80" E83000´25.45")

35 70 128 40 13 15 54 4 67

S4 (N25017´27.99" E83000´24.69")

42 89 239 46 16 16 17 7 70

S5 (N25017´40.40" E83000´26.56")

43 83 739 57 21 4 14 7 90

S6 (N25017´51.39" E83000´28.93")

53 65 62 50 15 11 17 11 85

S7 (N25018´11.64" E 83000´27.9")

60 69 980 59 12 27 21 9 90

S8 (N25018´22" E83000´34.78")

86 62 775 59 28 23 24 10 132

S9 (N25018´39.4" E 83000´52")

70 63 759 58 23 30 38 11 113

S10 (N25018´51.99" E8301´01.67")

88 71 899 75 21 19 26 9 146

S11 (N25019´4.19" E 83001´15.6")

92 5 56 72 20 6 22 9 106

S12 (N25019´14.7" E 8301´36.2")

96 62 215 66 11 2 19 14 146

S13 (N25019´23.1" E 8301´51.7")

89 65 151 74 11 9 24 17 128

S14 (N25019´25.35" E83001´57.87")

106 106 146 70 16 17 60 20 185

S15 (N25019´33.6" E 8302´15.9")

118 87 1100 74 29 48 830 19 220

Maximum 118 116 1100 75 29 48 830 20 220 Minimum 6 5 56 17 2 0 10 2 14

Mean 64.78 70.33 468.22 54.89 15.67 17.44 122.06 10.33 107.83SD 32.64 24.19 376.07 16.46 7.67 12.19 201.96 5.07 52.28

SD- Standard Deviation.

181


IAEES www.iaees.org

Humans are ultimately affected by the consumption of metal-accumulated edible fish tissue (Chi et al.,

2007). The adverse effects of metal accumulation in biotic components have been widely discussed. Therefore,

it is necessary to assess the effective ingestive dose (EID) for heavy metals by fish consumption. The EID in

the present study was calculated as described by Alhashemi et al. (2012):

Em = (Cm x CR x Xm)/Bw

where, Em is effective ingestive dose (mg/Kg-d); Cm is concentration of contaminant in edible portion (mgkg-

1); CR is mean daily consumption rate [142.4 g/d (0.1424 kg/d)]; Xm is relative absorption coefficient (in most

instances, it is 1); Bw is mean human body weight (70 Kg).

EID in exposed samples were found higher than those in unexposed samples. Highest EID was observed

for Fe followed by Pb, Zn and Cu while least EID was for Mn (Fig. 2). The EID for Cr, Co, Cd and Pb was

found significantly higher than the permissible dose (Fig. 2; USEPA, 2000; Alhashemi et al., 2012). The

results in the present study revealed that continuous and long term exposure of metals may be adversely

affecting the human population.

4 Conclusion

The aim of the present work was to quantify the metal concentration in the river water and their accumulation

in the vital organ tissues of native fish species C. batrachus. Results revealed that metal concentration in the

river water increases along downstream, mainly due to anthropogenic activities. River water characterization

showed good agreement with metal bioaccumulation study as the fish samples collected from downstream

were hyper-accumulated with metals as compared to unexposed samples. MPI results indicated that highest

accumulation of metals was in fish muscle followed by gills and least in liver. Therefore, fish muscle is the

principal source of metal exposure to human as the studied fish is an important food item of non-vegetarian

diet of population living in river Ganga basin.

Acknowledgements

Authors are thankful to University Grant Commission, New Delhi for providing financial support and Institute

of Environment and Sustainable Development and Centre of Advanced Study in Botany, Banaras Hindu

University, Varanasi, India for providing necessary infrastructure.

References

Agarwal R, Kumar R, Behari JR. 2007. Mercury and lead content in fish species from the river Gomti,

Lucknow, India, as biomarkers of contamination. Bulletin of Environment Contamination and Toxicology,

78: 118-122

Alhashemi ASH, Karbassi AR, Kiabi BH, et al. 2011. Bioaccumulation of trace elements in trophic levels of

wetland plants and waterfowl birds. Biological Trace Element Research, 142: 500-516

Alhashemi AH, Sekhavatjou MS, Kiabi BH, Karbassi AR. 2012. Bioaccumulation of trace elements in water,

sediment, and six fish species from a freshwater wetland, Iran. Microchemical Journal, 104: 1-6

