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Empirical regression models for next-day forecasting of PM 2.5 and O 3 air pollution concentrations have been developed and evaluated for three large Chinese cities, Beijing, Nanjing and Guangzhou. The forecast models are empirical nonlinear regression models designed for use in an automated data retrieval and forecasting platform. The PM 2.5 model includes an upwind air quality variable, PM24, to account for regional transport of PM 2.5 , and a persistence variable (previous day PM 2.5 concentration). The models were evaluated in the hindcast mode with a two-year air quality and meteorological data set using a leave-one-month-out cross validation method, and in the forecast mode with a one-year air quality and forecasted weather dataset that included forecasted air trajectories. The PM 2.5 models performed well in the hindcast mode, with coefficient of determination (R 2 ) values of 0.54, 0.65 and 0.64, and normalized mean error (NME) values of 0.40, 0.26 and 0.23 respectively, for the three cities. The O 3 models also performed well in the hindcast mode, with R 2 values of 0.75, 0.55 and 0.73, and NME values of 0.29, 0.26 and 0.24 in the three cities. The O 3 models performed better in summertime than in winter in Beijing and Guangzhou, and captured the O 3 variations well all the year round in Nanjing. The overall forecast performance of the PM 2.5 and O 3 models during the test year varied from fair to good, depending on location. The forecasts were somewhat degraded compared with hindcasts from the same year, depending on the accuracy of the forecasted meteorological input data. For the O 3 models, the model forecast accuracy was strongly dependent on the maximum temperature forecasts. For the critical forecasts, involving air quality standard exceedences, the PM 2.5 model forecasts were fair to good, and the O 3 model forecasts were poor to fair.

Particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) is the main air pollutant in Beijing. To have a comprehensive understanding of concentrations, compositions and sources of PM2.5 in Beijing, recent studies reporting ground-based observations and source apportionment results dated from 2000 to 2012 in this typical large city of China are reviewed. Statistical methods were also used to better enable data comparison. During the last decade, annual average concentrations of PM2.5 have decreased and seasonal mean concentrations declined through autumn and winter. Generally, winter is the most polluted season and summer is the least polluted one. Seasonal variance of PM2.5 levels decreased. For diurnal variance, PM2.5 generally increases at night and decreases during the day. On average, organic matters, sulfate, nitrate and ammonium are the major compositions of PM2.5 in Beijing. Fractions of organic matters increased from 2000 to 2004, and decreased afterwards. Fractions of sulfate, nitrate and ammonium decreased in winter and remained largely unchanged in summer. Concentrations of organic carbon and elemental carbon were always higher in winter than in summer and they barely changed during the last decade. Concentrations of sulfate, nitrate and ammonium exhibited significant increasing trend in summer but in reverse in winter. On average they were higher in winter than in summer before 2005, and took a reverse after 2005. Receptor model results show that vehicle, dust, industry, biomass burning, coal combustion and secondary products were major sources and they all increased except coal combustions and secondary products. The growth was decided both changing social and economic activities in Beijing, and most likely growing emissions in neighboring Hebei province. Explicit descriptions of the spatial variations of PM2.5 concentration, better methods to estimate secondary products and ensemble source apportionments models to reduce uncertainties would remain being open questions for future studies.