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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">ajcc</journal-id>
      <journal-title-group>
        <journal-title>American Journal of Climate Change</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2167-9509</issn>
      <issn pub-type="ppub">2167-9495</issn>
      <publisher>
        <publisher-name>Scientific Research Publishing</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.4236/ajcc.2025.141004</article-id>
      <article-id pub-id-type="publisher-id">ajcc-141053</article-id>
      <article-categories>
        <subj-group>
          <subject>Article</subject>
        </subj-group>
        <subj-group>
          <subject>Earth</subject>
          <subject>Environmental Sciences</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Monthly Variability of Selected Weather Elements in the Portharcourt Urban Enclaves, Rivers State, Nigeria from 2010 to 2020</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0001-3107-4737</contrib-id>
          <name name-style="western">
            <surname>Chukwudi</surname>
            <given-names>Dollah Osademe</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Francis</surname>
            <given-names>Onyeananam Udochukwu</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <contrib-id contrib-id-type="orcid">0000-0002-3286-8723</contrib-id>
          <name name-style="western">
            <surname>Famous</surname>
            <given-names>Ozabor</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-9976-6246</contrib-id>
          <name name-style="western">
            <surname>Onyeayana</surname>
            <given-names>Wekpe Vremudia</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">3</xref>
        </contrib>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Adekunle</surname>
            <given-names>Obisesan</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">3</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1"><label>1</label> Institute of Geosciences and Environmental Management, Rivers State University, Port Harcourt, Nigeria </aff>
      <aff id="aff2"><label>2</label> Department of Environmental Management, Dennis Osadebay University, Asaba, Nigeria </aff>
      <aff id="aff3"><label>3</label> Department of Geography and Environmental Management, University of Portharcourt, Choba, Nigeria </aff>
      <author-notes>
        <fn fn-type="conflict" id="fn-conflict">
          <p>The authors declare no conflicts of interest regarding the publication of this paper.</p>
        </fn>
      </author-notes>
      <pub-date pub-type="epub">
        <day>13</day>
        <month>02</month>
        <year>2025</year>
      </pub-date>
      <pub-date pub-type="collection">
        <month>02</month>
        <year>2025</year>
      </pub-date>
      <volume>14</volume>
      <issue>01</issue>
      <fpage>61</fpage>
      <lpage>74</lpage>
      <history>
        <date date-type="received">
          <day>02</day>
          <month>12</month>
          <year>2024</year>
        </date>
        <date date-type="accepted">
          <day>04</day>
          <month>03</month>
          <year>2025</year>
        </date>
        <date date-type="published">
          <day>07</day>
          <month>03</month>
          <year>2025</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>© 2025 by the authors and Scientific Research Publishing Inc.</copyright-statement>
        <copyright-year>2025</copyright-year>
        <license license-type="open-access">
          <license-p> This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link> ). </license-p>
        </license>
      </permissions>
      <self-uri content-type="doi" xlink:href="https://doi.org/10.4236/ajcc.2025.141004">https://doi.org/10.4236/ajcc.2025.141004</self-uri>
      <abstract>
        <p>The study examined Monthly Variability of selected weather elements in the Portharcourt urban enclaves, Rivers State, Nigeria from 2010 to 2020. Monthly weather data for 2010 to 2020 on temperature, rainfall, relative humidity and wind speed were obtained from Nigerian Meteorological Agency (NIMET) and used for the study. Both descriptive and inferential statistics were used for data analyses. Findings showed that mean monthly rainfall, relative humidity, temperature and wind speed were 2085.06 mm, 77.01%, 27.93˚C and 2.30 m/s respectively. The trend analysis shows that rainfall increased at the rate of 57.73 mm from January to December while relative humidity increased at the rate of 1.4846%. Monthly temperature was decreasing at the rate of 0.1075˚C and wind speed was also decreasing at the rate of 0.0562 m/s. The study concluded that the monthly rainfall, relative humidity and temperature varied significantly from January to December, due to natural and anthropogenic factors, while the wind speed was not varied, due to terrain and pressure gradient characteristics which are similar across the area. The month of highest rainfall was June, temperature was February, relative humidity was September and wind speed was August. The study recommended that more periodic monitoring studies are required to give in-depth knowledge about the variability in the weather parameters in Portharcourt Metropolis. Additionally, more studies on the influence of the seasons and land-use types on climate parameters should be initiated in the area.</p>
