Environmental Monitoring of Spatial and Temporal distribution pattern of Airborne Pollen grains in some selected Localities within Akoko division ...
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International Journal of Innovative Biosciences Research 9(1):1-20, Jan.-Mar., 2021
© SEAHI PUBLICATIONS, 2021 www.seahipaj.org ISSN: 2467-8457
Environmental Monitoring of Spatial and Temporal
distribution pattern of Airborne Pollen grains in some
selected Localities within Akoko division, Ondo State,
Nigeria
Prof. Ige, Olugbenga Ebenezer1 & Dr. Essien, Benjamin Christopher2
Department of Plant Science and Biotechnology,
Faculty of Science,
Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria
Author’s Email: olugbengaige@yahoo.com1; benjamin.essien@aaua.edu.ng2; benjaminessien8@gmail.com2
ABSTRACT
The study monitoring spatial and temporal distribution pattern of airborne pollen grains in some
selected localities within Akoko division, Ondo State, Nigeria gives information on diurnal,
seasonal and annual variations in the pollen types and their concentration in the atmosphere. The
airborne pollen grains were collected with Modified Tauber Sampler using simple random
sampling technique and analyzed palynologically. Results showed 50,661 pollen grains
comprising of 182 pollen types belonging to 74 plant families were encountered. Three (3) of
these pollen types were identified to family level, 57 to species level, 121 to generic level and
one (1) unidentified. The predominant pollen types belong to the family Poaceae, Arecaceae,
Euphorbiaceae, Asteraceae and Casuarinaceae. Airborne pollen grains were most abundant
between September and January. Rainfall and relative humidity had more negative effect on
pollen concentration. The effects of temperature and light intensity varied considerably with
time and location because their effects are influenced by other meteorological factors. The
period of pollen abundance, variability and distribution in the atmosphere revealed that the
highest mean concentration occurred during the late rainy season to early dry season, followed
by the period of dry season to early rainy season and the least occurred during the mid-rainy
season. Study has provided baseline information especially to persons suffering from pollen
grains allergies such as hay fever (pollinosis).
Keywords: Akoko division; Airborne-pollen; Distribution; Spatial; Temporal, Nigeria
1. INTRODUCTION
Palynological studies are important tools for interpreting the environment (past and present) and
elucidating questions related to palaeoclimates and palaeoecology (Barreto et al., 2012).
Research has shown that pollen grains are a major contributor to pollinosis symptoms (Ige and
Essien, 2019; Subiza et al., 1994). Despite the abundance of airborne pollen and its proven
implications in pollinosis, few studies have actually addressed the implications of this airborne
pollen content especially its seasonal production, abundance, distribution pattern and period of
prevalence in the atmosphere. However, it is necessary to understand the present environment
and the dynamics that controls the seasonal production, dispersal, distribution, deposition and
sedimentation of pollen grains before one attempts to interprete the pollen records.
Pollen grains are generally produced in excess of the biological needs of the plants. Established
observations have shown that anemophilous plants release large quantities of pollen into the air,
while entomophilous taxa release smaller amounts (Essien et al., 2016; Agwu, 1997). The
behavioural patterns lead to differences in the quantity of pollen and spores of various taxa
available in the atmosphere, and which of them can be trapped during sampling (Agwu, 2001;
Moore and Webb, 1978).
Studies monitoring the pollen content of air and deposition of airborne pollen grains have also
given enough insight. Early results of diverse trapping experiments and surface sample studies
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are widely reported (Giesecke et al., 2010). The comprehensive model of pollen deposition and
the pollen bucket studies by H. Tauber encouraged researchers to conduct similar experiments
using the same type of pollen trap, which became the main trapping device for Quaternary
palynologists (Tauber, 1974). The high precipitation in the tropics inspired the development of
alternative design.
