No abstract

Ambient air pollution is a global concern. It accounts for an estimated 4.2 million deaths yearly as a result of its ability to cause stroke, lung cancer, heart disease and chronic respiratory diseases. It has also been estimated that about 91 percent of the world's population lives in places, mostly urban centres, where air quality levels exceed World Health Organization (WHO) limits. Fortunately, efforts are regularly made in most developed countries to monitor and reduce the level of air pollution and ameliorate its negative consequences. Unfortunately, the case is not the same in most developing countries of which Nigeria is a member, as air pollution status is rarely monitored religiously. This study geospatially assessed ambient air quality footprints vis-à -vis urban land uses in Calabar Metropolis, Nigeria. Data on emission level of CO, NO 2 , SO 2 , H 2 S, and SPM 2.5 were collected for 6 months in both dry and wet seasons in the year 2020, using Crowcon Gasman, while point coordinates were collected using Garmin GPSMap 60CSx device. Geographic Information Systems (GIS) infrastructure was deployed to generate the ambient air quality maps for the metropolis. Descriptive and parametric analytical techniques were also deployed, based on the objectives of the study. From findings, F-ratio is significant for both season and land use for all the tested parameters (F-ratio for season is F(1,3224)=574.516, at p<0.05, while for land use, F(3,3224)=429.946, at p<0.05). The interaction between seasons and land use (season * land use) for all the parameters is also significant. It was concluded that there is a significant variation in air quality (CO, NO 2, SO 2, H 2 S, and SPM 2.5 ) in Calabar Metropolis in relation to either land use types or seasons of the year. It was therefore recommended that there should be protection of residential land uses to avoid encroachment by incompatible uses that cause pollution.

Poor air quality is widely considered as one of the major environmental hazards confronting several urban centres worldwide. This study examined regional trend in ambient air quality footprints in Calabar Metropolis. Data on emission level of CO, NO 2 , SO 2 , H 2 S, and SPM 2.5 were collected using Crowcon Gasman, while point coordinates were collected using Garmin GPSMap 60CSx device. Interpolation algorithm in Geographic Information Systems infrastructure was used to generate the regional trend maps for the metropolis. Parametric analytical techniques such as Analysis of Variance (ANOVA) were employed to test the hypotheses, while descriptive statistics including tables, maps and standard deviation were also used to present the data based on the objectives of the study. The results of the trend surface analysis for the five (5) measured parameters show that CO and SPM 2.5 were not significant at P˃0.05 with F-ratio of 0.99 and 2.45 respectively. Thus, the null hypothesis which states that there is no significant change in the regional trend in air quality across Calabar Metropolis was therefore accepted. Analysis for NO 2 , SO 2 and H 2 S, were significant at P < 0.05 with F-ratio of 3.47, 3.35 and 7.79 respectively, causing the null hypothesis to be rejected. It was therefore recommended that mitigatory measures should be employed for the purpose of ensuring a sustainable, clean and green urban environment.

&lt;p&gt;Landslides have continued to wreck its havoc in many parts of the globe; comprehensive studies of landslide susceptibilities of many of these areas are either lacking or inadequate. Hence, this study was aimed at predicting landslide susceptibility in Cross River State of Nigeria, using machine learning. Precisely, the frequency ratio (FR) model was adopted in this study. In adopting this approach, a landslide inventory map was developed using 72 landslide locations identified during fieldwork combined with other relevant data sources. Using appropriate geostatistical analyst tools within a geographical information environment, the landslide locations were randomly divided into two parts in the ratio of 7:3 for the training and validation processes respectively. A total of 12 landslide causing factors, such as; elevation, slope, aspect, profile curvature, plan curvature, topographic position index, topographic wetness index, stream power index, land use/land cover, geology, distance to waterbody and distance to major roads, were selected and used in the spatial relationship analysis of the factors influencing landslide occurrences in the study area. FR model was then developed using the training sample of the landslide to investigate landslide susceptibility in Cross River State which was subsequently validated. It was found out that the distribution of landslides in Cross River State of Nigeria was largely controlled by a combined effect of geo-environmental factors such as elevation of 250 &amp;#8211; 500m, slope gradient of &gt;35&lt;sup&gt;o&lt;/sup&gt;, slopes facing the southwest direction, decreasing degree of both positive and negative curvatures, increasing values of topographic position index, fragile sands, sparse vegetation, especially in settlement and bare surfaces areas, distance to waterbody and major road of &lt; 500m. About 46% of the mapped area was found to be at landslide susceptibility risk zones, ranging from moderate &amp;#8211; very high levels. The susceptibility model was validated with 90.90% accuracy. This study has shown a comprehensive investigation of landslide susceptibility in Cross River State which will be useful in land use planning and mitigation measures against landslide induced vulnerability in the study area including extrapolation of the findings to proffer solutions to other areas with similar environmental conditions. This is a novel use of a machine learning technique in hazard susceptibility mapping.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; Landslide; Landslide Susceptibility mapping; Cross River State, Nigeria; Frequency ratio, Machine learning&lt;/p&gt;