      </abstract>
      <kwd-group kwd-group-type="author-generated" xml:lang="en">
        <kwd>Variability</kwd>
        <kwd>Temperature</kwd>
        <kwd>Relative Humidity</kwd>
        <kwd>Wind Speed</kwd>
        <kwd>Monitoring</kwd>
        <kwd>Port Harcourt</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec1">
      <title>1. Introduction</title>
      <p>It is possible and has since been established in literature that weather variability happens within any climate system ([<xref ref-type="bibr" rid="B44">44</xref>]; [<xref ref-type="bibr" rid="B9">9</xref>]). The reasons for occurrence ranges from natural factors (revolution, rotation, volcanic eruptions etc.) to manmade factors (urbanization, deforestation, atmospheric pollution, etc.) ([<xref ref-type="bibr" rid="B53">53</xref>]). Recent studies ([<xref ref-type="bibr" rid="B61">61</xref>]; [<xref ref-type="bibr" rid="B52">52</xref>]) have emphasized the need for continuous examination of the weather and climatic systems, due to the impacts of climate variability on the environment, agriculture, food security and health. Also, studies have revealed that weather variability has been linked to urban flooding ([<xref ref-type="bibr" rid="B36">36</xref>]; [<xref ref-type="bibr" rid="B45">45</xref>]; [<xref ref-type="bibr" rid="B11">11</xref>]) insecurity, indoor thermal discomfort ([<xref ref-type="bibr" rid="B42">42</xref>], [<xref ref-type="bibr" rid="B43">43</xref>]), and food fermentation ([<xref ref-type="bibr" rid="B30">30</xref>]).</p>
      <p>[<xref ref-type="bibr" rid="B64">64</xref>] identified that fluctuating relative humidity (RH) affects temperature characteristics. The same is also reported for relationships between the fluctuations in RH and rainfall (RF) ([<xref ref-type="bibr" rid="B40">40</xref>]). This indicates that changes in the characteristics of temperature affects rainfall and RH ([<xref ref-type="bibr" rid="B41">41</xref>]; [<xref ref-type="bibr" rid="B34">34</xref>]). Additionally, the rates of solar radiation (SR) and urbanization characteristic (UC) is linked to temperature changes ([<xref ref-type="bibr" rid="B28">28</xref>]). [<xref ref-type="bibr" rid="B17">17</xref>] averred that the urban expansion, population rise, deforestation, and air pollution affects temperature variability. Also, [<xref ref-type="bibr" rid="B25">25</xref>] identified that variations in SR causes variation in temperature. [<xref ref-type="bibr" rid="B63">63</xref>] asserted that SR variability correlated with increased rainfall variability. The issue of climate change is certainly not one that surfaced recently ([<xref ref-type="bibr" rid="B48">48</xref>]); climate change has been an issue since early civilization and scientists have been sounding the alarms about climate change ([<xref ref-type="bibr" rid="B3">3</xref>]). In Southern Nigeria however, attenuation in rain patterns observed, results in unpredictable flooding, which has been detrimental to both mining and deep-water aquaculture in that area ([<xref ref-type="bibr" rid="B50">50</xref>]; [<xref ref-type="bibr" rid="B31">31</xref>]). Department for International Development (DFID) history also included in 2009 documents large loss in the manufacturing sector due to possibilities of flooding that affected the fulfilment of production objectives ([<xref ref-type="bibr" rid="B56">56</xref>]). This is especially true because the sector’s agricultural input is quite sensitive to climate change ([<xref ref-type="bibr" rid="B39">39</xref>]; [<xref ref-type="bibr" rid="B32">32</xref>])</p>
      <p>The adverse consequences of climate change such as, increased temperature have also resulted in more frequent and severe heat waves ([<xref ref-type="bibr" rid="B26">26</xref>]), with the worst summers registered to date being in the year of 2003 ([<xref ref-type="bibr" rid="B51">51</xref>]); where heat waves in that year saw nearly 40,000 people die from excess heat primarily in Europe ([<xref ref-type="bibr" rid="B16">16</xref>]). This event and its magnitude has since been eclipsed by the Russian heat-wave of the year 2010 ([<xref ref-type="bibr" rid="B21">21</xref>]), but that too resulted in excess deaths of 55,000 with estimated economic losses of an estimated 15 billion dollars. All these climate change milieu are traceable to manmade contributions through urbanisation, pollution and deforestation ([<xref ref-type="bibr" rid="B18">18</xref>]).</p>
