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In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOX, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter. [ABSTRACT FROM AUTHOR]

In this paper, based on a sample selection rule and a Back Propagation (BP) neural network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO2, NO2, and PM10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE) values decreasing by 4% and R² values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale. [ABSTRACT FROM AUTHOR]

With the rapid increase of the vehicle population in the Guanzhong Plain (GZP), the fine particulate matter (PM2.5) emitted by vehicles has an impact on regional air quality and public health. The spatial distribution of primary and secondary PM2.5 concentrations from vehicles in GZP in January and July 2017 was simulated in this study by using the Weather Research and Forecasting (WRF) model and the California Puff (CALPUFF) air quality model. The contributions of vehicle-related emission sources to total PM2.5 concentrations were also calculated. The results show that although the emissions of primary PM2.5, NOx, and SO2 in July were greater than those in January, the hourly average concentrations of primary and secondary PM2.5 in January were significantly higher than those in July. The highest concentrations of primary and total PM2.5 were mostly located in the urban areas of Xi'an and Xianyang in the central region of GZP. The contributions of exhaust emissions, secondary nitrates, brake wear, tire wear, and secondary sulfate to the total PM2.5 concentrations in GZP were 50.37%, 34.76%, 10.79%, 4.06%, and 0.04% in January and 71.91%, 11.14%, 11.89%, 5.03%, and 0.03% in July, respectively. These results will help us to further control PM2.5 pollution caused by vehicles. [ABSTRACT FROM AUTHOR]