The release, distribution and sedimentation of these airborne pollen grains are influenced by a
number of factors such as the type and floristic composition of the vegetation which determine
the abundance and types of pollen that are released into the atmosphere (Essien et al., 2016;
Agwu, 2001; Gregory, 1978). Also, the flowering pattern of individual plants play important role
in determining the abundance and taxa of pollen circulating in the atmosphere (Essien, 2020).
Airborne pollen grains generally travel short distances; however, when they are blown into the
upper strata of the atmosphere, pollen grains travel long distances, before they are deposited
(Essien, 2020; Essien, 2014). Meteorological factors, in particular temperature, precipitation,
humidity and wind speed strongly influence airborne pollen counts (Essien and Agwu, 2013;
Agwu and Osibe, 1992). Pollen dispersal is facilitated by dry weather and high wind velocity.
Pollen count is reduced after precipitation. The widely dispersed pollen and spores provide a
broad picture of the surrounding vegetation of the areas in which they are produced. Even
though sometimes, some of them are blown into these areas from distant places, experienced
palynologists are most of the time able to detect these re-deposited pollen and spores by
differences in preservation (e.g. colour, corrosion, wear), ecological or stratigraphical
inconsistencies and other associated evidences of being re-deposited (Melia, 1984).
Several studies presented evidence that local vegetation was a driver of spatial variation in
pollen distribution pattern. For example, Gonzalo-Garjo et al. (2006) used information from tree
census in his study area and found out that the site with highest Cycamore pollen concentration
was closer to major stands of Cycamore tree than two sites with lower concentrations, although
the exact distance and densities of the stands were not reported. Katelaris et al. (2004) conducted
vegetation surveys in 2,000 m circular buffers of their three sampling sites and found differences
in local vegetation that parallel differences in total pollen concentrations. In the study by Nowak
et al. (2012), one site was located 100 m from the closest trees while the other was 6.5 km from
the closest Cycamore trees. Annual Cycamore totals were 10 to 20 times lower at the site that
was farthest from the Cycamore trees, suggesting that most Cycamore pollen does not travel far
from its source and that the local sources are important drivers of local pollen concentrations.
Some factors that aid in pollen dispersal are the size, shape and density of the pollen, the
position of the parent plants (that produce them) in the vegetation and the prevailing climatic
conditions (Patrick et al., 2005). The distinctive surface sculptures of pollen grains aid in the
identification of their parent plants to at least family level (Njokuocha and Ukeje, 2006). Their
other qualities such as small size, abundance and high resistance to degradation have made them
an invaluable evidence of vegetation as well as environmental changes (Davies, 1969).
Aerobiological samples taken at a particular time in a given place provide an estimate of the
atmospheric pollen levels. Since turbulence phenomena cause temporal and spatial variations in
the concentrations, a more precise estimate is given by continuous sampling. This information
can be obtained using pollen trap at different points, although temporal variations will not be
recorded unless the sampling is carried out continuously. The degree to which a sampling site(s)
is located in a more central or pheripheral position will significantly influence not only the
airborne pollen grains distribution pattern but also the spectrum of pollen types trapped over the
course of the year.
Because of the transitional location of Akoko division, Ondo State, Nigeria between the tropical
rainforest in the South and the true savanna in the North; the area receives particles moving from
the South to the Northern part of Nigeria and vice versa. Equally, the movements of the South-
West Monsoon and North-East Trade winds across the latitudinal zones of Nigeria carrying
along with them large quantities of pollen grains which are deposited along their routes
depending on the strength of the winds during the major seasons of the year. The aim of this
study is to ascertain the pattern of spatial and temporal variations in the distribution of the
prevalent pollen types.
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2. MATERIALS AND METHODS
Forty locations were randomly selected within the four Local Government Areas of Akoko
division, Ondo State, Nigeria as sampling sites. The sampling sites for the study was purposely
selected to reflect (represent) as far as possible the Local Government Area of the study. In
choosing the sites, consideration was also given to urbanization, accessibility, and safety of the
sampling (experimental materials) instruments among others (Patton, 1990).
