汕头市PM_(2.5)的气象要素影响分析及预报研究
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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。
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.
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.
引文
[1]杨洪斌，邹旭东，汪宏宇，等．大气环境中PM2. 5的研究进展与展望[J]．气象与环境学报，2012,28(3):77-82.
[2]朱彤，尚静，赵德峰．大气复合污染及灰霾形成中非均相化学过程的作用[J]．中国科学:化学，2010,40(12):1731-1740.
[3]赵晨曦，王云琦，王玉杰，等．北京地区冬春PM2. 5和PM10污染水平时空分布及其与气象条件的关系[J]．环境学报，2014,35(2):418-427.
[4]孟昭阳，张怀德，蒋晓明，等．太原地区冬春季PM2. 5污染特征及影响因素[J]．中国科学院研究生院报，2007,24(5):648-656.
[5]刘爱明，杨柳，吴亚玲，等．城市区域大气颗粒物的健康效应研究[J]．中国环境监测，2012,28(5):19-23.
[6]郭新彪，魏红英．大气PM2. 5对健康影响的研究进展[J]．科学通报，2013,58(13):1171-1177.
[7]陈仁杰，陈秉衡，阚海东．我国113个城市大气颗粒物污染的健康经济学评价[J]．中国环境科学，2010,30(3):410-415.
[8]陶燕，刘亚梦，米生权，等．大气细颗粒物的污染特征及对人体健康的影响[J]．环境科学学报，2014,34(3):592-597.
[9]曾静，王美娥，张红星．北京市夏秋季大气PM2. 5浓度与气象要素的相关性[J]．应用生态学报，2014,25(9):2695-2699.
[10]张莹，贾旭伟，杨旭，等．中国典型代表城市空气污染特征及其与气象参数的关系[J]．气象与环境学报，2017,33(2):70-79.
[11]陈镭，马井会，甄新蓉，等．上海地区空气污染变化特征及其气象影响因素[J]．气象与环境学报，2017,33(6):59-67.
[12]赵妤希，陈义珍，杨欣，等．北京市中心城区PM2. 5长期变化趋势和特征[J]．生态环境学报，2016,25(9):1493-1498.
[13]李会霞，史兴民．西安市PM2. 5时空分布特征及气象成因[J]．生态环境学报，2016,25(2):266-271.
[14]刘新春，陈红娜，赵克蕾，等．乌鲁木齐气溶胶粒径分布及细颗粒物(PM2. 5)浓度变化分析[J]．生态环境学报，2016,25(4):605-613.
[15]宋桂英，江靖，狄慧，等．APEC会议期间呼和浩特市大气污染防控与气象条件分析[J]．气象与环境学报，2017,33(2):63-69.
[16]谢松元，凌良新，陈文锋．潮州市大气污染物与气象要素的关系[J]．广东气象，2010,32(5):36-38.
[17]蒲维维，赵秀娟，张小玲．北京地区夏末秋初气象要素对PM2. 5污染的影响[J]．应用气象学报，2011,22(6):716-723
[18]潘本锋，赵熠琳，李健军．气象因素对大气中PM2. 5的去除效应分析[J]．环境科技，2012,25(6):41-44.
[19]黄亚林，刘超，曾克峰，等．2013─2014年武汉市PM2. 5的时空分布特征及其与气象条件的关系[J]．生态环境学报，2015,24(8):1330-1335.
[20]张玮，郭胜利，申付振，等．南京地区PM2. 5和PM10浓度分布特征及与相关气象条件的关系[J]．科学技术与工程，2016,16(4):124-129.
[21]刘旸，官莉．人工神经网络法反演晴空大气湿度廓线的研究[J]．气象，2011,37(3):318-324.
[22]马学款，唐叔乙，林志强．人工神经网络在西藏中短期温度预报中的应用[J]．高原气象，2007,26(3):491-495.
[23]李晓岚，刘旸，栾健，等．基于BP人工神经网络法沈阳市PM2. 5质量浓度集成预报试验[J]．气象与环境学报，2018,34(2):99-105.
[24]马雁军，杨洪斌，张云海．BP神经网络法在大气污染预报中的应用研究[J]．气象，2003,29(7):49-52.
