【摘要】 利用2014—2017年汕头市PM_2.5的日浓度资料、以及汕头市国家基准气象观测站的同期地面气象资料,重点分析了汕头市PM_2.5浓度的变化特征以及风、混合层厚度、降水等气象条件对PM_2.5浓度的影响,同时探讨了污染物浓度变化的成因。在此基础上,根据汕头市的气候特点,采用BP（Back-Propagation）人工神经网络方法针对汛期和非汛期分别建立了PM_2.5质量浓度预报模型。结果表明:与多数内陆城市不同,汕头市PM_2.5浓度日变化为单峰型,这与汕头地处沿海受海陆风影响有关; PM_2.5浓度日峰值出现在08时左右,除早高峰污染物排放增加的因素外,与早晨时段的低风速环境有关; PM_2.5日均浓度随着风速的增大呈现减小趋势,PM_2.5日均浓度与08时混合层厚度显著相关（相关系数为-0. 143）;汕头市非汛期PM_2.5浓度比汛期高,这与汕头市的亚热带季风气候特征有关,汛期各量级降水（暴雨以上除外）对PM_2.5的清除效果无明显差别,而非汛期降水对PM_2.5浓度有明显清除作用; BP人工神经网络模型的预报效果表明,汛期和非汛期的PM_2.5级别命中率TS分别为100%和90. 3%,准确指数分别为87. 7%和89. 9%,总体预报效果良好。不同时期预报模型出现正误差的数量和程度均大于负误差,汛期预报模型在有强降水发生时误差较大,而非汛期预报模型在有冷空气入侵时误差较大。更多还原

【Abstract】 Based on the daily concentration of PM_2.5 data and the surface meteorological data from national meteorological observatory station in Shantou from 2014 to 2017,the variation characteristics of PM_2.5 concentration in Shantou and the influences of meteorological conditions such as wind,mixed-layer thickness and precipitation on the concentration of PM_2.5 were analyzed and the causes of pollutant concentration variation were investigated. On this basis,according to the climatic characteristics of Shantou,the models for predicting PM_2.5 mass concentration in flood and non-flood season were respectively established with BP（ Back-Propagation） artificial neural network method. The results show that the daily variation of PM_2.5 concentration in Shantou is unimodal,which is different from most inland cities and is related to the geographic location. More specifically,Shantou is located in the coastal area affected by the land-sea breeze. The daily peak of PM_2.5 concentration appears at around 8 o’ clock,which is caused by the lower wind speed and the increase of pollutant emission in the morning. The average concentration of PM_2.5 decreases with the increase of wind speed and is significantly correlated with the thickness of the mixed layer at 8 o’clock（ the correlation coefficient is-0. 143,p < 0. 001）. The concentration of PM_2.5 in the non-flood period in Shantou city is higher than that in the flood season,which is related to the subtropical monsoon climate characteristics. In addition,there is no significant difference in the removal effect of PM_2.5 among various magnitudes of precipitation in the flood season（ except for rainstorms）,while the precipitation during the non-flood period has an obvious effect in decreasing the concentration of PM_2.5. The BP artificial neural network model shows a high hit rate in forecasting the grade of PM_2.5 concentration. More specifically,Rank accuracy of PM_2.5 are 100% and 90. 3%,the accuracy coefficients are87. 7% and 89. 9% in flood season and non-flood season,respectively. The number and amplitude of positive errors of the forecasted PM_2.5 concentration by the model in different periods are larger than those of the negative errors.Furthermore,the prediction error of the model is larger when heavy rainfall occurs in flood season and cold air invades in non-flood season.更多还原