      <p>Greenhouse gas (GHG) emissions in Nigeria and other parts of the world however, have largely been attributed to human activities including agriculture ([<xref ref-type="bibr" rid="B58">58</xref>]). In the climate change discussion, projections are that hundreds of millions of people (especially in developing parts of the world) will be at risk of water and food shortages ([<xref ref-type="bibr" rid="B2">2</xref>]) that will harm health over the coming decades ([<xref ref-type="bibr" rid="B47">47</xref>]). Thus, governments and all concerned, must act jointly on a global scale to minimize the permanent effects of climatic variability and change ([<xref ref-type="bibr" rid="B1">1</xref>]). However, a modest increase in global temperature is expected to exacerbate the nature, frequency and intensity of extreme climatic events ([<xref ref-type="bibr" rid="B65">65</xref>]).</p>
      <p>Human activities have drastically changed many aspects of the atmosphere ([<xref ref-type="bibr" rid="B24">24</xref>]), including temperature ([<xref ref-type="bibr" rid="B29">29</xref>]), rainfall ([<xref ref-type="bibr" rid="B37">37</xref>]), levels of carbon dioxide ([<xref ref-type="bibr" rid="B22">22</xref>]) and levels of ozone close to the ground ([<xref ref-type="bibr" rid="B46">46</xref>]). The Intergovernmental Panel on Climate Change (IPCC) averred that, despite its lack of scepticism about specific climate change, the warming of the climate system is undeniable ([<xref ref-type="bibr" rid="B8">8</xref>]). As GHG levels rise, models predict that the frequency and intensity of heat waves will increase globally throughout the 21st century ([<xref ref-type="bibr" rid="B12">12</xref>]; [<xref ref-type="bibr" rid="B19">19</xref>]). Dramatic declines in soil moisture in the United States are expected to further increase summer temperatures in parts of the Midwest ([<xref ref-type="bibr" rid="B54">54</xref>]). Historically, rare heat waves, which occurred once every 20 years, are expected to occur every 2 - 3 years in the second half of this century ([<xref ref-type="bibr" rid="B62">62</xref>]). By the end of the century, under the IPCC High Emissions Scenario (AR5), extreme events like the 2010 Russian heat wave are projected to occur every two years in places like Southern Europe, North and South America, Africa and Indonesia ([<xref ref-type="bibr" rid="B27">27</xref>]). Although extreme heat waves and heat waves are common, unusually cold weather has decreased dramatically since the 1950s ([<xref ref-type="bibr" rid="B7">7</xref>]). For example, in 2015, 16% of the Earth experienced an extremely warm year, while only 0.2% experienced an extremely cold year ([<xref ref-type="bibr" rid="B20">20</xref>]). Similarly, in 2014, no significant cold rainfall was observed anywhere on Earth, while 12% experienced significantly warmer rainfall ([<xref ref-type="bibr" rid="B55">55</xref>]). Climate models have shown that these effects are primarily driven by high levels of GHGs ([<xref ref-type="bibr" rid="B60">60</xref>]).</p>
      <p>The 2000s in the US, has experienced fewer cold waves since 1895, more than double the lows of the late 1990s ([<xref ref-type="bibr" rid="B23">23</xref>]). Projections suggest a sharp decline in the coldest temperatures by the end of the 21st century, and the coldest nights of the year were warmer than the warmest days, especially in the northern elevations ([<xref ref-type="bibr" rid="B5">5</xref>]). At the same time, winter storm tracks moved northward, which is increasing and becoming more frequent since the 1950s ([<xref ref-type="bibr" rid="B10">10</xref>]).</p>
      <p>The rain-fed and dependent agriculture in the southern region of Nigeria makes it particularly vulnerable to climate change ([<xref ref-type="bibr" rid="B13">13</xref>]). This is because weather affects agriculture directly or indirectly. Previous studies ([<xref ref-type="bibr" rid="B30">30</xref>]; [<xref ref-type="bibr" rid="B44">44</xref>]) have shown variations in climate over time and space, however these studies did not specifically focus on Portharcourt. The current study examined climate variability and plausible natural and anthropogenic factors affecting temperature, rainfall, and humidity in Rivers State. The variability in climate, have severe impacts on agriculture, food security, health, and urban flooding. Therefore, this study examined the monthly changes in temperature, precipitation, humidity and wind speed from 2010 to 2020 in Portharcourt Metropolis, Rivers State, Nigeria.</p>
    </sec>
    <sec id="sec2">
      <title>2. Materials and Methods</title>
