Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 1 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET MANAGEMENT OF THE IMPACTS OF SUDDEN AND SLOW ONSET WEATHER EVENTS ON AVIATION OPERATIONS IN SOUTHEASTERN NIGERIA 1Ogu, Eucharia Chinyere, 2Ozakpo, Ogaga Akpode, 3Kpang, MeeluBari Barinua Tsaro, 4Nwagbara, Moses Okemini 1’2’3Department of Geography and Environmental Management, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria 4Department of Water Resources Management and Agrometeorology, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria DOI: https://doi.org/10.5281/zenodo.17182818 Abstract This study examined the impact of sudden and slow onset weather events on aviation operations in south-eastern Nigeria. The ex post facto and cross-sectional research designs were adopted for in this study. Data were sourced from both primary and secondary sources and the secondary data were hourly data on sudden weather parameters (wind, cloud cover, visibility, and precipitation) and slow weather parameters (minimum and maximum temperature) were collected from the archives of Nigerian Meteorological Agency (NiMet) for a period of 10 years. On the other hand, air traffic data were collected from the airliners while primary data were derived with the aid of distributed copies of questionnaire. Descriptive statistics, statistical diagrams and tables were used to present the data and analysis of variance (ANOVA) test, was used for analysis. The results showed that rainfall, temperature, wind and cloud cover established remarkable variation annually and monthly. Also, rainfall showed lack of consistency in most of the years, but 2013 had a similar pattern across the region whereas temperature ranged between 30⁰C and 35⁰C with Enugu recorded the highest maximum temperature. There was substantial evidence of slow and sudden weather events effects on flight operations which spatially varied across the cities investigated. The revealed effects of the sudden and slow events were delays, cancelation of flights and additional cost for travelers. There was a significant spatial variation in the sudden and slow weather events in the region at p<0.05. The study recommended training and retraining programmes acquisition of automated instruments for real time and accurate data collection, replacement of aging aircraft and weather monitoring facilities and sensitization of passengers on coping techniques with sudden and slow weather events. Keywords: Aviation, Weather, Visibility, Precipitation, Operations, Disruption mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 2 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET 1. INTRODUCTION Weather has a significant impact on airport operations and the performance of the whole aviation network. Delayed operations can be caused by airport capacity constraints due to severe weather conditions (Glass, Davis & Watkins- Lewis, 2022). The prediction of aircraft processes along their whole trajectories is required to achieve punctual operations (Montlaur, Delgado & Prats, 2023). Delays in departure at one airport can affect the network, resulting in system-wide far reaching effects. As documented in the year 2016, reactionary delays were the main cause of delays, followed by turn around delays which accounted for 46% of departure delays (Reitmann & Schultz, 2022). When transport systems are interrupted, delays emerge, introducing uncertainty regarding travelers’ arrival time (Wang, Jin & Sun, 2022). Also, delays directly lead to extra travel time. Furthermore, in reaction to uncertain travel times, travelers may adjust their travelling schedule for potential delays leading to further cost on travel (Kumar & Khani, 2023). Generally, delays emerge when interactions between air transport players (i.e., carriers, airports and air traffic control entities) and external factors (i.e., adverse weather conditions, strikes and other incidents) lead to airport congestion (Lin, 2023).The empirical literature has analyzed two main determinants of airport congestion inherent to the air transport system - congestion externalities in general and how the structure of the airport network allows for diverse responses to these externalities. The congestion externality argument explains the emergence of delays as a consequence of airlines' failure to internalize the effect their scheduling decisions have on other airlines (Ren, 2023). At hub airports (i.e., airports with one or a few dominant carriers) two contrary forces affect delays. Thus, there is a tradeoff between providing additional connections and rising marginal congestion costs (i.e., increase in delays and connecting times) due to higher traffic numbers (Gnutzmann & Śpiewanowski, 2023). Again, hub airlines have leeway in their scheduling decisions, which allows to partially offset the increased congestion (Porter, 2023). Modern societies are characterized by a high degree of mobility of goods, services and people. Supply chains are interlinked across countries and continents and people increasingly travel for business and private reasons (Sytch, Kim & Page, 2022). As population grows and income increases, the demand for mobility is growing as well (Ahmed, Le & Shahzad, 2022). Overall, the increasing demand for mobility leads to a high dependence on transport systems and their services, with high costs when mobility suddenly is restricted, delayed, or canceled (Deng et al., 2023). Adverse weather conditions are an important external factor affecting delays in the air traffic system as well (Hutter & Pfennig, 2023). Depending on the year and month, they account for up to 50 percent of air traffic delays within the air space (Abdelghany, Guzhva, & Abdelghany, 2023). The impact of weather- related extremes on delays in the aviation system are only scarcely covered in recent empirical literature, due in part to the acclaimed improvements in the aviation systems and fleets. EXisting literature suggests that the general impact of adverse weather conditions on airport and airline operations is substantial and cannot be ignored (Voskaki, Budd, & Mason, 2023). Borsky & Unterberger, 2019) analyzed the impact of different weather shocks on airline operations at the Atlanta Hartsfield International Airport for one airline. He finds that annually over 165,000 min of delay are attributable to adverse weather conditions. Bertness, mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 3 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET (1980) shows that at the end of the 1970s, rainfall substantially increased the number of departures with a delay above 30minutes at