At each site, a pollen trap (Modified Tauber Sampler) was mounted according to the methods of
Tauber (1974; 1977), Pardoe et al. (2010) and Giesecke et al. (2010). Prior to this, a mixture of
glycerol (65 ml), formalin (30 ml) and phenol (5 ml) was poured into each of the trap. The
positions of the traps at various locations were recorded using a Global Position System (GPS).
The solutions in the trap prevented the palynomorphs from drying up, kill insects and also
prevented the decay of dead organisms. The trap was left to stand throughout the duration of the
study period. Fortnightly of each month, solution collection was done. The traps were washed
with water to remove any contaminants and were then recharged with the above mentioned
chemical solution. This procedure was repeated bi-monthly from October 2016 to December
2017 for one year. The palynomorphs were recovered through centrifugation at 2000 r.p.m
(revolution per minute) for 5 minutes and supernatant decanted each time. The precipitates were
washed twice with distilled water and recovered through centrifugation. The sediments were
treated with glacial acetic acid to remove water before acetolysis Erdtman (1969), Agwu and
Akanbi (1985). The recovered precipitates were washed with glacial acetic acid, and finally
washed twice with distilled water, centrifuged each time and decanted. The recovered
palynomorphs were stored in a plastic vials in glycerin and ethanol solution (2:1).
The palynomorphs were analysed palynologically and microscopically with Olympus
microscope at x400 magnification for counting and Leica microscope at x1000 magnification for
detailed morphological studies. Palynomorphs identification, counting and classification were
done with the help of reference descriptions and photomicrographs from Agwu and Akanbi
(1985), Bonnefille and Riollet (1980), Sowunmi (1995) and Shubharani et al. (2013). In
addition, prepared slides of pollen samples in the Palynological Research Unit; Department of
Plant Science and Biotechnology, Adekunle Ajasin University, Akungba- Akoko, Nigeria were
used.
3. RESULTS AND DISCUSSION
A total of 182 pollen types were identified across the study locations. These pollen types belong
to 74 plant families, 1 gymnosperm and 4 pteridophytes (Table 1). Out of these 74 plant families
encountered, 66 families belong to the dicotyledonous plants, 6 to monocotyledonous plants, 1
to the gymnospermous plants and 1 was unidentified (Indeterminata). At the study locations, the
highest pollen counts were recorded for Ayegunle followed by Ipesi, Ifira, Ipe and Akungba
while the least were recorded for Ogbagi and Oke- Agbe (Table 1).
However, the cumulative monthly pollen record showed that a total of 50661 pollen grains were
recorded with the highest monthly pollen counts of 9,023 recorded in November, followed by
December (8,750), October (5,789), September (5,600) and the least recorded in August (1,926)
and July (1,530) respectively (Table 1).
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Table 1: Spatial distribution of pollen types across study locations
S/N Study Total No. of Total No. Total No. Total No. Total No. Total No. Total No. of
Locations pollen types of pollen of Plant classified classified classified Plant family
(L1-L20) counted counted Families to Family to Generic to Species Unidentified
identified level level level
1 Ifira 125 3404 59 3 81 40 1
2 Ipe 110 3389 55 3 80 27 0
3 Ipesi 108 3710 53 3 76 28 1
4 Isua 91 2895 54 3 63 24 1
5 Sosan 80 2180 45 3 58 18 1
6 Akunnu 106 2664 58 3 70 32 1
7 Auga 110 2338 54 3 76 30 1
8 Iboropa 96 2587 53 3 66 26 1
9 Ikare 86 2130 51 3 61 21 1
10 Ise 75 1923 43 3 51 20 1
11 Akungba 78 2955 50 3 51 23 1
12 Ayegunle 89 4665 50 3 61 24 1
13 Oba 81 2187 47 3 56 21 1
14 Oke-Oka 81 2008 43 2 57 21 1
15 Supare 73 1955 43 2 49 21 1
16 Arigidi 71 1821 42 3 53 15 0
17 Ogbagi 66 1790 38 3 44 18 1
18 Oke-Agbe 64 1763 42 3 46 14 1
19 Ikaram 67 2111 42 3 51 12 1
20 Irun 78 2186 43 3 56 19 0
TOTAL 1735 50661 965 58 1206 454 17
The monthly airborne pollen concentrations showed three discernible periods of pollen
variability in the atmosphere: February - June (dry season to early rainy season); July - August
(mid rainy season) and September – January (late rainy season to early dry season). The three
periods of pollen abundance, variability and distribution in the atmosphere revealed that the
highest mean concentration occurred during the late rainy season to early dry season, followed
by the period of dry season to early rainy season and the least occurred during the mid-rainy
season (Fig. 1).
