兰州主城臭氧污染特征及气象因子分析
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摘要
基于2014—2017年兰炼宾馆、生物制品所、职工医院、铁路设计院4个国控站的监测数据,通过分析兰州市主城臭氧浓度变化时间分布特征以及气象指标对臭氧浓度的影响关系,进而通过遗传算法得到最优的气象因子范围,以此预测O_(3_8h)浓度的超标情况。结果表明:2014—2017年兰州市主城臭氧污染主要集中在4—8月,且每日14—16时处于高值区;对臭氧浓度变化起主导作用的气象因素有净地表太阳辐射、边界层高度、距地面2 m温度、西风、850 hPa相对湿度以及850 hPa垂直速度;采用遗传算法得到最优气象因子范围,据此判断气象条件是否处于高影响状态:当此时刻气象条件处于高影响状态时,下一时刻O_(3_8h)超标(O_(3_8h)大于160μg·m~(-3))概率为42. 31%,O_(3_8h)超过130μg·m~(-3)的概率为99. 04%;当此时刻气象条件处于低影响状态时,下一时刻O_(3_8h)未超标概率为99. 87%。因此,在兰州市主城臭氧防治中,首先需要对未来的气象条件进行判断,进而在09时前人为操控一些主要气象因子或者控制臭氧前体物浓度,从而抑制臭氧浓度在09—14时快速上升,防止臭氧浓度超标。
Based on the monitoring data from the four state-controlled stations of Lanlian Hotel,Biological Products Institute,Staff Hospital and Railway Design Institute from 2014 to 2017,this paper analyzed the time distribution characteristics of ozone concentration and the influence of meteorological indicators on ozone concentration over Lanzhou city. The optimal meteorological factor range was obtained by a genetic algorithm to predict the excessive O_(3_8h) concentration. The results showthat from 2014 to 2017,the higher ozone pollution over the main city of Lanzhou mainly occurs in the period of April to August during which a high value ranges between 14: 00 to 16: 00 every day. The meteorological factors that play a leading role in the change of ozone concentration are the net surface solar radiation,height of boundary layer,2 m temperature,westerly wind,relative humidity and vertical velocity at 850 hPa. The range of the optimal meteorological factors obtained by the genetic algorithm is used to identify whether the meteorological conditions are in a high-impact state. At time when the meteorological conditions are in a high-impact state,the probability of O_(3_8h) exceeding the standard value (O_(3_8h) greater than 160 ug·m~(-3)) at the next time is 42. 31%,and the probability of O_(3_8h) exceeding 130 ug·m~(-3) is 99. 04%. In contrast,at the time when the meteorological conditions are in a low-impact state,the probability of O_(3_8h) not exceeding the standard at the next time is 99. 87%. Therefore,in the main city of Lanzhou,it is necessary to identify the future meteorological conditions first,and then manually control some major meteorological factors or control the concentration of ozone precursors before 09: 00 to prevent the rapid rise of ozone concentration at 09: 00-14: 00 and prevent the ozone concentration from exceeding the standard level.
Based on the monitoring data from the four state-controlled stations of Lanlian Hotel,Biological Products Institute,Staff Hospital and Railway Design Institute from 2014 to 2017,this paper analyzed the time distribution characteristics of ozone concentration and the influence of meteorological indicators on ozone concentration over Lanzhou city. The optimal meteorological factor range was obtained by a genetic algorithm to predict the excessive O_(3_8h) concentration. The results showthat from 2014 to 2017,the higher ozone pollution over the main city of Lanzhou mainly occurs in the period of April to August during which a high value ranges between 14: 00 to 16: 00 every day. The meteorological factors that play a leading role in the change of ozone concentration are the net surface solar radiation,height of boundary layer,2 m temperature,westerly wind,relative humidity and vertical velocity at 850 hPa. The range of the optimal meteorological factors obtained by the genetic algorithm is used to identify whether the meteorological conditions are in a high-impact state. At time when the meteorological conditions are in a high-impact state,the probability of O_(3_8h) exceeding the standard value (O_(3_8h) greater than 160 ug·m~(-3)) at the next time is 42. 31%,and the probability of O_(3_8h) exceeding 130 ug·m~(-3) is 99. 04%. In contrast,at the time when the meteorological conditions are in a low-impact state,the probability of O_(3_8h) not exceeding the standard at the next time is 99. 87%. Therefore,in the main city of Lanzhou,it is necessary to identify the future meteorological conditions first,and then manually control some major meteorological factors or control the concentration of ozone precursors before 09: 00 to prevent the rapid rise of ozone concentration at 09: 00-14: 00 and prevent the ozone concentration from exceeding the standard level.