Chicago O'Hare airport. Li, Jing & Dong, (2023) examined the impact of thunderstorms on delays at Frankfurt Airport during 1997 and 1998. They discover that thunderstorms significantly increase delays by a factor of 6.3 in 1997 and by 1.1 in 1998. From the forgoing, the aviation industry and associated operations are significantly influenced by weather. Aviation safety, efficiency and capacity are sensitive to weather, and adverse weather can have negative impacts on the sector (Dalal et al., 2023). The increase in aviation demand can push airport's capacity to its limits, and even a small weather change can lead to a reduction of the airport capacity (Dönmez, 2023). Weather conditions affect all aspects of aerodrome operations such as aircraft fueling, cleaning, baggage handling, catering, aircraft maintenance, and the actual scheduled flights. The operational capacity of airports, and even a region’s entire airspace, can be significantly reduced due to bad weather, resulting in delays, diversions and cancellations of flights (Dalmau & Gawinowski, 2023). The problem being envisaged in finding solutions to the rampant air disaster in Nigerian airspace by the aviation authority is the negligence in addressing the weather factors identified among the causative factors (Dempsey, 2002). It was reported by Diamond, (2017) that the civil aviation practice in Nigeria has come to the front burners in recent years because of the fear to fly, as a result of the countless plane crashes that had drummed up public debate on the safety of lives and property. Delays are highly sensitive to the time of day affected by adverse weather, as the greatest amount of delays occur during the highest demand periods (Chen & Wang, 2019). It is against this background, that this study therefore seeks to examine the impact of sudden and slow onset weather events on flights’ departure delays, overall operability and accidents in south-eastern Nigeria. 2. MATERIALS AND METHODS 2.1 Study area Southeastern Nigeria is located within latitudes 4° 47' 35'' N and 7° 7' 44'' N, and longitudes 7° 54' 26'' E and 8° 27' 10'' E. The area comprises nine States namely; Abia, Anambra, Ebonyi, Enugu, Imo, Cross-River, Akwa- Ibom, Rivers and Bayelsa (Onyishi & Ofualagba, 2021). The area covers about 29095 km2 which accounts for 3.19 % of the total area of Nigeria. The area is bounded to the North by Kogi and Benue States, to the east the area is bounded by Cross-River State and bounded to the south and west by Rivers and Delta States respectively (see figure 1). mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 4 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Figure 1: Southeastern Nigeria Showing the selected Airports The region has a tropical climate with humidity and rainfall decreasing from the coast inland, and characterized by uniformly high temperature and a seasonal distribution of bimodal rainfall (Ezemonye & Emeribe, 2012). The rainfall of southern Nigeria generally is heavy and ranges from over 2500 mm in the southern most region towards the Atlantic to about 1500 mm annually around River Benue in the northern borders. The vegetation stretches from the mangrove swamp in the coast through to the derived savanna in the interior (Ezemonye & Emeribe, 2012) but the region lies in the lowland rainforest natural vegetation belt with evergreen trees in the south and gradually gives way northward to rainfall-savanna forest characterized by trees interspersed with grass.The underlying geology consists of heterogeneous materials namely basement complex, beach sands, coastal plain sands, mangrove swamp deposits, sandstones, shale, sombrero Warri- deltaic deposits, recent and sub- recent alluvium (Okereke & Emeribeole, 2020). The soils of the southeastern Nigeria is heterogeneous in nature comprising of loose red-earth with sands, sandstones, clayey-loam with or without ferric properties underlain by shale formation (Okereke & Emeribeole, 2020). Also reported by Isagba et al. (2021) the soils are derived from shale and sandstone parent materials which are deep, porous, and acidic with low organic content as a result of leaching from rainfall activity. mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 5 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET 2.2 Population/Sample size/Sampling Technique The target population for this study includes airline managers for local airlines and passengers of the airports (Akwa-Ibom {Victor Attah International airport} Calabar {Margaret Ekpo International airport}, Imo {Sam Mbakwe} Rivers {Port Harcourt International airport} and Enugu (Akanu-Ibiam airport) that have existed in the area for up to 10 years. In all by consulting the manifest, the researcher found that the total annual passenger traffic for the listed airports were 489235, added to the managers of the airlines that ply the airports, it brings the population to 489247. The weather data for these states were collected for a period of 10 years on hourly scale (synoptic). The reason for sampling on hourly scale was to help the researcher knit an analysis between the days of interruption of flight operations with the prevailing weather circumstance at the time of interruption, whether sudden or slow events. Therefore, the period of sample is 10 years (2011-2020). The ten years’ period was designed to eliminate hasty conclusions in the findings of the study. Secondly, the study was looking are the erraticism of sudden and slow weather events and how it affects aviation and Ayoade (2004), advised that using data from 2-10 years is sufficient for such inquiry. The researcher employed the Taro Yamane Equation to this population and a total of 400 respondents were derived. See equation 1: 𝑛 = 𝑁 1 + 𝑁(𝑒)2) … … … … … … … … … … … … … (1) Therefore, total sample size is 400. 