APHA. 2005. Standard Methods for The Examination of Water and Wastewater. APHA, AWWA

Ashwani K, Ashok KG. 2006. Acute toxicity of mercury to the fingerlings of Indian major carps (Catla, Rohu

and Mrigala) in relation to water hardness and temperature. Journal of Environmental Biology, 27: 89-92

Bellinger D, Bolger M, Goyer R, Barraj L, Baines J. 2004. WHO food additives series 46: cadmium—IPCS—

INCHEM. WHO, Switzerland

182


IAEES www.iaees.org

Canli M, Atli G. 2003. The relationships between heavy metal (Cd, Cr, Cu, Fe, Pb, Zn) levels and the size of

six Mediterranean fish species. Environmental Pollution, 121: 129-136

Chabukdhara M, Nema AK. 2012. Assessment of heavy metal contamination in Hindon River sediments: A

chemometric and geochemical approach. Chemosphere, 87: 945-953

Chi QQ, Zhu GW, Langdon A. 2007. Bioaccumulation of heavy metals in fishes from Taihu Lake, China.

Journal of Environmental Science, 19: 1500-1504

Clearwater SJ, Farag AM, Meyer JS. 2002. Bioavailability and toxicity of diet borne copper and zinc to fish.

Comparative Biochemistry Physiology C, 132: 269-313

FAO. 1983. Compilation of Legal Limits for Hazardous Substances in Fish and Fishery Products. Food and

Agriculture Organization of the United Nation, Rome, Italy

Gal J, Hursthouse A, Tatner P, et al. 2008. Cobalt and secondary poisoning in the terrestrial food chain: Data

review and research gaps to support risk assessment. Environmental International, 34: 821-838

Garg VK, Yadav P, Mor S, et al. 2014. Heavy metals bioconcentration from soil to vegetables and assessment

of health risk caused by their ingestion. Biological Trace Elements Research, 157: 256-265

Goodyear KL, McNeill S. 1999. Bioaccumulation of heavy metals by aquatic macro-invertebrates of different

feeding guilds: a review. The Science of the Total Environment, 229: 1-19

Gupta A, Rai DK, Pandey, Sharma B. 2009. Analysis of some heavy metals in the riverine water, sediments

and fish from river Ganges at Allahabad. Environment Monitoring and Assessment, 157: 449-458

Jain CK. 2004. Metal fractionation study on bed sediments of River Yamuna, India. Water Research, 38: 569-

578

Kelepertzis E, Argyraki A, Daftsis E. 2012. Geochemical signature of surface water and stream sediment of a

mineralized drainage basin at NE Chalkidiki, Greece: A pre-mining survey. Journal of Geochemical

Exploration, 114: 70-81

Kumar P, Prasad Y, Patra AK, Swarup D. 2007. Levels of cadmium and lead in tissues of freshwater fish

(Clarias batrachus L.) and chicken in Western UP (India). Bulletin of Environmental Contamination and

Toxicology, 79: 396-400

McCurry J. 2006. Japan Remembers Minamata. World Report, 367: 99-100

Mishra AK, Mohanty B. 2008. Acute toxicity impacts of hexavalent chromium on behavior and histopathology

of gill, kidney and liver of the freshwater fish, Channa punctatus (Bloch). Environmental Toxicology and

Pharmacology, 26: 136-141

Pandey S, Parvez S, Ansari RA, Ali M, et al. 2008. Effects of exposure to multiple trace metals on biochemical,

histological and ultrastructural features of gills of a freshwater fish, Channa punctata Bloch. Chemico-

Biological Interactions, 174: 183-192

Prajapati SK, Meravi N, Singh S. 2012. Phytoremediation of Chromium and Cobalt using Pistia stratiotes: A

sustainable approach. Proceedings of the International Academy of Ecology and Environmental Sciences,

2(2): 136-138

Rani AMJ, Sivaraj A. 2010. Adverse effects of chromium on amino acid levels in freshwater fish Clarias

batrachus (Linn.). Toxicological & Environmental Chemistry, 92(10): 1879-1888

Sastry KV, Shukla V. 1994. Influence of protective agents in the toxicity of cadmium to a freshwater fish

(Channa punctatus). Bulletin of Environment Contamination and Toxicology, 53: 711-717