      <p>The study area for this study is Portharcourt Metropolis, which is a coastal area in the southern part of Nigeria ([<xref ref-type="bibr" rid="B38">38</xref>]). Geographically, Portharcourt is located on latitudes 4˚73'06 N and 4˚90'17 N of the equator and longitudes 6˚92'05 E and 7˚07'56 E of the Greenwich meridian (<xref ref-type="fig" rid="fig1">Figure 1</xref>). Over the years, the city has experienced rapid urbanization driven by factors such as natural population growth, employment opportunities created by the oil boom of the 1970s, migration ([<xref ref-type="bibr" rid="B14">14</xref>]; [<xref ref-type="bibr" rid="B15">15</xref>]), improvements in transportation and communication. It is estimated that the presence of multinational oil companies has influenced the growth in population in this region of Nigeria ([<xref ref-type="bibr" rid="B57">57</xref>]).</p>
      <p>The development of Portharcourt is incredibly linked to the socio-economic history of Nigeria ([<xref ref-type="bibr" rid="B33">33</xref>]). <xref ref-type="fig" rid="fig1">Figure 1</xref> shows the study areas in Portharcourt Municipality. The rapid population growth coupled with the lack of housing has created many urban challenges such as unplanned sprawl, environmental pollution, waste challenges, lack of basic infrastructure, poverty, urban decay, overcrowding, irregular rural and settlement ([<xref ref-type="bibr" rid="B48">48</xref>]). However, it has been reported that private companies have been slow to meet the demands of the growing urban population. Historically, about 80% of the housing developments in the Port have been developed by the Harcourt private sector. However, these private companies prefer to develop buildings for Medium- and high income groups, neglecting the housing demands of lower income groups ([<xref ref-type="bibr" rid="B48">48</xref>]). However, there is a link between these population growth, urbanization and climate variability in the area.</p>
      <p>In terms of climate, the area falls under the koppens classification with high rainfall (2015 - 2250 mm), high relative humidity (85% to 95%) high annual mean temperature (27˚C - 30˚C). However, the changing urbanization characteristics and other anthropogenic activities have been affecting the climate over the year, causing effects on the local climate in the area ([<xref ref-type="bibr" rid="B33">33</xref>]; [<xref ref-type="bibr" rid="B59">59</xref>]).</p>
      <fig id="fig1">
        <label>Figure 1</label>
        <graphic xlink:href="https://html.scirp.org/file/2361531-rId16.jpeg?20260610051510" />
      </fig>
      <p><bold>Figure 1</bold><bold>.</bold> Port Harcourt Metropolis.</p>
      <p>Longitudinal research design was deployed in this study. The annual and monthly record of temperature, rainfall, relative humidity and wind speed from 2010 to 2020 were collected from the Nigerian Meteorological Agency (NiMet). The meteorological data were grouped into both wet season (April-October) and dry season (November-March) and used for further analysis. The descriptive statistics involved the means, standard deviation, frequency and percentages while analysis of variance was used to determine the significant variability in climate data. The null hypothesis which states ‘there is no significant variation in the rainfall, temperature and relative humidity in the study area’ was tested using Analysis of Variance (ANOVA). These analyses were done using the IBM/statistical package for the social sciences (SPSS) version 25.</p>
    </sec>
    <sec id="sec3">
      <title>3. Results and Discussions</title>
      <p>The monthly analysis of meteorological data is presented in <bold>Table 1</bold>. It is shown that the minimum monthly total rainfall was 162.50 mm and the maximum was 3697.20 mm while the mean monthly rainfall was 2085.06 mm. The monthly relative humidity had a minimum value of 44.25% and the maximum was 83.93% while the mean relative humidity was 77.01%. The monthly temperature analysis shows that the minimum was 26.41˚C and the maximum was 29.35˚C with the mean temperature of 27.93˚C. The wind speed monthly analysis reveals that the minimum was 1.81 m/s, maximum of 2.59 m/s with mean wind speed of 2.30 m/s.</p>
      <p>Furthermore, it is observed that the rainfall monthly analysis shown in <xref ref-type="fig" rid="fig1">Figure 1</xref> reveals that temperature was higher in both June and September with above 3500 mm of rainfall. The lower rainfall was experienced in January and December. It is thus observed that the trend surface line increases with increasing in the month from January to December. The trend line also showed that rainfall increased at a rate of 57.728 mm from January to December. The R Square analysis reveals that 2.72% of the variation in the rainfall could be explained by the variation in the months.</p>