2.3 Sources/Method of Data Collection/Analytical Technique The data used for study was obtained mainly from secondary sources. Hourly data on sudden weather parameters (wind, cloud cover, visibility, precipitation) and slow weather parameters (minimum and maximum temperature) were collected from the Nigerian Meteorological Agency (NiMet) and for a period of 10 years (2011-2020). Similarly, air traffic data (such as delays and cancellation, accidents and reasons for delay and cancellation) were collected from the archives of airliners (Arik, Air peace, Aero contractors and Ibom Air). Data on the perception of passengers view of impacts of sudden and slow onset weather events on aviation were derived with the aid of a questionnaire. Descriptive statistics, statistical diagrams and tables were used for the presentation of data while means and standard deviation were used for description and summarization of data. On the other hand, Analysis of variance (ANOVA) test was used to analyze the hypothetical statement that the effects of sudden-slow weather events on flight operations in the south east is not significantly same. All analyses were done in the statistical package for the social sciences (SPSS) version 26 and Micro-soft Excel 2016 environment. ANOVA Equation is expressed as follows: ANOVA Equation TSS = ∑ 𝑥2 − (∑ 𝑥)2 𝑁 ---- ---- ---- (2) BSS = (∑ 𝑥1)2 𝑛1 + (∑ 𝑥2)2 𝑛2 + (∑ 𝑥3)2 𝑛3 + (∑ 𝑥4)2 𝑛4 − (∑ 𝑥)2 𝑁 … .. … … .. …. (3) mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 6 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET WSS = TSS – BSS ---- ---- …. ……. (4) Where: TSS = Total Sum of Squares BSS = Between Sample Sum of Squares WSS = Within Sample Sum of Squares n1 … n3 = Number of Samples means being compared N = Total items of all groups. 3. DATA PRESENTATION 3.1 The sudden and slow weather event characteristics over the years in South Eastern Nigeria The sudden and slow weather events are those weather events that come up suddenly in the day and can disrupt anthropogenic activities including aviation operations. These sudden and slow events were monitored within the data available in the data archive of the Nigerian meteorological agency. The weather characteristics in the south eastern region of Nigeria are presented in Figures 1-6. Figure 2: Precipitation characteristics in South Eastern Nigeria (2012-2021) In figure2, the rainfall amounts and distribution for the whole region is displayed. In 2012, the highest rainfall amount for the region was recorded in Uyo with annual rainfall amount of 3585 mm of rainfall. The nearest to this amount of rainfall was recorded in Calabar (3495mm). These two locations are due to the fact that they are close to each other, having similar environmental and forcing factors. As such they are expected to have similar rainfall. Although, Enugu and Port Harcourt had similarity in rainfall amounts (1500mm and 1575mm respectively). Although the rainfall amount for Port Harcourt is fairly higher than that of Enugu with about 75mm of rainfall. In 2013 the pattern is similar, although the rainfall amount for all the locations were higher than that of the year 2012. For example, the rainfall amount for Uyo in 2013 was over 4500mm compared to the 3585mm posted in the 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 R ai n fa ll A m o u n t in m m Year Portharcourt Enugu Owerri Calabar Uyo mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 7 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET year 2012 for the same location. This same pattern can be reported therefrom. However, in 2020 whereas other locations were experiencing lowered amount in rainfall, Port Harcourt recorded about 3000mm of rainfall. In 2021, the general rainfall amount for the locations was low. Figure 3 (a), presented the maximum temperature for the study area. The maximum temperature ranged between 30ºC to 32.5ºC. The maximum temperature for Enugu has been on the rise since 2012 and peaked at the year 2018 with a mean maximum temperature of 32.5ºC. Maximum temperature in Enugu also represents the highest in the region. The reason for this is that the area is further hinterland compared to other states of the region. It is also common to see less water bodies around this part of the region. Consequently, the maximum temperature of this area appears to be the highest in the continuum. On the other hand, the Maximum temperature of Calabar revealed that it is the lowest in the study region and ranged between 30ºC. The other locations within the region simply falls within these two extremes described above (figure 3a). For the minimum temperature, it is conspicuous that the mean temperature ranges between 20.5ºC and 24ºC (Figure 3b). Temperature for Owerri appears to be lower than that of other locations within the region. It is possible that the topography and the spatial extent of development in the region partly explains these minimum temperature characteristics. Other location just simply ranged minimum temperature between 21ºC and 24ºC accordingly. However, in Enugu the year 2015 had a remarkably low minimum temperature (21ºC), same can be said of Owerri in 2019. Figure 3: Temperature characteristics in Eastern Nigeria (2012-2021) Wind characteristics is one weather element that is very essential for the operation of the aviation industry. The characteristics of which is very important to measure from time to time and the direction and buoyance determined. This will help determine the magnitude of the wind and adequate precautions taken. For the wind direction of the region showed that it is mostly south west winds in direction (Figure 4). mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 8 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Figure 4: Wind direction in south eastern Nigeria (2012-2021) The speed of the wind is presented in figure 4. The wind appeared to have more speed in the Enugu axis of the region with an average of 7 Knot to 8 knots. This region is closer to the northern part of the country and the grass land vegetation is more rampant in this area, and so the buoyance of the wind in this region is expected to be higher. Figure 5: Wind Speed in south eastern Nigeria (2012-2021) 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 W in d S p ee d in K n o t Years Portharcourt Enugu Owerri Calabar Uyo mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 9 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Surprisingly, the wind speed in Calabar was lower than that of other regions even though it is a coastal town surrounded with a lot of water bodies. However, in this study our main aim is not to reveal with area has more wind speed or not, but to determine if there are departures from the norm and how such departures from the norms affects the aviation industry in the region. The visibility characteristics of the study area is displayed in figure 6. Port Harcourt appears to be having better visibility than the other areas in the study area. Enugu is very poor with annual visibility that range from 5km to 10km. Calabar also presented a very weak visibility although it seems to have improved since 2019. The main reasons for the behavior of visibility in the study area is that to the north section of the region, there is the interference of the highlands and the intercontinental airmass which comes in from the north bearing dust and hazardous weather. To the Calabar section, the highlands of the