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Figure 1: Variation in cumulative monthly pollen counts recorded during the study period
Figure 2: Variation in cumulative pollen counts recorded across the study locations
LEGEND: L1=Ifira, L2=Ipe, L3=Ipesi, L4=Isua, L5=Sosan, L6=Akunnu, L7=Auga, L8=Iboropa,
L9=Ikare, 10=Ise, L11=Akungba, L12=Ayegunle, L13=Oba, L14=Oke-Oka, L15=Supare, L16=Arigidi,
L17=Ogbagi, L18=Oke-Agbe, L19=Ikaram, L20=Irun.
There were noticeable monthly increase and decrease in the quantity of pollen types
documented. The pollen load of the entire study area varied quantitatively and qualitatively not
only from month-to-month but also from location-to-location (Fig. 2). Among the classified
plant families, highest pollen counts was recorded for Poaceae followed by Arecaceae,
Casuarinaceae and Asteraceae whereas the least count was recorded for Boraginaceae as well as
Dichapetalaceae and Leeaceae (Table 2).
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Table 2: Spatial distribution of pollen types across family level for the study period (October 2016- September 2017)
S/N Plant Family OCT. NOV. DEC. JAN. FEB. MAR APR. MAY JUN. JUL. AUG. SEP. TOTAL
1 Acanthaceae 4 13 0 3 0 4 1 8 6 2 2 5 48
2 Amaranthaceae/ 26 28 24 16 11 158 35 53 26 9 3 26 415
Chenopodiaceae
3 Anacardiceae 1 129 236 4 0 92 192 87 2 35 51 197 1026
4 Annonaceae 1 0 2 3 0 3 15 0 0 8 3 0 35
5 Apocynaceae 3 283 0 224 26 20 8 8 2 2 0 7 583
6 Arecaceae 350 1411 1153 400 258 639 600 330 148 123 187 379 5978
7 Asteraceae 678 955 66 155 38 15 41 80 583 85 208 152 3056
8 Bignoniaceae 3 41 41 25 0 41 0 13 8 11 0 8 191
9 Bombacaceae 25 14 6 5 4 2 19 18 13 0 3 17 126
10 Boraginaceae 0 0 0 0 0 0 0 3 0 0 0 0 3
11 Burseraceae 1 12 10 0 0 7 22 38 7 0 0 1 98
12 Caesalpinoideae 117 143 133 91 76 52 91 49 19 30 92 247 1140
13 Cannabaceae 0 0 0 15 0 7 3 1 0 0 2 1 29
14 Capparaceae 1 0 4 0 0 0 9 1 0 0 0 0 15
15 Caprifoliaceae 3 5 2 2 0 0 0 0 0 5 2 0 19
16 Caricaceae 0 0 0 0 0 0 0 0 0 0 11 31 42
17 Casuarinaceae 719 14 23 27 17 61 31 110 34 33 90 33 1192
18 Celastraceae 0 0 0 4 9 0 0 0 0 0 0 0 13
19 Chrysobalanaceae 0 0 0 0 0 31 5 11 9 0 0 0 56
20 Clusiaceae 4 2 5 0 0 8 15 5 25 29 0 2 95
21 Cochlospermaceae 0 0 0 0 0 5 0 0 30 1 0 0 36
22 Colchicaceae 0 0 0 0 0 5 7 4 2 0 3 0 21
23 Combretaceae 11 42 30 24 47 40 31 42 42 12 20 35 376
24 Commelinaceae 0 0 0 0 0 3 7 0 0 0 0 0 10
25 Connaraceae 0 0 0 0 0 4 1 11 0 0 0 0 16
26 Convolvulaceae 0 6 0 0 6 7 0 0 12 0 0 4 35
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27 Costaceae 0 2 0 1 0 0 0 3 2 5 0 0 13
28 Cucurbitaceae 0 0 0 0 0 4 12 3 0 0 0 5 24
29 Cyperaceae 19 13 12 10 9 17 10 116 42 25 22 26 321
30 Dichapetalaceae 0 0 0 0 0 0 0 2 3 0 0 0 5
31 Dilleniaceae 0 3 0 0 0 1 2 4 0 0 0 0 10
32 Dioscoreaceae 0 0 0 0 0 8 3 1 0 0 0 2 14
33 Diptilomiodeae 0 0 0 0 0 6 9 0 0 0 0 0 15
34 Ebanaceae 0 0 0 0 0 0 3 6 18 88 237 0 352