Su SGL, Ramos GB, Su MLLS. 2013. Bioaccumulation and histopathological alteration of total lead in

selected fishes from Manila Bay, Philippines. Saudi Journal of Biological Sciences,

http://dx.doi.org/10.1016/j.sjbs.2013.03.003

183


IAEES www.iaees.org

Tripathi BD, Sikandar M, Shukla SC. 1991. Physico-chemical characterization of city sewage discharged unto

river Ganga at Varanasi, India. Environment International, 17(5): 469-478

U.S. Environmental Protection Agency. 2000. Guidance for Assessing Chemical Contaminant Data for Use in

Fish Advisories (Third Edition). Risk Assessment and Fish Consumption Limits (Volume 2). Washington

DC, USA

Vaseem H, Banerjee TK. 2013. Contamination of metals in different tissues of rohu (Labeo rohita, Cyprinidae)

collected from the Indian River Ganga. Bulletin of Environment and Contamination Toxicology, 91: 36-41

184


IAEES www.iaees.org

Article

Optimization of phytoremediation in Cd- contaminated soil by using

Taguchi method in Spinacia oleracea

Shirin Jahanbakhshi1, Mohammad Reza Rezaei1, Mohammad Hassan Sayyari-Zahan2

1University of Birjand, Faculty of Agriculture, Department of Environment, Birjand, Iran 2Assistant Prof, University of Birjand, Faculty of Agriculture, Department of Soil Science, Birjand, Iran


Received 10 June 2014; Accepted 15 July 2014; Published online 1 December 2014

Abstract

Phytoremediation is an environmental friendly technique to the cleanup of polluted soils, which combines the

disciplines of plant, soil and microbiology. In this study, four factors including: cow manure, compost, urea

fertilizer and Cd-resistant bacteria with three different levels in soils contaminated with cadmium using 50 mg

kg-1 cadmium chloride (CdCl2.H2O) were used to optimize of phytoremediation by Spinacia oleracea. Taguchi

method has been used for experimental design. Results showed that significant factors in the order of

importance were: cow manure, Cd- resistant batteries, urea fertilizer and compost. The optimum conditions for

the selected levels were inoculate three types of bacteria (CC3, CC4, CC5), compost = 10 (g kg-1), urea

fertilizer = 0.05 (g kg-1) and cow manure = 40 (g kg-1). The performance of` these conditions were estimated at

257.27 (mg kg-1). Cow manure is the most contribution to efficiency of phytoremediation in Spinacia Oleracea.

Keywords optimization; Spinaceae oleracea; fertilizer; resistance bacteria; cadmium.

1 Introduction

1 Introduction

Contamination of heavy metals in soils may affect soil ecology, quality of agricultural products and water

resources, human health problem (Thawornchaisit and Polprasert, 2009; Su et al., 2014). Soil are contaminated

by heavy metals due to different sources such as agricultural activates; sewage sludge, fertilizers and pesticide

(Sayyed and Sayadi 2011; Sayadi and Rezaei 2014). One of heavy metals is cadmium. The amount of

cadmium enter into the soil through utilization of sewage sludge and agricultural activates and also as a result

of industrial activities such as dye making, rubber making, production of fertilizer from phosphate rock,

automobile fuel and metal melting industry (Moteshare-zadeh et al., 2010; Sayadi et al., 2010). Increasing

awareness of the environmental and public health hazard of toxic metals pressurizes society to develop

management strategies to remediate or restore the contaminated area (Thawornchaisit and Polprasert, 2009;

Sayadi and Torabi, 2009). The high cost of existing remediation technologies led to the search for novel



IAEES www.iaees.org

strategies that have the potential to be low cost and impact (Chhotu et al., 2009). Phytoremediation is a

technology that plants and their associated rhizospheric microorganisms are applied to remove, degrade and

detoxify of soil, sediments, water resources and even atmosphere pollution (Ying, 2002). In this study, four

factors include: cow manure, compost, urea fertilizer and Cd-resistant bacteria (after isolation and identity) in

Cd-contaminated soil has been used to optimize of phytoremediation by Spinacia oleracea. Taguchi method

has been used for experimental design. The Taguchi method is a strong design approach to study a large

number of parameters with small number of experiments using fractional factorial design (Khosla et al., 2006).

Soil bacteria play an important role in the recovery of nutrients, development of the soil structure, removal of

toxicity resulting from soil chemical activities, control of plant’s pests and also in stimulation of the plant's

growth (Moteshare-zadeh and Savaghebi-Firoozabadi, 2010). Kuffnet et al. (2008) revealed the effects of

rhizosphere bacteria on absorption and metal concentration in willows in the contaminated soils from lead

mine. pseudomonas, agromyces, streptomyces, flavobacterium, servatia and janthinobacterium were identified.

