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<p>Biometeorology typifies climate change threats with indices such as the thermal stress index (TSI) for heat-related illnesses. The study aimed to evaluate the actual TSI (<i>which has five categories</i><i>: </i><i>TSI</i> &lt; <i>27 (safe), 27</i> &lt; <i>TSI</i> &lt; <i>32 (heat fatigue), 32</i> &lt; <i>TSI</i> &lt; <i>41 (sunstroke and heat exhaustion), 41</i> &lt; <i>TSI</i> &lt; <i>54 (sunstroke and heat cramps), and TSI</i> &gt; <i>54 (sunstroke, heat stroke and heat confusion, or delirium</i>) across four rain-fed provinces (Markudi in Benue state, Benin in Edo state, Minna in Niger state and Ondo in Ondo state) using 32-year (1987–2018) monthly temperature and relative humidity data obtained from Nigeria Meteorology Services, Abuja. The TSI was computed for each state, and its average was compared across the states. Pearson’s correlation momentum (PCM), Normal probability residual plot (NPRP), Multiple regression analysis (MRA), and Principal component analysis (PCA) were also adopted. The results showed that there were more heat fatigue indications in the dry periods than in the wet period in the Benin environment; nearly one-third of the study years (12 out of 32&#xa0;years) indicated heat fatigue, denoting prevalence of climate change (CC) effects in Ondo State and environs. The order of occurrences of heat fatigue (27 &lt; TSI &lt; 32) in Minna and its environs were peculiar to April &gt; March &gt; May with 21, 7, and 6 values of occurrences, respectively. Both 2004 and 2018 had 4-month with the highest indication of heat fatigue (27 &lt; TSI &lt; 32) in Makurdi environs. The multivariate analysis: PCM, NPRP, MRA, and PCA indicated various monthly strong TSI associations at 0.01 level of statistics; valid and established homoscedasticity for the model; significant reduced effects in February, June and October while increased effect in September; and the seven wet season, four dry season (November to February), and March had 47, 19 and 10% variance across component 1, 2, and 3, respectively. The study concluded that the most prevalent TSI category was fatigue, against which the study advised healthcare education for dwellers and their environs to cope with the CC effects.</p>

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Thermal stress indication for heat-related illnesses by climate change in Nigeria using multivariate analysis

  • 期刊论文

  • 发表日期：

    2025年4月25日

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  • Adeleke Taofik Towolawi ,
  • Olusegun Oguntoke,
  • Babatunde Saheed Bada,
  • Joseph O. Adejuwon,
  • Abidemi Kafayat Adegbore,
  • Baseerat Adebola Abdulsalami,
  • Zainab Opeoluwa Buari

摘要

Biometeorology typifies climate change threats with indices such as the thermal stress index (TSI) for heat-related illnesses. The study aimed to evaluate the actual TSI (which has five categories: TSI < 27 (safe), 27 < TSI < 32 (heat fatigue), 32 < TSI < 41 (sunstroke and heat exhaustion), 41 < TSI < 54 (sunstroke and heat cramps), and TSI > 54 (sunstroke, heat stroke and heat confusion, or delirium) across four rain-fed provinces (Markudi in Benue state, Benin in Edo state, Minna in Niger state and Ondo in Ondo state) using 32-year (1987–2018) monthly temperature and relative humidity data obtained from Nigeria Meteorology Services, Abuja. The TSI was computed for each state, and its average was compared across the states. Pearson’s correlation momentum (PCM), Normal probability residual plot (NPRP), Multiple regression analysis (MRA), and Principal component analysis (PCA) were also adopted. The results showed that there were more heat fatigue indications in the dry periods than in the wet period in the Benin environment; nearly one-third of the study years (12 out of 32 years) indicated heat fatigue, denoting prevalence of climate change (CC) effects in Ondo State and environs. The order of occurrences of heat fatigue (27 < TSI < 32) in Minna and its environs were peculiar to April > March > May with 21, 7, and 6 values of occurrences, respectively. Both 2004 and 2018 had 4-month with the highest indication of heat fatigue (27 < TSI < 32) in Makurdi environs. The multivariate analysis: PCM, NPRP, MRA, and PCA indicated various monthly strong TSI associations at 0.01 level of statistics; valid and established homoscedasticity for the model; significant reduced effects in February, June and October while increased effect in September; and the seven wet season, four dry season (November to February), and March had 47, 19 and 10% variance across component 1, 2, and 3, respectively. The study concluded that the most prevalent TSI category was fatigue, against which the study advised healthcare education for dwellers and their environs to cope with the CC effects.