35 Euphorbiaceae 25 518 851 200 104 229 140 148 67 9 16 68 2375
36 Fagaceae 0 4 1 7 0 0 0 0 0 0 0 0 12
37 Gentianaceae 0 1 2 3 0 0 0 0 2 2 0 0 10
38 Hypericaceae 35 0 0 2 8 5 21 20 25 27 31 0 174
39 Irvingiaceae - 7 4 123 15 202 29 31 2 3 3 7 426
40 Lamiaceae 113 56 91 28 31 27 9 12 35 59 34 267 762
41 Leeaceae 0 3 0 0 0 0 0 0 2 0 0 0 5
42 Linaceae 12 0 0 3 5 8 6 12 12 23 8 0 89
43 Loganiaceae 5 0 18 3 3 2 2 5 2 1 0 2 43
44 Malvaceae 35 311 60 49 164 8 9 3 19 1 34 30 723
45 Melastomataceae 41 0 0 0 0 0 0 0 11 26 21 13 112
46 Meliaceae 4 22 21 10 7 31 76 10 8 13 17 7 226
47 Mimosoideae 91 342 390 458 372 177 182 277 79 63 29 94 2554
48 Moraceae 0 50 143 161 0 277 17 12 5 0 0 19 684
49 Myristicaceae 0 61 21 32 0 0 0 0 0 0 0 10 124
50 Myrtaceae 80 72 3 9 5 49 49 253 28 17 60 30 655
51 Ochnaceae 4 9 41 6 10 15 14 14 2 0 4 4 123
52 Olacaceae 2 0 5 7 7 2 0 0 0 0 2 4 29
53 Pandaceae 1 5 13 11 0 36 21 10 0 0 1 2 100
54 Papilionoideae 11 15 16 0 3 29 19 12 13 0 0 6 124
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55 Passifloraceae 0 0 0 0 0 0 1 4 2 0 0 0 7
56 Pedaliaceae 0 72 15 15 0 0 0 0 0 0 0 125 227
57 Phyllanthaceae 44 61 435 122 41 60 69 247 59 28 28 36 1230
58 Pinaceae 9 0 7 4 0 1 4 4 1 2 5 2 39
59 Plumbaginaceae 11 0 0 0 0 0 0 0 0 0 3 7 21
60 Poaceae 3109 3892 4145 1679 594 551 567 889 699 703 565 3418 20811
61 Polygalaceae 5 21 15 18 0 0 0 0 0 0 8 10 77
62 Portulacaceae 18 12 8 0 0 0 0 0 0 0 5 5 48
63 Proteaceae 1 1 0 3 3 0 0 0 0 0 1 0 9
64 Rhamnaceae 0 1 2 1 0 9 7 0 0 0 0 0 20
65 Rosaceae 7 11 3 8 0 48 22 159 12 4 12 14 300
66 Rubiaceae 73 171 162 193 5 269 37 106 21 13 48 45 1143
67 Rutaceae 4 9 41 6 10 15 14 14 2 0 4 4 123
68 Sapindaceae 2 0 5 7 7 2 16 47 24 0 2 4 116
69 Sapotaceae 1 11 5 7 0 27 18 10 0 0 1 2 82
70 Solanaceae 28 4 406 4 2 10 12 4 33 6 5 16 530
71 Sterculiaceae 16 129 44 133 39 50 57 243 26 22 23 145 927
72 Urticaceae 26 25 20 18 0 0 0 0 0 0 14 19 122
73 Verbenaceae 9 9 3 3 3 0 0 0 0 0 3 3 33
74 Indeterminata 1 2 7 1 6 1 4 4 1 5 3 4 39
TOTAL 5789 9023 8750 4343 1945 3380 2594 3557 2225 1530 1926 5600 50661
8
Ige & Essien ….. Int. J. Innovative Biosciences Res 9(1):1-20, 2021 Statistical analysis of the mean monthly pollen counts showed that there was a significant difference (P
Ige & Essien ….. Int. J. Innovative Biosciences Res 9(1):1-20, 2021 Table 4: Mean pollen grains counts recorded at the study locations Study Locations Mean Pollen grains±S.E Ayegunle 2.07±0.45d Ipesi 1.64±0.25cd Ipe 1.54±0.27bcd Ifira 1.51±0.21abcd Akungba 1.31±0.25abc Isua 1.28±0.20abc Akunnu 1.19±0.18abc Iboropa 1.14±0.19abc Auga 1.05±0.17abc Oba 0.97±0.19abc Irun 0.97±0.19abc Sosan 0.97±0.17abc Ikare 0.94±0.18abc Ikaram 0.94±0.17abc Oke-Oka 0.89±0.18ab Ise 0.86±0.17ab Supare 0.86±0.16ab Arigidi 0.80±0.15a Ogbagi 0.79±0.18 a Oke-Agbe 0.78±0.15a p Value 0.000* Means not followed by the same letter are significantly different at P