Among strains, four strains of pseudomonas and serratia and two strains of streptomyces type have the ability

of siderophore production and also three strains of janthino bacterium and serratia have the ability of auxin

production. Tu et al. (2000) reported Fertilizers can affect the soil properties such as pH, CEC and ions in soil.

All these effects would result in the changes in the forms of heavy metals.

The objectives of the study were to:

1. Choice of appropriate experimental design.

2. Determination the optimal settings of the design parameters which would maximize uptake cadmium in

Spinacia oleracea.

3. Improving the performance of phytoremediation process.


This research was done to pot culture at the University of Birjand (Iran). Soil samples were collected from a

depth of 0-30 cm from the field of faculty of agriculture university of Birjand. Samples were air dried and

passed through 4-mesh sieve and mixed uniformly. Physical and chemical properties soil samples were

measured. Results are given in Table 1.

The methods for bacterial isolation and identity of resistant microorganisms to heavy metals have been

reported previous in (Jahanbakhshi et al., 2011). Isolation and purification of native resistant bacteria from soil

samples were done before the performance of pot culture experiments (Table 2). Cd- contaminated soil

samples with concentration of 50 mg kg-1 Cd were simulated. Cadmium solution was prepared from

CdCl2.H2O. Four other parameters including: compost, cow manure, urea and Cd-resistant bacteria in three

levels were also formulated. Characteristic of compost and cow manure are shown in Table 3.

The plant species Spinacia oleracea was used to remove or reduce concentrating cadmium (Jahanbakhshi,

2011). Pots were made of polyethylene material and weighed about 250 g with diameter of 14.2 cm and height

of 13 cm. In each pot 1000g of sieved soil were added. The pots were placed in a laboratory environment with

lighting of 14 hours during the mean day and night temperatures of 25°C and 18°C. Soil moisture was

maintained by weighing the pots and replenishing the water loss daily. Seedlings were fifteen plants in each

pot and grown for about four weeks. Taguchi method has been used for experimental design.

After planting period, shoot samples were harvested and washed with distilled water and placed in the

special packets and dried in oven at 70°C for 48 hours. The dry weight was recorded. The plant sample was

grinded with pestle and mortar. Sample was prepared for Cd determination by wet digestion method where

nitric acid (65%) and percholoric acid (70%) were used to chop off the organic component of the sample

(Ebrahimpour and Mushrifah, 2008). The extracts were determined for Cd using model Shimadzu 6300 AA

186


IAEES www.iaees.org

atomic absorption spectrophotometer (Japan). The optimization of the observed values was determined by

analysis of variance (ANOVA) which was based on the Taguchi method.

Table 1 Characteristic physical and chemical of used soil (sandy-loam).

FC: Field Capacity, OM: Organic matter, EC: Electrical conductivity.

Table 2 Characteristic of activate Cd- Cr resistant bacteries (Jahanbakhshi et al, 2011).

Table 3 Characteristic of compost and cow manure.

Cd Total N

Available P

Available K

Electrical conductivity extractECe

pH Type of component

mg kg-1 % mg kg-1 mg kg-1 dS m-1 -

- 2.1 6300 8100 5.85 7.7 compost

- 3.02 2600 7500 3.45 7.8 cow manure

Taguchi replaces the full factorial experiment with a lean, less expensive, faster, partial factorial

experiment. Taguchi's design for the partial factorial experiment is based on specially developed Orthogonal

Arrays (Christopher and Tpwle, 2001). The Taguchi method having a orthogonal array table to affect the

design process (Gerjan et al., 2000). OA (Orthogonal array) is a matrix of numbers arranged in rows and

columns. Each row represents the level of factors in each run and each column represents a specific level for a

factor that can be changed for each run (Khosla et al., 2006).

Cd

mg kg-1)(

Available K

mg kg-1)(

Available P

(mg kg-1)

Total N

)%(

pH

EC

(dS m-1)

OM

)%(

FC

%)(

Silt

)%(

Sand

)%(

Clay

)%(

0/02 250 10 0/04 7.5 2.21 0/4 15 32 56 12

Color colony

Size colony

Status colony

Gram reacts

Morphology Number bactery

White Point Soft Negative Coco basil CC1

White Point Soft Negative Cocsi CC2



White Point Soft Positive Coco basil CC5

187


IAEES www.iaees.org

3 Results

For our experiments, we considered four parameters. These four main factors for convenience are represented

by the letters A~D (Table 4). The levels of the parameters are listed in Table 5. For these experiments, L9(34)

OA was selected, which represents 9 experiments with four three-level factors. The selected OA is represented

in Table 6.