      <p>The relative humidity monthly analysis in <xref ref-type="fig" rid="fig2">Figure 2</xref> shows that the least was experienced in January while the higher relative humidity was experienced between June and November. The trend surface analysis reveals that relative humidity increases at the rate of 1.4846% and the R Square analysis reveals that 22.04% of the variation of relative humidity could be explained by the variation in the months between January and December.</p>
      <p><bold>Table 1</bold><bold>.</bold> Descriptive statistics of monthly analysis of meteorological parameters.</p>
      <table-wrap id="tbl1">
        <label>Table 1</label>
        <table>
          <tbody>
            <tr>
              <td>Meteorological Parameters</td>
              <td>Minimum</td>
              <td>Maximum</td>
              <td>Mean</td>
              <td>Std. Deviation</td>
            </tr>
            <tr>
              <td>Monthly Total Rainfall</td>
              <td>162.50</td>
              <td>3697.20</td>
              <td>2085.06</td>
              <td>1261.9</td>
            </tr>
            <tr>
              <td>Monthly Mean Relative Humidity</td>
              <td>44.25</td>
              <td>83.93</td>
              <td>77.01</td>
              <td>11.0</td>
            </tr>
            <tr>
              <td>Monthly Mean Temperature</td>
              <td>26.41</td>
              <td>29.35</td>
              <td>27.93</td>
              <td>1.0</td>
            </tr>
            <tr>
              <td>Monthly Mean Wind Speed</td>
              <td>1.81</td>
              <td>2.59</td>
              <td>2.30</td>
              <td>0.25</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <fig id="fig2">
        <label>Figure 2</label>
        <graphic xlink:href="https://html.scirp.org/file/2361531-rId17.jpeg?20260610051510" />
      </fig>
      <p><bold>Figure 2</bold><bold>.</bold> Total monthly rainfall of Port Harcourt from 2010 to 2020.</p>
      <p>The monthly analysis of temperature of Port Harcourt between 2010 and 2020 shows that temperature was lower between June and July while the highest temperature was noticed in February and December (<xref ref-type="fig" rid="fig3">Figure 3</xref>). However, the trend surface analysis reveals that monthly temperature was decreasing at the rate of 0.1075˚C and the R Square analysis shows that 14.97% of the variation in the monthly temperature could be explained by the variation in the months between January and December.</p>
      <p>Similarly, the analysis of monthly wind speed of Port Harcourt displayed in <xref ref-type="fig" rid="fig4">Figure 4</xref> reveals that the wind speed across the months was lower than 3.00 m/s </p>
      <fig id="fig3">
        <label>Figure 3</label>
        <graphic xlink:href="https://html.scirp.org/file/2361531-rId18.jpeg?20260610051510" />
      </fig>
      <p><bold>Figure 3</bold><bold>.</bold> Monthly relative humidity of Port Harcourt from 2010 to 2020.</p>
      <fig id="fig4">
        <label>Figure 4</label>
        <graphic xlink:href="https://html.scirp.org/file/2361531-rId19.jpeg?20260610051510" />
      </fig>
      <p><bold>Figure 4</bold><bold>.</bold> Monthly temperature of Port Harcourt from 2010 to 2020.</p>
      <fig id="fig5">
        <label>Figure 5</label>
        <graphic xlink:href="https://html.scirp.org/file/2361531-rId20.jpeg?20260610051510" />
      </fig>
      <p><bold>Figure 5</bold><bold>.</bold> Monthly wind speed of Port Harcourt from 2010 to 2020.</p>
      <p><bold>Table 2</bold>. ANOVA of monthly meteorological parameters.</p>
      <table-wrap id="tbl2">
        <label>Table 2</label>
        <table>
          <tbody>
            <tr>
              <td colspan="2">
              </td>
              <td>Sum of Squares</td>
              <td>df</td>
              <td>Mean Square</td>
              <td>F</td>
              <td>Sig.</td>
            </tr>
            <tr>
              <td rowspan="3">Rainfall</td>
              <td>Between Groups</td>
              <td>15,92,608.822</td>
              <td>11</td>
              <td>144,782.620</td>
              <td>30.358</td>
              <td>0.000</td>
            </tr>
            <tr>
              <td>Within Groups</td>
              <td>572,296.986</td>
              <td>120</td>
              <td>4769.142</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Total</td>
              <td>2,164,905.807</td>
              <td>131</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td rowspan="3">Relative Humidity</td>
              <td>Between Groups</td>
              <td>14,689.533</td>
              <td>11</td>
              <td>1335.412</td>
              <td>28.188</td>
              <td>0.000</td>
            </tr>
            <tr>
              <td>Within Groups</td>
              <td>5684.953</td>
              <td>120</td>