Cameroons plays serious roles in the modification of the weather and visibility inclusive. Figure 6: Visibility characteristics in Eastern Nigeria (2012-2021) 3.2 The changes in the sudden and slow weather events of south eastern Nigeria (2012-2021) The annual extreme weather events between 2012 and 2021 is presented in Table 1. Rainfall data revealed a slight variation in occurrences of rainfall amongst the cities sampled. Port Harcourt, Calabar and Uyo recorded higher rainfall in 2012 with 28, 25 and 26 occurrences. This significant variation from Enugu (17 times) and Owerri (12 times) is attributable to the sub equatorial climate in the South South region of Nigeria. While the case of Enugu is adduced to the character of the hinterland climate, that of Owerri present evidence for critical investigation. There is evidence of lack of uniformity in rainfall occurrences and increase in 2016, but the most recent increase is recorded in Port Harcourt with 39 to times in 2016. What is very conspicuous is that Port Harcourt, Calabar and Uyo had high decadal mean rainfall with 227, 215 and 180 respectively. 0.0 5.0 10.0 15.0 20.0 25.0 30.0 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 V is si b ili ty in K m Years Portharcourt Enugu Owerri Calabar Uyo mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 10 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Table 1: Annual extreme weather (sudden) events in south-east Nigeria 2012-2021 Rainfall Locations 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Decadal Mean Port Harcourt 28 34 12 29 39 14 12 19 23 17 227 22.7 Enugu 17 19 11 16 18 14 9 15 12 10 141 14.1 Owerri 12 11 13 4 1 16 15 16 12 4 104 10.4 Calabar 25 23 22 14 22 15 18 31 11 34 215 21.5 Uyo 26 21 17 8 15 19 14 17 25 18 180 18 Cloud Oktas Port Harcourt 6 4 1 2 3 5 6 7 1 5 40 4 Enugu 4 2 5 1 5 6 1 3 5 5 37 3.7 Owerri 1 2 3 2 3 6 2 1 4 5 29 2.9 Calabar 3 2 1 1 1 2 3 2 1 3 19 1.9 Uyo 2 1 2 3 4 1 4 5 4 2 28 2.8 Visibility Port Harcourt 18.0 21.0 23.0 15.0 33.0 15.0 14.0 14.0 3.0 14.0 170 17.0 Enugu 45 39 38 47 32 28 34 32 8 22 325 32.5 Owerri 23 23 14 25 11 13 15 21 4 22 171 17.1 Calabar 3 1 5 3 6 7 2 6 1 12 46 4.6 Uyo 2 13 1 3 4 5 6 3 1 7 45 4.5 The obvious implication is that more flight delays caused by obscured weather conditions would be recorded in those three cities with spiraling effect in Enugu and Owerri. Table 1 also present annual and decadal cloud cover between 2012 and 2021. Remarkably, only Port Harcourt recorded 7 oktas in 2019 which is not full cloud and not sufficient to obscure visibility. There is prevalence of 1 oktas and mean decadal cloud cover of less than 3.0 given that the city with the highest decadal cloud cover is Uyo with 2.8 oktas. It can be generalized from the foregoing that airline operation in the decade under investigation was not significantly disrupted by poor visibility and cloud cover. In Table 2, the extreme monthly weather events for 2012-2021 is shown. Port Harcourt recorded a remarkable exponential progression in rainfall occurrences from June to September with 52 and June 12 times respectively. The case of Port Harcourt is similar to Uyo and Calabar. However, all the sampled cities recorded a blip between mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 11 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET November and March. This is characteristic of the two seasons in Nigeria which are largely determined by the tropical continental and tropical maritime air masses. Only Port Harcourt recorded a full cloud in June with 8 and August with 9. Table 2: Monthly extreme weather (sudden) events in south-east Nigeria 2012-2021 Rainfall Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Port Harcourt 2 3 4 23 15 49 41 52 31 6 1 0 Enugu 4 7 9 12 9 26 23 26 19 5 1 0 Owerri 0 2 4 6 7 20 18 21 18 6 2 0 Calabar 5 7 9 11 9 41 33 38 31 23 5 3 Uyo 2 4 5 7 4 29 33 35 31 22 5 3 Cloud OKtas Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Port Harcourt 1 2 3 3 2 8 6 9 3 2 1 0 Enugu 0 1 3 1 6 6 6 5 6 3 0 0 Owerri 1 2 3 3 4 5 2 4 1 4 0 0 Calabar 0 2 1 3 1 2 3 2 2 3 0 0 Uyo 0 1 2 3 4 1 4 5 4 4 0 0 Visibility Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Port Harcourt 18 21 2 1 3 14 13 17 15 14 31 21 Enugu 45 19 14 11 14 12 10 32 34 38 52 44 Owerri 23 3 4 1 2 15 18 14 13 21 35 22 Calabar 1 0 1 0 4 1 1 1 0 3 19 15 Uyo 1 2 1 3 1 4 1 3 2 3 9 15 The implication of having a full cloud is that the sky was obscured in the two months which could cause disruption in flight operations. Enugu, Owerri, Calabar and Uyo recorded zero cloud cover in November and December which is highly supportive of flight operations. 3.3 Effects of sudden-slow weather events on airports operations in South Eastern Nigeria (2012-2021) Figure 7 presents effects of sudden slow weather events on airports operations. Flight cancellation and delay are the dominant reactionary measures to slow weather events with 700 and 800 cases respectively. The data in figure 7 also demonstrated diversion of flight as a measure to surmount slow weather events. But this will have very little effect on passengers’ satisfaction. mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 12 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Figure 7: Effects of sudden-slow weather events on airports operations in South Eastern Nigeria (2012- 2021) 3. 4: Sudden-slow weather events that affects aviation most in the south eastern Nigeria (2012-2021) Table 3 present sudden weather events with more impacts on flight operations. The table reflects that cities in the sub equatorial climate recorded more occurrences of rainfall cumulatively. This is linked in extant literature to the tropical maritime air mass that causes rainy climate. But Owerri, though in the South East, recorded 104 rainfall occurrences, which is less than Enugu that is characteristic of hinterland climate. The case of Owerri can be adduced to the weather dynamics associated with the elevation of the state. Table 3: Sudden-slow weather events that affects aviation most in the south eastern Nigeria (2012-2021) Locations Rainfall Cloud Visibility Port Harcourt 227 40 170 Enugu 141 37 325 Owerri 104 29 171 Calabar 215 19 46 Uyo 180 28 45 Furthermore, the sudden-slow weather events that affects aviation most in the south eastern Nigeria (2012-2021) reflected in table 4.3 showed that Port Harcourt, Uyo and Calabar had more rainfall events that have affected aviation operations in the region. But the numbers of events in Owerri and Enugu is sufficient to disrupt flight operations. The figure show that visibility is not in sync with data on rainfall events. While Enugu and Owerri had less occurrences of rainfall, the two cities had more occurrences of when visibility disrupted flight operations, thus evident that poor visibility in the two cities is not a function of rainfall. Other climatic events could be responsible 0 100 200 300 400 500 600 700 800 900 cancellation divertion delay N u m b er o f ca se s in 1 0 0 s Effects mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 13 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET for poor visibility. All the cities had cases of cloud effects on flight operations but Port Harcourt, Uyo and Owerri had more effects on cloud, this is because only the three cities had more oktas. 