Table 4 Main factors to optimization of phytoremediation.

Factor Description A Compost B Resistant Bacteria C Urea Fertilizer D Cow Manure

Table 5 Factors and levels.

Factor Level 1 2 3

A 10 (gr kg-1) 20 (gr kg-1) 40 (gr kg-1) B - CC1, CC2 CC3, CC4, CC5 C 0.05 (gr kg-1) 0.1 (gr kg-1) 0.15 (gr kg-1) D 10 (gr kg-1) 20 (gr kg-1) 40 (gr kg-1)

Table 6 Matrix experiments with L9 OA.

Experiment Factors and Levels Response of shoots A B C D Concentration of Cd

( mg kg-1) 1 1 1 1 1 197.96 2 1 2 2 2 173.50 3 1 3 3 3 222.79 4 2 1 2 3 183.98 5 2 2 3 1 169.28 6 2 3 1 2 205.51 7 3 1 3 2 130.26 8 3 2 1 3 222.34 9 3 3 2 1 196.29

The data produced is analyzed for identifying optimum parameters. The response of each experiment is

shown (Table 7). The average response of each factor is calculated at each level. Response table is used for

recording the processed data and presents the calculations of the effects from the orthogonally designed

experiments.

188


IAEES www.iaees.org

Table 7 Average response of each factor at each level (for Concentration of Cd (mg kg-1)).

Factor Level 1 Level 2 Level 3 A 198.08 186.26 182.96 B 170.73 188.37 208.20 C 208.60 184.59 174.11 D 187.84 169.75 209.70

The optimum condition is defined by studying the main effects of each of the factors. The levels of the

factors are expected to produce the best results can be predicted. From the response table or the response graph,

the optimum level of each factor can be predicted as the level that has the highest value of response. Thus, the

optimal configuration for the phytoremediation parameters was identified A (1) B (3) C (1) D (3). The

corresponding parameter values are listed in Table 8.

Table 8 Predicted best strategy parameters.

Factor (Level) Value( mg kg-1) A(1) 594.25 B(3) 624.59 C(1) 625.81 D(3) 629.11

The response graph is the graphical representation for the data presented in the response table to quickly

identify the effects of different parameters (Taguchi et al., 2005). The response graphs corresponding to the

different factors are represented in Fig 1.

Fig. 1 Response graphs.

189


IAEES www.iaees.org

Since the partial experiment is only a sample of the full experiment, the analysis of the partial experiment

must include analysis of the confidence that can be placed in the results. A standard technique called Analysis

of Variance (ANOVA) is used to provide a measure of confidence. (Christopher and Tpwle, 2001). Table 9

lists the ANOVA for each operating parameter of Optimization of phytoremediation in Spinacia oleracea .

Table 9 ANOVA table.

DOF: Degree of freedom; e: error; T: Total of all results.

One of the columns of the ANOVA table is percent contribution, which reflects the portion of the total

variation observed in an experiment to each significant parameter. It signifies that the parameters with

substantial percent contributions are the most important for reducing variation. The contributions of different

of phytoremediation in Spinacia oleracea parameters are also represented in Fig 2. Cow manure parameter D

(3) is the most significant factor and compost the least impact amongst the factors considered for Optimization

of phytoremediation in Spinacia oleracea. The optimum value obtained was 257.27.

Fig. 2 Percent contribution of each parameter.

Factor (DOF)

Sum of Squares

Variance

Variance Ratio

Pure sum of Squares

Percent Contribution

f S V F S' P

A 2 379.33 189.66 37932 379.32 5.6

B 2 217.63 1053.81 210762 2107.62 31.15

C 2 1876.25 938.12 187624 1876.24 27.75

D 2 2400.72 1200.36 240072 2400.71 35.5

e 18 .09 .005 1 .13 .001

T 26 6764.02 ….. …... ….. 100

190


IAEES www.iaees.org

4 Discussion

A number of factors affect Cd bioavailability in soil, including soil pH, organic matter presence of other ions,

root exudates, types and cultivars of crop plants, and plant age. These factors influence the solubility of Cd

compounds and the release of Cd into the soil solution or affect the ability of plants to take up Cd from soil.