[25]廖国莲．大气混合层厚度的计算方法及影响因子[J]．中山大学研究生学刊:自然科学、医学版，2005,26(4):66-73.
[2]朱彤，尚静，赵德峰．大气复合污染及灰霾形成中非均相化学过程的作用[J]．中国科学:化学，2010,40(12):1731-1740.
[3]赵晨曦，王云琦，王玉杰，等．北京地区冬春PM2. 5和PM10污染水平时空分布及其与气象条件的关系[J]．环境学报，2014,35(2):418-427.
[4]孟昭阳，张怀德，蒋晓明，等．太原地区冬春季PM2. 5污染特征及影响因素[J]．中国科学院研究生院报，2007,24(5):648-656.
[5]刘爱明，杨柳，吴亚玲，等．城市区域大气颗粒物的健康效应研究[J]．中国环境监测，2012,28(5):19-23.
[6]郭新彪，魏红英．大气PM2. 5对健康影响的研究进展[J]．科学通报，2013,58(13):1171-1177.
[7]陈仁杰，陈秉衡，阚海东．我国113个城市大气颗粒物污染的健康经济学评价[J]．中国环境科学，2010,30(3):410-415.
[8]陶燕，刘亚梦，米生权，等．大气细颗粒物的污染特征及对人体健康的影响[J]．环境科学学报，2014,34(3):592-597.
[9]曾静，王美娥，张红星．北京市夏秋季大气PM2. 5浓度与气象要素的相关性[J]．应用生态学报，2014,25(9):2695-2699.
[10]张莹，贾旭伟，杨旭，等．中国典型代表城市空气污染特征及其与气象参数的关系[J]．气象与环境学报，2017,33(2):70-79.
[11]陈镭，马井会，甄新蓉，等．上海地区空气污染变化特征及其气象影响因素[J]．气象与环境学报，2017,33(6):59-67.
[12]赵妤希，陈义珍，杨欣，等．北京市中心城区PM2. 5长期变化趋势和特征[J]．生态环境学报，2016,25(9):1493-1498.
[13]李会霞，史兴民．西安市PM2. 5时空分布特征及气象成因[J]．生态环境学报，2016,25(2):266-271.
[14]刘新春，陈红娜，赵克蕾，等．乌鲁木齐气溶胶粒径分布及细颗粒物(PM2. 5)浓度变化分析[J]．生态环境学报，2016,25(4):605-613.
[15]宋桂英，江靖，狄慧，等．APEC会议期间呼和浩特市大气污染防控与气象条件分析[J]．气象与环境学报，2017,33(2):63-69.
[16]谢松元，凌良新，陈文锋．潮州市大气污染物与气象要素的关系[J]．广东气象，2010,32(5):36-38.
[17]蒲维维，赵秀娟，张小玲．北京地区夏末秋初气象要素对PM2. 5污染的影响[J]．应用气象学报，2011,22(6):716-723
[18]潘本锋，赵熠琳，李健军．气象因素对大气中PM2. 5的去除效应分析[J]．环境科技，2012,25(6):41-44.
[19]黄亚林，刘超，曾克峰，等．2013─2014年武汉市PM2. 5的时空分布特征及其与气象条件的关系[J]．生态环境学报，2015,24(8):1330-1335.
[20]张玮，郭胜利，申付振，等．南京地区PM2. 5和PM10浓度分布特征及与相关气象条件的关系[J]．科学技术与工程，2016,16(4):124-129.
[21]刘旸，官莉．人工神经网络法反演晴空大气湿度廓线的研究[J]．气象，2011,37(3):318-324.
[22]马学款，唐叔乙，林志强．人工神经网络在西藏中短期温度预报中的应用[J]．高原气象，2007,26(3):491-495.
[23]李晓岚，刘旸，栾健，等．基于BP人工神经网络法沈阳市PM2. 5质量浓度集成预报试验[J]．气象与环境学报，2018,34(2):99-105.
[24]马雁军，杨洪斌，张云海．BP神经网络法在大气污染预报中的应用研究[J]．气象，2003,29(7):49-52.
[25]廖国莲．大气混合层厚度的计算方法及影响因子[J]．中山大学研究生学刊:自然科学、医学版，2005,26(4):66-73.
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