引文
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[2]于世杰,尹沙沙,张瑞芹,等.郑州市近地面臭氧污染特征及气象因素分析[J].中国环境监测,2017,33(4):140-149.
[3]易睿,王亚林,张殷俊,等.长江三角洲地区城市臭氧污染特征与影响因素分析[J].环境科学学报,2015,35(8):2370-2377.
[4]张青,宫正宇,孟晓艳,等.成渝地区臭氧污染特征分析[J].环境科学与技术,2017,40(S1):9-11.
[5]程麟钧,王帅,宫正宇,等.京津冀区域臭氧污染趋势及时空分布特征[J].中国环境监测,2017,33(1):14-21.
[6]梁碧玲,张丽,赖鑫,等.深圳市臭氧污染特征及其与气象条件的关系[J].气象与环境学报,2017,33(1):66-71.
[7]黄俊,廖碧婷,吴兑,等.广州近地面臭氧浓度特征及气象影响分析[J].环境科学学报,2018,38(1):23-31.
[8]林燕芬,王茜,伏晴艳,等.上海市臭氧污染时空分布及影响因素[J].中国环境监测,2017,33(4):60-67.
[9]严仁嫦,叶辉,林旭,等.杭州市臭氧污染特征及影响因素分析[J].环境科学学报,2018,38(3):1128-1136.
[10]吴锴,康平,王占山,等.成都市臭氧污染特征及气象成因研究[J].环境科学学报,2017,37(11):4241-4252.
[11]蒋璐璐,钱燕珍,杜坤,等.宁波市近地层臭氧浓度变化及预测[J].气象与环境学报,2016,32(1):53-59.
[12]祝合勇,杨太保,金庆森.兰州市城区大气污染现状及防治对策分析[J].环境科学导刊,2011,30(2):48-52.
[13]姜允迪,王式功,祁斌,等.兰州城区臭氧浓度时空变化特征及其与气象条件的关系[J].兰州大学学报:自然科学版,2000,36(5):118-125.
[14]环境保护部,国家质量监督检验检疫总局.环境空气质量标准(GB3095-2012)[S].北京:中国环境科学出版社,2016.
[15]卢秀娟,巨天珍,谢顺涛,等.兰州地区大气臭氧时空变化及影响因素研究[J].地球与环境,2018,46(4):355-363.
[16]陈世俭,童俊超,Kazuhiko K,等.气象因子对近地面层臭氧浓度的影响[J].华中师范大学学报:自然科学版,2005,39(2):273-277.
[17]张灿,蒋昌潭,罗财红,等.气象因子对臭氧的影响及其在空气质量预报中的应用[J].中国环境监测,2017,33(4):233-240.
[18]杨雅琴,高会旺.青岛大气臭氧及其前体物时间变化与污染特征[J].气象与环境学报,2008,24(2):1-5.
[19]李顺姬,李红,陈妙,等.气象因素对西安市西南城区大气中臭氧及其前体物的影响[J].气象与环境学报,2018,34(4):59-67.
[20]Long Q,Wu C Z,Huang T W,et al.A genetic algorithm for unconstrained multi-objective optimization[J].Swarm and Evolutionary Computation,2015(22):1-14.