Some have positive effects while others have negative effects on Cd bioavailability (Sarwar et al., 2010).

Results of the analysis showed that the intended soil have suitable physical and chemical properties for

greenhouse culture.

The present study found that Application cow manure led to cadmium concentration increase in shoot of

Spinacia oleracea. There are several possible explanations for this result. Gregson and Alloway (1984)

reported, application of cow manure, the soil pH was increased slightly which may have caused the formation

of soluble Cd-organic complexes which can increase metal solubility. In 1994, Ramos et al. reported Cd may

form weak complexes with OM and can be easily removed. According to the report of Lamas and Singh

(2001), the provision of soluble organic compounds complexes causes Cd solubility. High concentrations of

dissolved organic compounds increase metal uptake to root surface (McBride, 1995). Finally, the high biomass

productivity in the plants grown in cow manure soil possibly resulted the increase of Cd accumulation in shoot

and total Cd uptake (Lamas and Singh, 2001). Narwal and Singh showed significant increase in Cd

concentration in wheat when pig manure was the OM source.

With regard to results provided in fig2 Application of inoculants (CC3, CC4 and CC5 have the positive

effects of absorbing and translocating Cd to the shoots of Spinacia oleracea. Thereby application of these

inoculants, better results will be achieved in phytoextraction. According to Yan- de et al. (2007), multiple

metal resistance (MMR) have more effects in bacteria than the resistance to one metal, thereby it is possible

that in treatments with application of these inoculants, better results will be achieved in phytoremedation.

Native and resistant bacteria, by their effective growth promoting abilities, especially providing necessary

iron for the plants by production of microbial siderophores, influencing phytopathogens, could bring out the

drought and salinity resistance and also producing indole acetic acid (IAA), and stimulating root, caused

enhancement in translocation of metal from soil to the plant and as a result increased the efficiency of

phytoremediation (Moteshare-zadeh and Savaghebi-Firoozabadi, 2010). Growth of the plant's roots could

increase as a result of production of auxin (IAA) by plant growth promoting rhizobacteria. Also, existence of

ACC deaminase enzyme with decreasing of stress ethylene stimulates the plant's growth, so assists in less

toxicity of heavy metals in plant (Yan-de et al., 2007).

In this research, Application urea fertilizers led to cadmium concentration increase in shoot of Spinacia

oleracea. Application of some fertilizers, such as NH4+ fertilizers including urea, ammonium sulfate and

monoammonium phosphate (MAP), can enhance Cd availability by lowering pH. Rhizosphere acidification

occurs as a result of NH4+ nutrition due to the release of protons (H+) by root cells or nitrification of NH4+, and

this induced acidification can promote mobilization of a localized metal in neutral to alkaline soil

contaminated with a particular heavy metal like Cd. The type of N fertilizers applied will determine whether

there would be a decrease or an increase in Cd uptake with its application. Compared to NO3− fertilizers,

NH4+containing fertilizers could result in enhanced Cd uptake due to decrease in soil pH (Sarwar et al., 2010).

The addition of organic matter to soil, especially in the form of compost, results in increased mineralization of

urea and also micronutrients (Dick and McCoy, 1993). The compost treatment causes the development of

microbial populations and influence the metal distribution, during organic matter mineralization or the metal

solubilization by decreasing of pH, metal adsorption by the microbial biomass\ metal complexion with the

freshly formed humic substances (Hsu and Lo, 2001; Zorpas et al., 2003)

191


IAEES www.iaees.org

Utilization of compost in soil improves physical properties which can helps to phytoremediation

(Zheljazkov and Warman, 2004) due to increased biomass by growing plants, suggesting that more metal can

be taken up from the contaminated soils (Tang et al., 2003).

5 Conclusions

The optimal configuration for the phytoremediation parameters was identified A (1) B (3) C (1) D (3). Cow

manure factor is the most significant factor and compost the least impact amongst the factors considered for

Optimization of phytoremediation in Spinacia oleracea.

References

Chhotu D, Jadia Fulekar MH. 2009. Phytoremediation of heavy metals: Recent techniques. African Journal of

Biotechnology, 8(6): 921-928

Christopher M, Towle BSEE. 2001. Exposure of soft defects in integrated circuits using taguchi. MSc thesis.

