Background: Air quality modeling can be considered as a useful tool to predict air quality in future and determine the control strategies of emissions abatement. In this study, AERMOD dispersion model has been applied as a tool for the analysis of the values of CO emissions from the stacks and flares of South Pars fourth Gas Refinery located in Asaluyeh. Methods: First, the values of CO emissions from the refinery's stacks and flares were investigated by measurement and using the emission factors in four seasons of 2013. Then, dispersion of pollutants was predicted by using the AERMOD model in the region with area of 10×10 km 2 in each direction of x and y, in average times of 1, 3, 8, 24-hours and for the annual statistical period. Then the predicted and field measurement values in 9 receptors have been compared. Results: Statistical evaluation showed that the correlation coefficient values for CO were 0.85 in spring, 0.89 in summer, 0.96 in fall, and 0.95 in winter. The maximum concentration of CO was occurred in local scale of 10×10 km 2. Conclusion: Comparison of maximum 1-hour and 8-hour concentrations of the predicted results with the national and international standards showed that CO concentration is higher than standard values. In total, according to the evaluation of the predictions made, the performance of AERMOD model was acceptable in prediction of CO concentrations in the study area.
This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution during the summer of 2017 over the contiguous United States (CONUS). We employed the partial convolutional neural network (PCNN) and the deep neural network (DNN) to fill gaps in the OMI tropospheric NO2 column and estimate the daily proxy surface NO2 map at a spatial resolution of 10 km × 10 km, showing high capability with strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). Subsequently, we conducted an inversion of NOx emissions using the Community Multiscale Air Quality (CMAQ) model at 12 km grid spacing to gain a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion indicated higher NOx emissions over CONUS (3.21 ± 3.34 times), effectively mitigating the underestimation of surface NO2 concentrations with the prior emissions. Incorporating the DL-estimated daily proxy surface NO2 map yielded primary benefits, reducing bias (-1.53 ppb to 0.26 ppb) and enhancing day-to-day variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) across CONUS.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.