[Variation Analysis of Daily PM_2.5 Concentrations Based on Boosted Regression Tree: A Case Study in Changzhou]

Based on hourly concentration data from six state-controlled air quality monitoring stations in urban area of Changzhou from December 2014 to November 2015, the seasonal variation of PM_2.5 pollution was analyzed, and the contributions of PM₁₀, four kinds of gaseous pollutants and seven meteorological factors to daily changes of ρ(PM_2.5) were quantified by boosted regression tree (BRT). The results showed that:the seasonal differences of PM_2.5 pollution were significant, the pollution was serious in winter and the pollution duration was long, while the pollution was light in summer. The spatial distribution of ρ(PM_2.5) in four seasons was different, but the six monitoring stations showed similar trends in each season. Daily average ρ(PM_2.5) was simulated and verified by BRT. The correlation coefficient of the training data was 0.981, and the cross-validation correlation coefficient was 0.957. In addition, the mean deviation between the simulated values and the measured values was 1.80%, and the standardized mean error was 10.41%, which showed that the model fitted well. The contribution percentages of four kinds of impact types (PM₁₀, gaseous pollutants, meteorological factors and regional transport and diffusion) to daily average ρ(PM_2.5) changes of four seasons were 23.4%, 28%, 36.2% and 12.6%, respectively. So, the most significant affecting factor was meteorological condition, followed by secondary formation, primary emission, and regional transport and diffusion. In the factors with contribution percentages of more than 5%, the daily average ρ(PM_2.5) was positively associated with PM₁₀, relative humidity, CO and O₃, and was negatively correlated with temperature, SO₂ and mixed layer high. In addition, the daily average ρ(PM_2.5) had complex relationships with atmospheric pressure and NO₂. For regional transport and diffusion, the polluted air flow from southeast, west and north had a relatively great negative impact on PM_2.5 pollution of urban area in Changzhou.