Texas Tech University, USA

Dick WA, McCoy EL. 1993. Science and engineering of composting: Design, environmental, microbiological

and utilization aspects. Ohio Agricultural Research and Development Center. The Ohio State University,

USA

Gerjan D, Vitronics S, Oosterhout NL. 2000. All roads lead to apex. Session P-SM4 / 1- 1. Analyzing Lead-

Free Soldering Defects in Wave Soldering Using Taguchi Methods Presented at Apex 2000 March 14-16.

IPC, California, USA

Gregson SK, Alloway BJ. 1984. Gel permeation chromatography studies on the speciation of lead in solutions

of heavily polluted soils. Journal of Soil Science, 35: 55-61

Hsu JH, Lo SL. 2001. Effect of composting on characterization and leaching of copper, manganese, and zinc

from swine manure. Environmental Pollution, 114: 119-127

Jahanbakhshi, Sh 2011. Evaluation of phytoremediation of heavy metal in contaminated soil by Spinacia

oleracea and Lepidium sativum. MSc thesis. Birjand University, India

Jahanbakhshi Sh, Rezaei MR, Sayyari-Zahan MH. 2011. Isolation and identity of resistant microorganisms to

heavy metals in order to improvement of process Phytoremediation. First Congress Technologies of

Monitoring In Environment, Tehran, Iran

Khosla A, Kumar S, Aggarwalk K. 2006. Identification of strategy parameters for particle swarm optimizer

through Taguchi method. Journal of Zhejiang University Science, 7(12): 1989-1994

Kuffner M, Puschenreiter M, Wieshammer G, Gorfer M, Sessitsch A. 2008. Rhizosphere bacteria affect

growth and metal uptake of heavy metal accumulating willows. Plant Soil, 304: 35-44

McBride MB. 1995. Toxic metal accumulation from agricultural use of sludge: Are the USEPA regulations

protective? Journal of Environmental Quality, 24: 5-18

Moteshare-zadeh B, Savaghebi-Firoozabadi GhR, Mirseyed Hosseini H, Alikhani HA. 2010. Study of the

Enhanced Phytoextraction of Cadmium in a Calcareous Soil. International Journal Environmental Research,

4(3): 525-532

Moteshare-zadeh B, Savaghebi-Firoozabadi GhR. 2010. Bioaccumulation and phyto translocation of Nickel by

Medicago sativa in a calcareous soil of Iran. Desert, 15: 61-69

Narwal RP, Singh BR. 1998. Effect of organic materials on partitioning, extractability and plant uptake of

metals in an alum shale soil. Water Air Soil Pollution, 103: 405-421

192


IAEES www.iaees.org

Ramos L, Hernandez LM, Gonzalez MJ. 1994. Sequential fractionation of copper, lead, cadmium and zinc in

soils from or near Do˜nana National Park. Journal of Environmental Quality, 23: 50-57

Sarwar N, Saifullah Malhi SS, Zia MH, et al. 2010. Role of mineral nutrition in minimizing cadmium

accumulation by plants. Journal of The Science of Food and Agriculture, 90: 925-937

Sayadi MH, Rezaei MR. 2014. Impact of land use on the distribution of toxic metals in surface soils in Birjand

city, Iran. Proceedings of the International Academy of Ecology and Environmental Sciences, 4(1): 18-29

Sayadi MH, Sayyed MRG, Kumar S. 2010. Short-term accumulative signatures of heavy metals in river bed

sediments in the industrial area, Tehran, Iran. Environmental Monitoring and Assessment, 162(1-4): 465-

473

Sayadi MH, Torabi S. 2009. Geochemistry of soil and human health: A review. Pollution Research, 28(2):

257-262

Sayyed MRG, Sayadi MH. 2011. Variations in the heavy metal accumulations within the surface soils from the

Chitgar industrial area of Tehran. Proceedings of the International Academy of Ecology and Environmental

Sciences, 1(1): 36-46

Su C, Zhang WJ, Jiang LQ. 2014. A review on heavy metal contamination in the soil worldwide: Situation,

impact and remediation techniques. Environmental Skeptics and Critics, 3(2): 24-38

Taguchi G, Chowdhury S, Wu Y. 2005. Taguchi Quality Engineering Handbook. John Wiley and Sons, USA

