Control of ammonia and urea emissions from urea manufacturing facilities of Petrochemical Industries Company (PIC), Kuwait.

UNLABELLED: Petrochemical Industries Company (PIC) in Kuwait has mitigated the pollution problem of ammonia and urea dust by replacing the melting and prilling units of finished-product urea prills with an environmentally friendly granulation process. PIC has financed a research project conducted by the Coastal and Air Pollution Program's research staff at the Kuwait Institute for Scientific Research to assess the impact of pollution control strategies implemented to maintain a healthy productive environment in and around the manufacturing premises. The project was completed in three phases: the first phase included the pollution monitoring of the melting and prilling units in full operation, the second phase covered the complete shutdown period where production was halted completely and granulation units were installed, and the last phase encompassed the current modified status with granulation units in full operation. There was substantial decrease in ammonia emissions, about 72%, and a 52.7% decrease in urea emissions with the present upgrading of old melting and prilling units to a state-of-the-art technology "granulation process" for a final finished product. The other pollutants, sulfur dioxide (SO2), nitrogen oxides (NOx), and volatile organic compounds (VOCs), have not shown any significant change, as the present modification has not affected the sources of these pollutants. IMPLICATIONS: Petrochemical Industries Company (PIC) in Kuwait has ammonia urea industries, and there were complaints about ammonia and urea dust pollution. PIC has resolved this problem by replacing "melting and prilling unit" of final product urea prills by more environmentally friendly "granulation unit." Environmental Pollution and Climate Program has been assigned the duty of assessing the outcome of this change and how that influenced ammonia and urea dust emissions from the urea manufacturing plant.

A heuristic optimization approach for Air Quality Monitoring Network design with the simultaneous consideration of multiple pollutants.

An interactive optimization methodology for allocating the number and configuration of an Air Quality Monitoring Network (AQMN) in a vast area to identify the impact of multiple pollutants is described. A mathematical model based on the multiple cell approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. These spatial temporal patterns were subject to a heuristic optimization algorithm to identify the optimal configuration of a monitoring network. The objective of the optimization is to provide maximum information about multi-pollutants (i.e., CO, NO(x) and SO(2)) emitted from each source within a given area. The model was applied to a network of existing refinery stacks and the results indicate that three stations can provide a total coverage of more than 70%. In addition, the effect of the spatial correlation coefficient (R(C)) on total area coverage was analyzed. The modeling results show that as the cutoff correlation coefficient R(C) is increased from 0.75 to 0.95, the number of monitoring stations required for total coverage is increased. A high R(C) based network may not necessarily cover the entire region, but the covered region will be well represented. A low R(C) based network, on the other hand, would offer more coverage of the region, but the covered region may not be satisfactorily represented.

Investigation of the efficiency of existing air pollution monitoring sites in the state of Kuwait.

The use of mathematical modelling for investigation of the efficiency of existing monitoring sites for the impact of SO(2) emissions from power stations in the state of Kuwait is described. The Industrial Source Complex Short Term (ISCST3) model is utilised to obtain the spatial and temporal variations of SO(2) over residential areas. Statistical comparison between the 50 highest daily measured and predicted SO(2) concentrations at six monitoring sites shows that the model is capable of generating results with accuracy of 60--94%. An important conclusion of this work is that the existing locations of the Kuwait-EPA monitoring stations are not suitable for measuring the actual impact of SO(2). Therefore, there is a need for relocation of these sites to register the highest levels of SO(2) emitted from the current power stations in the state of Kuwait.