Ige & Essien ….. Int. J. Innovative Biosciences Res 9(1):1-20, 2021
The study also showed that the concentration of pollen grains in the atmosphere fluctuates
considerably with the seasons. With regards to the identified pant families, Poaceae which is
among the predominanat plant communities in the study area produces large quantities of pollen
grains which are released into the atmosphere. The variation in cumulative monthly pollen counts
of Poaceae showed that the frequency increased greatly from September, reached its peak in
November and thereafter declined progressively to a relatively low level from February. The
pollen abundance of Amaranthaceae/ Chenopodiaceae increased sharply to its highest peak in
March and declined sharply in April. From May, the pollen abundance declined again
progressively up to August, and rose again in September, maintaining a relatively stable presence
up to December before declining again to the lowest level in February. Equally, while the pollen
grains of Casuarinaceae had only one peak in October; Cyperaceae pollen had its peak in May;
the pollen of Arecaceae declined progressively from its second peak in March to July, after which
it increased again reaching its peak in December. It subsequently declined from November to
February while those of Combretaceae fluctuated considerably from the
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a. Monthly pollen variation in Location 1 b. Monthly pollen variation in Location 2
c. Monthly pollen variation in Location 3 d. Monthly pollen variation in Location 4
Fig. 3: Monthly variation in spatial distribution of pollen types across study locations (a) 1 (b) 2 (c) 3 (d) 4
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a. Monthly pollen variation in Location 5 b. Monthly pollen variation in Location 6
c. Monthly pollen variation in Location 7 d. Monthly pollen variation in Location 8
Fig. 4: Monthly variation in spatial distribution of pollen types across study locations (a) 5 (b) 6 (c) 7 (d) 8
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a. Monthly pollen variation in Location 9 b. Monthly pollen variation in Location 10
c. Monthly pollen variation in Location 11 d. Monthly pollen variation in Location 12
Fig. 5: Monthly variation in spatial distribution of pollen types across study locations (a) 9 (b) 10 (c) 11 (d) 12
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a. Monthly pollen variation in Location 13 b. Monthly pollen variation in Location 14
c. Monthly pollen variation in Location 15 d. Monthly pollen variation in Location 16
Fig. 6: Monthly variation in spatial distribution of pollen types across study locations (a) 13 (b) 14 (c) 15 (d) 16
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a. Monthly pollen variation in Location 17 b. Monthly pollen variation in Location 18
c. Monthly pollen variation in Location 19 d. Monthly pollen variation in Location 20