Tang S, Xi L, Zheng J, Li H. 2003. Response to elevated CO2 of Indian Mustard and Sunflower growing on

copper contaminated soil. Bulletin of Environmental Contamination and Toxicology, 71: 988-997

Thawornchaisit U, Polprasert C. 2009. Evaluation of phosphate fertilizers for the stabilization of cadmium in

highly contaminated soils. Journal of Hazardous Materials, 165: 1109-1113

Tu C, Zheng CR, Chen HM. 2000. Efect of applying chemical fertilizers on forms of lead and cadmium in red

soil. Chemosphere, 41: 133-138

Yan-de J, Zhen-li H, Xiao Y. 2007. Role of soil rhizobacteria in phytoremediation of heavy metal

contaminated soils. Journal of Zhejiang University Science, 8(3): 197-207

Ying O. 2002. Phytoremediation: modeling plant uptake and contaminant transport in the soil–plant–

atmosphere continuum. Journal of Hydrology, 266: 66-82

Zheljazkov VD, Warman PR. 2004. Application of high Cu compost to Dill and Peppermint. Journal of

Agricultural and Food Chemistry, 52:2615-2622

Zorpas AA, Arapoglou D, Panagiotis K. 2003. Waste paper and clinoptilolite as a bulking material with

dewatered an aerobically stabilized primary sewage sludge (DASPSS) for compost production. Waste

Management, 23: 27-35

193

Proceedings of the International Academy of Ecology and

Environmental Sciences

The Proceedings of the International Academy of Ecology and Environmental Sciences (ISSN 2220-8860) is an open access, peer-reviewed online journal that considers scientific articles in all different areas of ecology and environmental sciences. It is the flagship journal of the International Academy of Ecology and Environmental Sciences. It dedicates to the latest advances in ecology and environmental sciences. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. All manuscripts submitted to Proceedings of the International Academy of Ecology and Environmental Sciences must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal. The topics to be covered by this journal include, but are not limited to theory, methodology, technology, innovation, activity, and project in the following areas:

Animal ecology, plant/microbe ecology, wetland ecology, farmland ecology, forest/grassland ecology, marine ecology, pollution ecology, etc.

Biological conservation & preservation, ecosystem restoration, environmental policy, environmental toxicology, environmental pollution and control, natural resource, bioenergy research, environmental technology, waste management, environmental economics, environmental management & planning, environmental education, environmental engineering, global climate change, oceanography, etc.

Authors can submit their works to the email box of this journal, [email protected].

In addition to free submissions from authors around the world, special issues are also accepted.

The organizer of a special issue can collect submissions (yielded from a research project, a

research group, etc.) on a specific topic, or submissions of a conference for publication of special

issue.

Editorial Office: [email protected]

Publisher: International Academy of Ecology and Environmental Sciences

Address: Flat C, 23/F, Lucky Plaza, 315-321 Lockhart Road, Wanchai, Hong Kong

Tel: 00852-6555 7188

Fax: 00852-3177 9906


Proceedings of the International Academy of Ecology and Environmental Sciences ISSN 2220-8860 ∣ CODEN PIAEBW Volume 4, Number 4, 1 December 2014 Articles

Assessment of aerosol-cloud-rainfall interactions in Northern Thailand V. Tuankrua, Piyapong Tongdeenog, Nipon Tangtham, et al. 134-147

Detected foraging strategies and consequent conservation policies of the Lesser Kestrel Falco naumanni in Southern Italy Marco Gustin, Alessandro Ferrarini , Giuseppe Giglio, et al. 148-161

Dynamics of 35 trace elements throughout plant organs in the subalpine broad leaf evergreen shrub Rhododendron ferrugineum Charles Marty, André Pornon, Thierry Lamaze, Jérome Viers 162-175

Assessment of metal bioaccumulation in Clarias batrachus and exposure evaluation in human

Mayank Pandey, Ashutosh Kumar Pandey, Ashutosh Mishra, B. D. Tripathi 176-184

Optimization of phytoremediation in Cd- contaminated soil by using Taguchi method in Spinacia oleracea Shirin Jahanbakhshi, Mohammad Reza Rezaei, Mohammad Hassan Sayyari-Zahan 185-193

IAEES http://www.iaees.org/

Proceedings of the International Academy of Ecology and Environmental Sciences, 2014, Vol. 4, Iss. 4

Documents

Transcript of Proceedings of the International Academy of Ecology and Environmental Sciences, 2014, Vol. 4, Iss. 4