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The evolution of the power industry toward large‐scale automation and self‐monitoring provides the opportunity to optimize the technical and environmental performance of the plant with data‐driven methods with little changes in infrastructure. This article applies the artificial neural network (ANN) and genetic algorithm (GA) to predicting and optimizing NO emissions. Multiple linear regression models, correlation matrix, and research background are employed to find the most influential input features. The generated power, natural gas flow, the flow of gas recirculation fan, gas air heater temperature, and the amounts of oxygen in the stack are identified as the effective input features. Mean Square Error (MSE) and the coefficient of determination (R2) of best architecture (22 neurons in a hidden layer) are calculated 0.0117 and 0.96651, respectively. The Average Percentage Error (APE) is usually below 10%, meaning the model is in good agreement with real data. Finally, the Genetic Algorithm (GA) is used to minimize the amount of NO emissions. The ANN‐GA techniques reduce the NO emission by at least 32% for selected records, enabling us to optimally find the prominent features affecting NO emission by the operational conditions and low economic costs. [ABSTRACT FROM AUTHOR]

Consumption of fossil fuels in residential, industrial, and transportation sectors has a significant contribution to air pollution and high attributable premature deaths. To target specific strategies for reducing health effects, the anthropogenic sources and their contributions should be identified. This article provides an integrated modeling approach to evaluate the sector-specific contributions of different PM2.5 sources on air pollution and relevant health impacts. The dispersion of emissions from different sources is modeled by the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD), and the health impacts are estimated by the Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE), while the global exposure mortality model (GEMM) is used as the health impact function. The methodology is applied for the special region in Qazvin province, Islamic Republic of Iran. The total premature deaths from exposure to PM2.5 which are attributable to the suburban transportation systems, industries, and towns and villages (domestic sector and intra-urban transportation systems) are 108.1, 96.3, and 653.4 that are equal to 4%, 4%, and 24% of the total premature deaths, respectively. In some cases, the spatial distribution of PM2.5 from different emission sources, altogether, is not compliant with the limits, while their single distribution is compliant with the limit. This synergy effect is very important for sustainable environmental planning in the region. The results show that the emissions of PM2.5 from towns and villages play a major role in public health, and thus, additional measures and investigations should be implemented to further reduce PM2.5 pollutions and protect public health in the urban area in the future. This research suggests a framework to identify the sources of the health impacts and help policymakers to carefully design the environmental action plans in the region. [ABSTRACT FROM AUTHOR]