3.4: Passengers’ perception of the effects of sudden or slow weather events on aviation (2012-2021) The perception of the passengers regarding the effects sudden slow weather events on the aviation industry was also considered and these passengers comprised of 72.3% of males and 27.2% females (see Table 4). Table 4 shows that more males (72.5%) boarded flight than females (27.7%) within the period under investigation. 19.8% of the passengers are less than 40 years of age while 36.2% are between 20 to 30 years. This is consistent with more volume of air travels for the economically active population in Nigeria that utilizes air travel for work, education, healthcare care, tourism, recreation and business. Table 4: Demographic characteristics of the passengers Sex Frequency Percentages (%) Male 289 72.3 Female 111 27.7 Total 400 100 Age Frequency Percentages (%) 20-30 145 36.2 31-40 176 44 >40 79 19.8 Total 400 100 Education status Frequency Percentages (%) Basic 1-9 27 6.8 SSCE 104 26 Tertiary education 269 67.2 Total 400 100 Frequency of air travel Frequency Percentages (%) Weekly 156 39 Every fortnight 121 30.3 Monthly 89 22.2 > Monthly 34 8.5 Total 400 100 Data also showed that 67.2% of the passengers have tertiary education and 26% have SSCE. Only 6.8% possess basic education which is also consistent with studies that air travel is elitist. Frequency of air travel is recorded more in fortnight with 30.3% and weekly with 39%. This shows a high volume of the utility of air traffic in Nigeria. mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 14 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Table 5 indicates that a good number of the passengers are aware of the effects of weather on aviation. High level of awareness can be adduced to high literacy rate of passengers. However, 5.3% of the passengers are skeptical. Table 5: Effects of sudden or slow weather events on aviation Awareness of effects of weather on aviation Option Frequency Percentage 1 Yes 379 94.7 2 No 00 00 3 Undecided 21 5.3 Weather element that has affected your flight Option Frequency Percentage 1 Temperature 00 00 2 Rainfall 103 25.8 3 Cloudiness 82 20.5 4 Visibility 192 48 5 Wind 00 00 6 Pressure 23 5.7 Total 400 100 Perceived effects of sudden slow weather events on aviation operations Option Frequency Percentage 1 Flight delays 193 48.3 2 Flight cancellations 134 33.5 3 Flight diversion 55 13.8 4 Near crash 18 4.5 Total 400 100 Perceived economic loss from sudden-slow weather events Option Frequency Percentage 1 Financial loss 178 44.5 2 Appointment cancelations 143 35.7 3 Loss of opportunities 79 19.8 Total 400 100 In Table 5, passengers affirmed that visibility, rainfall, and cloudiness are more impactful on flight operations with 48%, 25.8% and 20.5% respectively. Though they did not see temperature and wind as impactful. 5.7% of the mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 15 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET passengers recognized pressure as a cause of air travel disruption. It is also clearly revealed in Table 5 that the main effects of sudden slow weather events on flights and aviation operation in the study area are flight delays (48.3%) and flight cancellations (33.5%). Other challenges of sudden slow weather events are flight diversion (13.8%) and near crash (4.5%). Overall, there appears to be a significant effect of sudden slow weather events on aviation operations in the south eastern part of Nigeria. The economic losses from sudden slow weather events are multidimensional. Passengers recognized financial losses, cancellation of appointments and loss of opportunities as major offshoots of sudden slow weather events. However, financial losses is more prevalent with 44.5%. Table 6A: ANOVA table showing the spatial difference in the effects of sudden weather events (rainfall) on aviation in the south east of Nigeria ANOVA Rainfall Sum of Squares Df Mean Square F Sig. Between Groups 8761.000 4 2190.250 12.684 .000 Within Groups 102747.500 595 172.685 Total 111508.500 599 Table 6B: Duncan statistics showing the spatial difference in the effects of sudden weather events (rainfall) on aviation in the south east of Nigeria Rainfall Duncan Identifiers N Subset for alpha = 0.05 1 2 3 4 Owerri 120 8.6667 Enugu 120 11.7500 11.7500 Uyo 120 15.0000 15.0000 Calabar 120 17.9167 17.9167 Port Harcourt 120 18.9167 Sig. .070 .056 .086 .556 Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = 120.000. Analysis of variance of the spatial variation in the effects of sudden weather events in rainfall for sampled cities in Table 4A (Port Harcourt, Uyo, Enugu, Owerri and Calabar) is significant at p<0.05 level, F 12.684, sig 0.000. Since the significant level is below 0.05(p value). It indicates that there is a statistically significant difference amongst the five states. Duncan statistics in Table 4B was further computed to decipher where the variation in rainfall is conspicuous. The analysis revealed that Owerri (M= 8.667), Sig 0.070) is significantly varied from Uyo (M= 15.000 Sig 0.86) and varied from Port Harcourt (M= 18.9167, Sig 0.556). mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 16 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Table 6C: ANOVA table showing the spatial difference in the effects of sudden weather events (Cloud-Oktas) on aviation in the south east of Nigeria ANOVA Cloud_Oktas Sum of Squares df Mean Square F Sig. Between Groups 227.667 4 56.917 13.651 .000 Within Groups 2480.833 595 4.169 Total 2708.500 599 Table 6D: Duncan statistics showing the spatial difference in the effects of sudden weather events (cloud-Oktas) on aviation in the south east of Nigeria Cloud-Oktas Duncan Identifiers N Subset for alpha = 0.05 1 2 3 Calabar 120 1.5833 Uyo 120 2.3333 Owerri 120 2.4167 Enugu 120 3.0833 Port Harcourt 120 3.3333 Sig. 1.000 .752 .343 Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = 120.000. Analysis of variance of the spatial variation in the