              <td>47.375</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Total</td>
              <td>20,374.486</td>
              <td>131</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td rowspan="3">Temperature</td>
              <td>Between Groups</td>
              <td>121.309</td>
              <td>11</td>
              <td>11.028</td>
              <td>5.054</td>
              <td>0.000</td>
            </tr>
            <tr>
              <td>Within Groups</td>
              <td>261.839</td>
              <td>120</td>
              <td>2.182</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Total</td>
              <td>383.148</td>
              <td>131</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td rowspan="3">Wind Speed</td>
              <td>Between Groups</td>
              <td>7.956</td>
              <td>11</td>
              <td>0.723</td>
              <td>1.710</td>
              <td>0.079</td>
            </tr>
            <tr>
              <td>Within Groups</td>
              <td>50.744</td>
              <td>120</td>
              <td>0.423</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Total</td>
              <td>58.700</td>
              <td>131</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p>and it continued to decrease from January to December. The trend surface line continues to decrease from January to December and the decrease was at the rate of 0.0562 m/s. The R Square analysis suggests that 62.41% of variation in the wind speed could be explained by the variation in the months.</p>
      <p>The analysis in <bold>Table</bold><bold>2</bold> presents the analysis of variance of the monthly meteorological parameters and it is revealed that total rainfall (F = 30.358; p &lt; 0.05), relative humidity (F = 28.188; p &lt; 0.05) and temperature (F = 5.054; p &lt; 0.05) were significantly varied among the months while the wind speed was not significantly varied by months. Therefore, the null hypothesis is accepted for only the wind speed while the alternative hypothesis is accepted for total rainfall, relative humidity and temperature.</p>
    </sec>
    <sec id="sec4">
      <title>4. Discussion of Findings</title>
      <p>The monthly relative humidity was higher from June to October affirming its significant influence on the total rainfall. The wind speed was highest in August and the least was observed in December. The least in December may be attributed to the presence of harmattan whereby several suspended dust particles could slow down the wind movement. The monthly analysis for the rainfall was increasing at the rate of 57.728 mm and the months of June and September served as the higher peaks of rainfall. This is corroborated the findings of [<xref ref-type="bibr" rid="B59">59</xref>] that observed highest rainfall amount average prevailed in the area from July to September. [<xref ref-type="bibr" rid="B35">35</xref>] studied monthly rainfall distribution in Nigeria between 1985-1994 and 1995-2004 and noticed some fluctuations in most months within the decades. [<xref ref-type="bibr" rid="B4">4</xref>] also investigated the seasonal rainfall variability in Guinea savannah part of Nigeria and concluded that rainfall variability continues to be on the increase as an element of climate change. Portharcourt experienced more rainfall months than dry months. This is in agreement to the study of [<xref ref-type="bibr" rid="B6">6</xref>] which revealed that in the tropical coastal belts, it was observed that there are more wet months than dry ones. Similar to rainfall is relative humidity which was higher between June and October. The hottest months were February and December while the coldest months were June and July. The wind speed in the entire study area was relatively low and it shows a calm condition. However, the highest windy month was August and the least windy month was November. Rainfall, relative humidity and temperature varied significantly among the months.</p>
    </sec>
    <sec id="sec5">
      <title>5. Conclusion and Recommendations</title>
      <p>The study concluded that the monthly rainfall, relative humidity and temperature varied significantly from January to December while the wind speed was less erratic. The month with the highest rainfall was June. Temperature was hottest in February, while relative humidity was highest in the month of September. The study recommended that more periodic monitoring studies are required to have more in-depth knowledge about the variability in the weather parameters in Port Harcourt Metropolis and more studies on the influence of the seasons and land use types on climate parameters can be proposed and executed. This kind of study should be extended to other urban centres and rural areas for comparison purposes.</p>
    </sec>
  </body>
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