Fig. 7: Monthly variation in spatial distribution of pollen across study locations (a) 17 (b) 18 (c) 19 (d) 20
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months of February to June having high occurrences in February, May, June, and September to October.
There were noticeable monthly increase and decrease in the quantity of pollen grains recorded across the
study locations. The cumulative monthly variation in spatial distribution of pollen counts at Location 1
showed that the frequency increased steadily in September and had its highest peak in November and
thereafter declined progressively in December to February and had its second peak in the month of
March. At study Location 2, the pollen counts increases steadily up to the month of October and had its
highest peak of abundance in December and decline in January, rise in March and fluctuates steadily
between the months of April to August and had its second peak in September. Similar parttern of increase
and decrease in pollen counts were observed across the study locations (Fig. 3 to 7).
Similarly, there were also noticeable variations in the quantity and quality of pollen grains recorded
across the study locations. Possibly, differences in the density and distribution of the pollen–producing
plants over the study areas may be one of the primary factors that contributed to the observed variations in
pollen abundance. The pattern of pollen grains distribution across the locations is illustrated in Figures 3
through 7. Similar findings on the airborne pollen flora of Jabulur- the Central India were previously
reported (Al-Eisawi and Danjani, 1988; Mishra et al., 2002). Meteorological parameters were obtained
during the study period and are presented in Table 5.
Table 5: Mean meteorological data of Akoko division, Ondo State, Nigeria
Meteo. Tempe- Relative Light Wind Atmospheric Wind
parameters/ rature humidity Rainfall intensity direction pressure speed
Months (0C) (%) (mm) (W/m2) (degrees) (mbar) (m/s)
October 27.04 81.63 4.86 158.85 182.25 969.86 0.99
November 27.34 71.89 0.28 178.4 175.24 968.98 0.95
December 27.32 62.59 1.14 172.56 173.81 968.54 0.89
January 27.3 55.68 1.46 153.85 168.98 968.62 0.98
February 29.97 67.83 1.3 170.39 162.81 967.93 1.28
March 30.29 68.71 0.33 176.79 157.22 967.81 1.36
April 29.05 75.34 0.26 169.88 169.43 968.2 1.37
May 28.91 79.38 0.46 167.24 181.48 969.28 1.17
June 28.1 81.73 0.1 145.26 180.26 971.01 1.2
July 26.69 67.96 177.86 100.87 168.73 971.05 1.26
August 25.85 62.17 11.14 114.62 123.11 970.6 1.38
September 26.97 76.36 4.59 80.63 174.75 970.72 0.93
Sources: Department of Geography and Planning Sciences, Adekunle Ajasin University, Akungba-Akoko and the
Centre for Atmospheric Research, National Space Research and Development Agency, Federal Ministry of Science
and Technology, Akungba, Nigeria.
