Mapping of Traffic-Related Air Pollution Using GIS Techniques in Ijebu-Ode, Nigeria

Spatial and temporal characteristics of traffic related air pollutants (CO, NO, NO2 and SO2) in Ijebu-ode, Nigeria were determined using replicate portable gas detectors (Land Duo Multi Gas Monitor) at selected road junctions, motor garages and markets. Mapping of different concentration of air pollutants was carried out using kriging type of interpolation method in GIS environment. Concentration of CO ranges from 4.8 ppm at Erinlu/Molipa Roundabout to 137ppm on Sagamu/Ore Expressway. Concentrations of NO2  range from 100-662 ppb with overall average value (OAV) of 299.8 ppb, while concentration of nitrogen oxide (NO) ranges between 67-302 ppb and OAV of 166.23 ppb. SO2 had concentration ranging between 38-245 ppb and an OAV of 139.07 ppb all of which are above standard ambient air quality standards. AQI indicated very unhealthy air quality in most areas which calls for the need to establish and strengthen the health-based standard for air pollutants

Air Quality Indexing, Mapping and Principal Components Analysis of Ambient Air Pollutants around Farm Settlements across Ogun State, Nigeria

The focus of this study was to portray the spatial pattern of air quality across seasons in the eight sampled farm settlements using air quality indexes and assess the clusters of monitored air pollutants. The concentrations of air pollutants were determined using in-situ portable gas detectors and particulate counter. The AQI for each criteria pollutants (CO, O3, TSP, PM10, SO2, and PM2.5) was calculated using AQI formulae of the United States Environmental Protection Agency and mapped using the Inverse distance weighting (IDW) interpolation method in the Geographic information systems (GIS) environment. Principal component analysis (PCA) was used to group the parameters and estimate the interrelationships between the loadings of the parameters in each component. The AQI ranges of pollutants which deviated from the acceptable good status are CO (71.98 – 238 and 88.85 – 220.93), NO2 (10.14 – 107.07 and 10.84 – 72.88) and PM2.5 (12.90 – 70.85 and 12.56 – 54.02) for the dry and wet seasons, respectively. There were five and four PCs with eigenvalues > 1, accounting for 69.75% and 61.73% of the total variance during the wet and dry season, respectively. The parameters in each component are as follows; PC1 - TSP, PM10, PM2.5, Bacteria and fungi; PC2 - CO and Temperature; PC3 - relative humidity and O3; PC4 - CO2; PC5 - NO2 and SO2 for the wet season and PC1 - TSP, PM10, PM2.5, Bacteria and fungi; PC2 - NH3 and NO2; PC3 - CO2 and O3; PC4 - Temperature and relative humidity during the dry season. Biomass burning, engine exhausts and fine-particulate related activities are sources of air pollution and such may pose negative implication to human health and environment. Therefore, the use of alternative biomass disposal, regular servicing of processing engines and the wearing of protective wears against dust are recommended

Spatial Pattern of Air Pollutant Concentrations and Their Relationship with Meteorological Parameters in Coastal Slum Settlements of Lagos, Southwestern Nigeria

This study assessed the spatial disposition of air pollutants and their relationship with meteorological parameters in urban slum settlements of Lagos city. The gaseous pollutants were quantified using a gas analyzer, and the PM2.5 concentration and meteorological parameters were determined using an Air Metric Sampler and Wind Mate, respectively. SPSS for Windows and ArcGIS were used for data analysis. The results revealed that the seasonal variations in SO2, NO2, CO2, and PM2.5 showed a higher level of air pollutant concentration during the dry season than during the wet season. During the wet season, a significant correlation was observed between PM2.5 and temperature at the 1% level (0.957 **), and VOC and SO2 (0.907 *) at the 5% level; during the dry season, significant correlations were observed between NO2 and SO2 at the 1% level (0.9477 **), and PM2.5 and relative humidity (0.832 *) at the 5% level. Atmospheric pressure (72%), temperature (60%), and relative humidity (98.4) were the primary meteorological factors affecting air pollutants such as VOC, CO2, and SO2. The spatial dispersal of air pollutants revealed a high Z score and a moderate p-value, indicating hot spot locations throughout the five selected slum settlements. It is recommended that regular monitoring based on quantifiable economic costs that are beneficial to the well-being of the populace be investigated, and policy-based initiatives for air pollution control based on scientific evidence be advocated for