Urbanisation and industrialization are predominant indicators of regional growth with some adverse effect especially in ambient air quality often prone to contamination by emissions. Vehicular emissions have been identified as a consistent air pollutant in urban areas. However, meteorological conditions such as rainfall also affect air pollution concentration level. The aim was to identify relationships between a meteorological factor like rainfall, vehicular load and atmospheric pollutant concentration in Effurun City, Delta State. In-situ sampling of CO, VOC and NO2 and Geostatistical analysis were used to obtain concentration level and relationships between the selected variables which was used to predict spatial trend for efficient monitoring. From our results, it was observed that Iterigbi, Ages Gas and Okuokoko Junctions had the highest concentration of VOC, NO2 and CO respectively. At residential areas, Iterigbi had the highest concentration of VOC and NO2 while Okuokoko had the highest concentration of CO.

Urbanisation and industrialization are predominant indicators of regional growth with some adverse effect especially in ambient air quality often prone to contamination by emissions. Vehicular emissions have been identified as a consistent air pollutant in urban areas. However, meteorological conditions such as rainfall also affect air pollution concentration level. The aim was to identify relationships between a meteorological factor like rainfall, vehicular load and atmospheric pollutant concentration in Effurun City, Delta State. In-situ sampling of CO, VOC and NO2 and Geostatistical analysis were used to obtain concentration level and relationships between the selected variables which was used to predict spatial trend for efficient monitoring. From our results, it was observed that Iterigbi, Ages Gas and Okuokoko Junctions had the highest concentration of VOC, NO2 and CO respectively. At residential areas, Iterigbi had the highest concentration of VOC and NO2 while Okuokoko had the highest concentration of CO.

Modeling air pollution concentration is a key aspect of environmental quality assessment and ecological resources management. In-situ measurements of three pollutants VOC, CO, and NO2 mainly determined by a combination of climatic variables and vehicle load were collected during field campaigns in three phases. Also, meteorological data (temperature, wind speed, and precipitation rate) was collected from an online weather forecasting platform. Results from the mean values computed and graphs plotted showed Five junction which has the highest concentration of vehicular movement (50 vehicles every two minutes) and human activities have the highest mean concentration of 980 ppm and a standard error of 40.60 ppm for VOC and 11.04 ppm for CO and a standard error value of 0.41 ppm, NO2 concentration of 0.35 ppm. Express junction which has the next highest vehicular concentration (47 vehicles every 2 minutes) has the next ranked mean concentration of 713.91 ppm and a standard error value of 59.56 ppm, CO value of 8.05 ppm, and NO2 value of 1.88 ppm. Ekwere road along was the next in pollution concentration ranking for VOC with 524.18 ppm and a standard error value of 20.64 ppm. it also had a high CO mean concentration of 9.20 and a standard error value of 0.69 ppm. which was higher than the express junction. Ughughelli road had a VOC mean concentration of 426 ppm, a standard error value of 31.53 ppm, a CO value of 4.14 ppm, and a standard error of 0.32 ppm. CDI and Agofure park which has similar vehicular loads had values of VOC at 323.05 ppm and 327.08 ppm respectively. They also had CO values of 4.03 ppm and 3.29 ppm. Ekwere less residential which has the lowest vehicular and human presence was the least polluted, with a VOC mean concentration of 193.42 ppm, CO mean concentration of 0.25 ppm, and NO2 concentration of 0.14 ppm.

This research work applied geospatial and weight of evidence approach to ecological risk assessment for quantifying environmental exposure to oil pollution in the Niger Delta. Spatial data for Pipelines, Oil spills and Land Cover were used to quantify the extent of Ecological resources exposed to oil pollution using a data-process model. Regional scale risk assessment was done using the combination of geospatial and statistical approaches. Hotspot and Proximity analysis were used for geospatial analysis while weight of evidence was adopted for statistical computation.   Ecological resources were identified from land cover maps and ranked according to their perceived importance. Hotspots of oil spill incidents were determined using spatial autocorrelation. Ecological resource vulnerability was determined using buffer zoning of 5 km and 10 km respectively as high and low risk zones, with sample maps made to show extents of resources at risk. Areal extent of ecological resources at risk were calculated and standardized for each of the delineated buffer zones. An aggregate of the weight of each ecological resources and area was computed to categorize the risk as either high, medium or low. This study has successfully assembled and produced relevant spatial and attribute data sets and applied integrated geostatistical analytical techniques to understand the distribution and impacts of oil spills in the Niger Delta. The procedure was seen as an alternative to existing management processes used for monitoring and management of oil spill events.