Major meteorological parameters have long been reported to influence the release, distribution and
settling of airborne pollen concentration. Naturally, temperature promotes the dehydration of anthers and
dries up pollen grains, and consequently increases the chances of anther dehiscence as well as the ease of
pollen dispersal. This may be responsible for the high number of positive correlation obtained between
some of the pollen types and air temperature. This finding compares favourably with the reports of Yates
and Sparks (1993); Barnes et al. (2001), and van Vliet et al. (2002) who related pollen increase to
increase in temperature.
The magnitude of positive or negative effect of temperature on pollen concentration has been explained
on the basis of its interaction with other meteorological parameters such as relative humidity and rainfall
(McDonald, 1980; Bicakel et al., 1999). According to Spieksma et al. (1985), the beginning of grass
(Poaceae) pollen season seems to be triggered off by a rise in temperature. However, the negative
correlation obtained has also been similarly reported by Mishra et al. (2002) and Khandelwal (1988).
17Ige & Essien ….. Int. J. Innovative Biosciences Res 9(1):1-20, 2021
Because there are more positive correlations with pollen types, it can be concluded that temperature plays
an important role in pollen release.
The decreasing effect of relative humidity on pollen concentration was noticed in this study and this may
be responsible for the negative correlation observed among some of the pollen types analysed. Airborne
pollen wash-out has been associated with high relative humidity. At high condensation, it washes out
pollen grains from the air by formation of large molecules of water droplets particularly during the early
hours of the morning. Similar findings have been reported by Bicakel et al. (1999) and Alba et al. (2000).
However, pollen release and to a greater extent dispersal has been related to daily patterns of temperature,
air current, relative humidity and electrostatic conditions (Spieksma et al., 1985).
Rainfall has been consistently reported as one of the major meteorological factors that lower the
concentration of airborne pollen grains by atmospheric washout and acceleration of the process of their
precipitation and deposition. Also the low pollen counts obtained in the months of June, July and August
may be due to atmospheric washout of pollen because these months are periods associated with constant
rainfall (Table 5). Like the relative humidity, over 75% of the analysed plants showed negative
relationship. It could then be concluded that rainfall and high relative humidity play significant roles in
decreasing atmospheric pollen content. Similar findings were also reported by Alba et al. (2000); Barnes
et al. (2001); Mishra et al. (2002); Njokuocha and Ezenwajiaku (2010).
The concentration and diversity of pollen types at any given time and place may be influenced by wind
direction relative to the location of the sampler. Because the source vegetation are not uniformly
distributed in terms of abundance, diversity, pollen production, flowering season and mode of pollination,
the distribution and concentration of pollen along any direction of the wind will be strongly influenced by
the strength of the wind along its direction both in time and space. Similar relationship and interactions
among individual pollen and plant families with meteorological parameters were observed in the study.
4. CONCLUSION
The result of monitoring spatial and temporal distribution pattern of airborne pollen grains in some
selected localities within Akoko division, Ondo State, Nigeria gives information on diurnal, seasonal and
annual variations in the pollen types and their concentration in the atmosphere. Study revealed that the
pollen load of the entire study area varied quantitatively not only from month-to-month but also from site-
to-site. The period of collection of pollen grains (in months) does not affect the dispersal mechanism and
rate of concentration in the atmosphere. For instance, the highest monthly pollen count was recorded for
the month of November and the lowest in July and August. In the same way, atmospheric pollen studies
conducted in various parts of the world showed that there were variations not only in monthly pollen
concentration, but also site-to-site variations in monthly pollen content of major individual pollen types as
regards maximum count. The study also showed that the concentration of pollen grains in the atmosphere
fluctuates considerably with the seasons. The period of pollen abundance, variability and distribution in
the atmosphere revealed that the highest mean concentration occurred during the late rainy season to early
dry season, followed by the period of dry season to early rainy season and the least occurred during the
mid-rainy season.
DISCLOSURE OF CONFLICT OF INTEREST
The authors declare that there is no conflict of interest.
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