effects of sudden weather events in cloud -oktas for sampled cities (Port Harcourt, Uyo, Enugu, Owerri and Calabar) is presented in Table 4C and it is significant at p<0.05 level, F 13.651 sig 0.000. Since the significant level is below 0.05(p value). It indicates that there is a statistically significant difference amongst the five states. Duncan statistics was further computed to decipher where the variation in cloud oktas is conspicuous. Table 4D revealed that Calabar (M= 1.5833), Sig 1.000) is significantly varied from Uyo (M= 2.333 Sig 0.752) and varied from Port Harcourt (M= 3.333, Sig 0.343) Table 6E: ANOVA table showing the spatial difference in the effects of sudden weather events (Visibility) on aviation in the south east of Nigeria ANOVA Visibility Sum of Squares df Mean Square F Sig. Between Groups 44414.333 4 11103.583 125.345 .000 Within Groups 52707.500 595 88.584 Total 97121.833 599 mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 17 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Table 6F: Duncan Statistics showing the spatial difference in the effects of sudden weather events (Visibility) on aviation in the south east of Nigeria Visibility Duncana Identifiers N Subset for alpha = 0.05 1 2 3 Uyo 120 3.7500 Calabar 120 3.8333 Port Harcourt 120 14.1667 Owerri 120 14.2500 Enugu 120 27.0833 Sig. .945 .945 1.000 Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = 120.000. Analysis of variance of the spatial variation in the effects of sudden weather events in visibility for sampled cities (Port Harcourt, Uyo, Enugu, Owerri and Calabar) is presented in Table 4E and it is significant at p<0.05 level, F 125.345 sig 0.000. Since the significant level is below 0.05 (p value). It indicates that there is a statistically significant difference amongst the five states. Duncan statistics was further computed to decipher where the variation in visibility is conspicuous. Table 4F revealed that Uyo (M= 3.7500, sig 0.945) is significantly varied from Port Harcourt (M= 14.1667 Sig 0.45) and varied from Enugu (M= 27.0883, Sig 1.000) 4: DISCUSSION OF RESULTS The critical characteristics of sudden and slow weather events over the years in South Eastern Nigeria were evaluated. The character of weather and climate showed changes in frequency, intensity, blip, spatial coverage, number of occurrences and timing. Extremity of rainfall and cloud cover in Port Harcourt and Uyo can be linked to their geographic location and dominance of the south west trade wind. The implication is that the tropical maritime gives more potency for cloud and rainfall to reduce visibility, and affect flight operations given the ubiquity of thunderstorms. Enugu had higher wind speed, but its closeness to the northern part of the country would mean dominance of the tropical continental air-mass. Rainfall, temperature, cloud cover, wind and cloud cover showed remarkable variation annually and monthly. Rainfall showed lack of consistency in most of the years, but 2013 had a similar pattern, temperature ranged between 30 ⁰C to 35⁰C with Enugu having the highest temperature maximum, and this is consistent with the dynamics of the various climate belts in Nigeria. These findings are consistent with those of Samson & Sumi, (2019). This study has identified the slow and sudden weather events characteristics in the South East. What was very conspicuous is the spatiality of weather characteristics and similarities amongst Port Harcourt, Uyo and Calabar. They showed near similarities in rainfall, cloudiness and visibility which is consistent with the character of the sub equatorial climate. However, this finding is not in tandem with the finding of Ogunrinde et al., (2023). The weather characteristics of Enugu appeared mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 18 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET different and similar to that of the tropical hinterland climate, and have higher wind speed. However, all the sampled cities demonstrated that cloudiness is not very impactful on visibility. The study observed changes in weather characteristics in the decade under investigation, one deviation from the norm is that Calabar which is a coastal town has lower wind speed than other cities, and recorded the most recent increase occurrences in rainfall of 34 evets in 2016. However, studies have shown that most of the disruption, delays and cancellations of flight are avoidable if human errors and negligence are tackled (Borsky & Unterberger, 2019). The changes in the sudden and slow weather and climate events in the south eastern Nigeria is consistent with the environmental changes. But empirical evidences have shown that climate change is one of the defining contemporary issues that have been recognized as a major driver of change in the period under investigation. All the cities sampled showed remarkable differences in changes in precipitation, temperature, cloud cover and visibility. The wind in Enugu which shares the characteristics of the tropical hinterland climate had more speed than other cities but with marked variation across the years. Port Harcourt recorded remarkable shift with double maxima of high rainfall events in 2013 (34 occurrences) and 2016 (39 occurrences). Only 12 and 12 rainfall events recorded in port Harcourt in 2014 and 2018 are the only similarities, but the difference between rainfall events of 12 which is the lowest and that of 2016 (39 events which is the highest is significant. The case of Owerri where rainfall was recorded just ounces in 2016, 4 times 2015 and 2021 represent concrete evidence of climate. The only city that had one level of consistency in rainfall is Uyo, but there is abrupt reduction in 2018 with only 8 events. These findings are in line with those of Eludoyin & Akinbode (2009), although Ugochukwu, Emmanuel, & Lekia, (2023) reporter otherwise. Adverse weather and climate events are external factors that determines the operations of airports operations globally, but there is perceived over generalization of the climatic parameters that causes more disruptions in various climate regions. This study showed concrete evidence of slow and sudden weather events and potential effects on flight operations in the south east, but there is spatial variation among the cities investigated. As also reported by Uchechukwu et al. (2018). The effects of the sudden and slow events are multidimensional, one the one hand, it increases the cost of operations, and create a negative public perception for airlines that do not have the required expertise to be proactive, and minimize the effects on passengers. On the other