Modelling of the potential pattern and concentration of fugitive dust around a cement plant


  • Olanrewaju Lawal University of Port Harcourt
  • Aminanyanaba Onari Asimiea



fugitive dust, Gaussian plume model, Geostatistical analysis, Obajana Cement plant, Empirical Bayesian Kriging


Fugitive dust (FD) contribute to air pollution by its contribution to particulate matter (PM) in the air. FD comes from various sources one of which is cement production. With this understanding it is important to know the concentration of FD in the environment. Obajana Cement Plant is one of the recent cement production outfits in Nigeria. With many parts of the country having no air quality monitoring infrastructure, it is thus important to use advances in modelling to understand the potential areas of impact of the plant. Using Gaussian plume modelling in combination with geostatistical technique, this study examined potential distribution FD, exposure index and population at risk to PM exposure. With the two major prevailing atmospheric stability condition maximums of 59µg/m3 and 78µg/m3 were obtained in June/July and December/January respectively. Using Empirical Bayesian Kriging (EBK), most places across the state have concentrations between 20 µg/m3 and 38µg/m3 for July/June condition and between 33µg/m3 and 50µg/m3 in the December/January condition. The probability of exceeding 15µg/m3 computation showed that more than 98% of the area have >60% chance of exceeding this threshold over the two periods. The average exposure index shows that about 23% of the population are exposed to concentrations below 25µg/m3 while about 28% are exposed to concentration about 35µg/m3. There is a clear indication that there is need to review the threshold of 250µg/m3 set by the regulatory agencies as modelling with values less than this still create a significant amount of FD impacting across vast areas of the State.


Author Biography

Olanrewaju Lawal, University of Port Harcourt

Olanrewaju Lawal is a Lecturer in the Department of Geography and Environmental Management. He has background in geo-information science, spatial and environmental modelling and geo-computation. He has interest in linking social sciences and natural sciences with the use of GIS and computational techniques.