hands, sudden weather events cause delays, flight cancelation and additional cost for travelers, it can also lead to fatalities. In most cases, delays occur when there is no accurate forecast of imminent sudden weather events, and when communication with passengers is poor. It has been established in the literature that weather events are not always controllable (Oriola, 2014), however, the onus is in airlines to minimize cost and losses of productive hours for passengers. Occurrences of heavy rainfall and strong wind are sudden, and occur at specific time of the day, but causes shift in disruption in logistics planning with economic cost for operators and passengers, but data on amount of departure delays in various airline companies is not availability for research (Balogun & Odjugo, 2020). The constancy of the effects of rainfall and cloud cover on visibility and airline operations in the decade under review is attributable to the dynamics in the sub equatorial and hinterland climate. Similar observation has been made by Borsky & Unterberger, (2019), they however, averred that being acquainted with the weather and mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 19 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET climate characteristics of the locale is key to ameliorating the sudden and slow weather events effects of aviation operations. Passengers are largely aware of the effects of weather events on air travels. Over the years, financial losses, flight delays and cancelation and loss of opportunities have been recorded by passengers. But there is still no policy to compensate for poor service. Evidently, flight travels are still largely patronized by the elites as there seem to be a correlation between literacy and the utility of air transport. Only few passengers with basic education utilizes air transport in the sampled cities and they are obviously the respondents that are not aware of the effects of weather events on air travels (Adefolalu, 2007). Annual and decadal rainfall revealed steady increase from June to August and slight decrease from October to March. There is positive statistical relationship and variation in weather characteristics which also manifest in flight delays, cancellation and diversion. This is consistent with Mande (2019) where he reported that 71% of air travel disruption in Nigeria are due to the poor weather conditions, with the inclusion of human errors, aging aircraft and deficiency in safety management system. This case of full cloud in Port Harcourt also aligns with Mande (2019) where he reported flight disruption in the city with reduced horizontal visibility to between 200 to 800 for several days leading to spiraling flight disruption across the country. 5. CONCLUSION AND RECOMMENDATIONS It is obvious as captured in several studies in the literature that knowledge on weather phenomena is very important for flight operations while others revealed that adverse weather conditions may cause delays, diversions, accidents and even outright cancellations of journeys, causing loss of time, revenue and even lives. In addition, available reports maintained that in aviation, weather has remained the important parameter in determining safety, regularity and efficiency of aviation operations. The plethora of studies in the literature that interrogate the effects of weather parameters on aviation have obviously failed to differentiate between slow and sudden weather events. Thus, there is perceived wrongful generalization and errors in responses to weather related problems in the aviation industry. This study has established the difference between slow and sudden weather events and disparity in weather characteristics amongst cities in the south east. It revealed that there are variations in cloudiness, rainfall and visibility amongst the cities and given the interconnectedness of aviation infrastructure and operations with weather parameters, disruption in one city leads to chain disruption in other cities resulting in flight disruptions. Noting that the economic cost of flight cancelation, diversion and delays are not quantified and duly compensated, the need to device various methods to address the peculiarities of problems in various cities are necessary. On the premise of the above, the following recommendations are made: 1. improvement in the expertise of aviation staff through training and retraining to keep pace with effects of weather variability and change in airline operations. 2. migrating from analog data collection methods to automated instruments to engender real time and accurate data collection for planning due to the dynamic nature of atmospheric parameters. 3. enactment and enforcement of stringent regulations on airline companies to engender global best practices. 4. replacement of aging aircraft and weather monitoring facilities. mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 20 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET 5. improvement in maintenance culture. 6. synergy between all airport services providers is needed to activate proactive planning, anticipate climate change and design appropriate resilience and adaptation plan.as well measures to tackle flight disruption, damage of airport infrastructure given the progression of rainfall and changing wind. 6. REFERENCES Abdelghany, A., Guzhva, V. S., & Abdelghany, K. (2023). The limitation of machine-learning based models in predicting airline flight block time. Journal of Air Transport Management, 107, 102339. Adefolalu, D. O. (2007). Climate change and economic sustainability in Nigeria. In International Conference on Climate Change and Economic Sustainability held at Nnamdi Azikiwe University, Enugu, Nigeria (pp. 12-14). Ahmed, Z., Le, H. P., & Shahzad, S. J. H. (2022). Toward environmental sustainability: how do urbanization, economic growth, and industrialization affect biocapacity in Brazil?. Environment, Development and Sustainability, 24(10), 11676-11696. Akpootu, D. O., Iliyasu, M. I., Mustapha, W., Salifu, S. I., Sulu, H. T., Arewa, S. P., & Abubakar, M. B. (2019). Models for estimating precipitable water vapour and variation of dew point temperature with other parameters at Owerri, South Eastern, Nigeria. Journal of Water Resources and Ocean Science, 8(3), 28- 36. Ayoade, J. O. (2004). Introduction to climatology for the Tropics Ibadan. Spectrum Book. Balogun, V. S., & Odjugo, P. A. O. (2020). Gender-Based Assessment of Climate Chang e Adaptation Strategies in Delta State, Nigeria. Journal of Geographic Thought & Environmental Studies, 15(2), 94-110. Bertness, J. (1980). Rain-related impacts on selected transportation activities and utility services in the Chicago area. Journal of Applied Meteorology and Climatology, 19(5), 545-556. Borsky, S., & Unterberger, C. (2019). Bad weather and flight delays: The impact of sudden and slow onset weather events. Economics of transportation, 18, 10-26. Chen, Z., & Wang, Y. (2019). Impacts of severe weather events on high-speed rail and aviation delays. Transportation research part D: transport and environment, 69, 168-183. mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 21 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Dalal, S., Seth, B., Radulescu, M., Cilan, T. F., & Serbanescu, L. (2023). Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety. Sustainability, 15(7), 6070. Dalmau, R., & Gawinowski, G. (2023). Learning With Confidence the Likelihood of Flight Diversion Due to Adverse Weather at Destination. IEEE Transactions on Intelligent Transportation Systems. Dempsey, P. S. (2002). Aviation security: the role of law in the war against terrorism. Colum. J. Transnat'l L., 41, 649. Deng, X., Wang, L., Gui, J., Jiang, P., Chen, X., Zeng, F., & Wan, S. (2023). A review of 6G autonomous intelligent transportation systems: Mechanisms, applications and challenges. Journal of Systems Architecture, 102929. Diamond, S. (2017). In search of the primitive: A critique of civilization. Taylor & Francis. Dönmez, K. (2023). Aircraft sequencing under the uncertainty of the runway occupancy times of arrivals during the backtrack procedure. The Aeronautical Journal, 127(1310), 562-580. Eludoyin, A. O., & Akinbode, O. M. (2009). Millennium development goal on water supply and the challenges of headwaters quality in Nigeria: example from a small community in Southwest Nigeria. Journal of Sustainable Development in Africa, 11(2), 178-93. Ezemonye, M. N., & Emeribe, C. N. (2012). Rainfall erosivity in southeastern Nigeria. Ethiopian Journal of Environmental Studies and Management, 5(2), 112-122. Glass, C., Davis, L., & Watkins-Lewis, K. (2022). A visualization and optimization of the impact of a severe weather disruption to an air transportation network. Computers & Industrial Engineering, 168, 107978. Gnutzmann, H., & Śpiewanowski, P. (2023). Can consumer rights improve on-time performance? Evidence from European Air Passenger Rights. Transport Policy, 136, 155-168. Hutter, F. G., & Pfennig, A. (2023). Reduction in ground times in passenger air transport: A first approach to evaluate mechanisms and challenges. Applied Sciences, 13(3), 1380. Isagba, E. S., Izinyon, O. C., Emeribe, C. N., Uwadia, N. O., & Ezeh, C. U. (2021). Estimation of stream discharge of ungauged basins using NRCS-CN and remote sensing methods: A case study of Okhuwan mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 22 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET and Okhaihe catchments, Benin City, Edo State, Nigeria. International Journal of Water Resources and Environmental Engineering, 13(3), 165-183. Kumar, P., & Khani, A. (2023). Schedule-based transit assignment with online bus arrival information. Transportation Research Part C: Emerging Technologies, 155, 104282. Li, Q., Jing, R., & Dong, Z. S. (2023). Flight Delay Prediction with Priority Information of Weather and Non- Weather Features. IEEE Transactions on Intelligent Transportation Systems. Lin, M. H. (2023). Airport congestion pricing and capacity: cost recovery with per-flight or per-passenger charges. Available at SSRN 4494785. Mande, K. H. (2019). Effect of climate change on airline flights operations at Nnamdi Azikiwe International Airport Abuja, Nigeria. Science World Journal, 14(2), 33-41. Montlaur, A., Delgado, L., & Prats, X. (2023). Domain-driven multiple-criteria decision-making for flight crew decision support tool. Journal of Air Transport Management, 112, 102463. Ogunrinde, A. T., Oguntunde, P. G., Akinwumiju, A. S., Fasinmirin, J. T., Adawa, I. S., & Ajayi, T. A. (2023). Effects of climate change and drought attributes in Nigeria based on RCP 8.5 climate scenario. Physics and Chemistry of the Earth, Parts A/B/C, 129, 103339. Okereke, C. A., & Emeribeole, A. C. (2020). Soil erosion as an emerging environmental challenges in South- East geopolitical zone of Nigeria: Towards a sustainable land conservation for national development. African Journal of Environment and Natural Science Research, 3(1), Oriola, A. O. (2014). System dynamics modelling of waste management system. In Proceedings of 1st Asian- Pacific System Dynamics Conference (pp. 22-24). Porter, E. (2023). A People's History of SFO: The Making of the Bay Area and an Airport. Univ of California Press. Reitmann, S., & Schultz, M. (2022). An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-) Systems with Machine Learning. Aerospace, 9(2), 77. Ren, J. (2023). Financial conditions and incumbent quality responses to entry: Evidence from airlines' on-time performance. Journal of Air Transport Management, 107, 102352. mailto:topacademicjournals@gmail.com Academic Journal of Science, Engineering and Technology Vol. 10, Issue 5; September-October 2025; ISSN: 2837-2964 Impact Factor: 7.67 1252 Columbia Rd NW, Washington DC, United States https://topjournals.org/index.php/AJSET; mail: topacademicjournals@gmail.com 23 | A c a d e m i c J o u r n a l o f S c i e n c e , E n g i n e e r i n g a n d T e c h n o l o g y | https://topjournals.org/index.php/AJSET Samson, B. P. V., & Sumi, Y. (2019). Exploring factors that influence connected drivers to (not) use or follow recommended optimal routes. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1-14). Sytch, M., Kim, Y., & Page, S. (2022). Supplier-selection practices for robust global supply chain networks: a simulation of the global auto industry. California Management Review, 64(2), 119-142. Uchechukwu, N. B., Agunwamba, J. C., Tenebe, I. T., & Bamigboye, G. O. (2018). Geography of Udi Cuesta contribution to hydro-meteorological pattern of the South Eastern Nigeria. Engineering and Mathematical Topics in Rainfall. Ugochukwu, A., Emmanuel, B. O., & Lekia, N. (2023). Bivariate Time Series Analysis of Rainfall Pattern in Selected States in Nigeria. Journal of Mathematical Sciences & Computational Mathematics, 4(4), 442- 461. Voskaki, A., Budd, T., & Mason, K. (2023). The impact of climate hazards to airport systems: a synthesis of the implications and risk mitigation trends. Transport Reviews, 1-24. Wang, X., Jin, J. G., & Sun, L. (2022). Real-time dispatching of operating buses during unplanned disruptions to urban rail transit system. Transportation Research Part C: Emerging Technologies, 139, 103696. mailto:topacademicjournals@gmail.com