基于数据挖掘算法和数值模拟技术的大气污染减排效果评估
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摘要
近年来,京津冀地区采取了大量污染减排措施进行大气污染治理,如何客观评估减排效果是目前大气环境领域的研究难点.为准确评估大气污染过程的减排效果,本文利用北京地区常规气象资料、国控站PM_(2.5)浓度资料,遴选了北京地区2018年3月11—14日和2013年3月14—17日两次空气污染过程,计算了大气容量系数、静稳指数,并利用KNN数据挖掘算法和WRF-Chem模式,对比分析了有无减排条件下的PM_(2.5)日均浓度.结果表明:两次空气污染过程的天气形势和局地气象条件较相似,就大气热力和动力的垂直结构来看,2018年空气污染过程比2013年空气污染过程的大气稳定性更强、边界层高度更低、环境容量更小,但PM_(2.5)峰值浓度却显著下降,平均浓度明显降低,PM_(2.5)小时浓度的增长趋势相对平缓,重污染持续时间缩短.KNN数据挖掘算法减排评估结果显示,该方法能够较好地预测PM_(2.5)日均浓度的变化趋势,2018年3月11—14日,在减排和不减排情景下PM_(2.5)日均值分别为171和229μg·m~(-3),减排使得污染过程PM_(2.5)平均浓度下降了25.3%.数值模拟结果与KNN数据分析结论吻合,进一步验证了减排措施的有效性.综合看来,2018年空气污染过程中PM_(2.5)浓度相比历史相似气象条件下的污染过程显著降低,这是长期大力度减排效果的体现.
Air pollution reduction measures have been recently adopted in the Beijing-Tianjin-Hebei region. Objectively assessing atmospheric pollution reductions is difficult in the atmospheric environment field. This study used meteorological data, the PM_(2.5) concentration data in Beijing, and two air pollution processes(March 11—14, 2018 and March 14—17, 2013) in the Beijing area to calculate the atmospheric capacity coefficient and static stability index for assessing the effects of the air pollution reduction measures. Average daily PM_(2.5) concentrations were analyzed under an emission reduction condition by using the KNN data mining algorithm and WRF-Chem mode. The results showed that the weather conditions and local meteorological conditions were similar for the two air pollution processes from the perspective of atmospheric thermal and dynamic vertical structures. For the 2018 process, the atmosphere was more stable, the boundary layer height was lower, and the environmental capacity was smaller than that of the 2013 process. Peak and average PM_(2.5) concentrations dropped sharply and the PM_(2.5) hourly concentration grew relatively slowly. Moreover, the duration of heavy pollution was shortened. Evaluating emission reduction results based on the KNN data mining algorithm showed that this method can reliably predict the changing tendency of daily average PM_(2.5) concentrations. The average PM_(2.5) concentration during March 11—14, 2018 was 171 and 229 μg·m~(-3) under the emission reduction and nonemission reduction conditions, respectively. The PM_(2.5) concentration decreased by approximately 25.3% because of the emission reduction measures. Moreover, numerical simulation results were consistent with those from the KNN data analysis, which further verified the effectiveness of the emission reduction measures. In summary, the PM_(2.5) concentration for the 2018 pollution process was significantly lower than that in similar meteorological conditions in history, which is a reflection of the long-term, large-scale emission reduction efforts.
Air pollution reduction measures have been recently adopted in the Beijing-Tianjin-Hebei region. Objectively assessing atmospheric pollution reductions is difficult in the atmospheric environment field. This study used meteorological data, the PM_(2.5) concentration data in Beijing, and two air pollution processes(March 11—14, 2018 and March 14—17, 2013) in the Beijing area to calculate the atmospheric capacity coefficient and static stability index for assessing the effects of the air pollution reduction measures. Average daily PM_(2.5) concentrations were analyzed under an emission reduction condition by using the KNN data mining algorithm and WRF-Chem mode. The results showed that the weather conditions and local meteorological conditions were similar for the two air pollution processes from the perspective of atmospheric thermal and dynamic vertical structures. For the 2018 process, the atmosphere was more stable, the boundary layer height was lower, and the environmental capacity was smaller than that of the 2013 process. Peak and average PM_(2.5) concentrations dropped sharply and the PM_(2.5) hourly concentration grew relatively slowly. Moreover, the duration of heavy pollution was shortened. Evaluating emission reduction results based on the KNN data mining algorithm showed that this method can reliably predict the changing tendency of daily average PM_(2.5) concentrations. The average PM_(2.5) concentration during March 11—14, 2018 was 171 and 229 μg·m~(-3) under the emission reduction and nonemission reduction conditions, respectively. The PM_(2.5) concentration decreased by approximately 25.3% because of the emission reduction measures. Moreover, numerical simulation results were consistent with those from the KNN data analysis, which further verified the effectiveness of the emission reduction measures. In summary, the PM_(2.5) concentration for the 2018 pollution process was significantly lower than that in similar meteorological conditions in history, which is a reflection of the long-term, large-scale emission reduction efforts.
引文
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徐敬,马志强,赵秀娟,等.2015.边界层方案对华北低层O3垂直分布模拟的影响[J].应用气象学报,26(5):567-577
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Yan T,An X,Sun Z,et al.2012.Association between dust weather and number of admissions for patients with respiratory diseases in spring in Lanzhou[J].Science of the Total Environment,423(8):8-11
张恒德,张碧辉,吕梦瑶,等.2017.北京地区静稳天气综合指数的初步构建及其在环境气象中的应用[J].气象,43(8):998-1004
张恒德,吕梦瑶,张碧辉,等.2016.2014年2月下旬京津冀持续重污染过程的静稳天气及传输条件分析[J].环境科学学报,36(12):4340-4351
赵秀娟,徐敬,张自银,等.2016.北京区域环境气象数值预报系统及PM2.5预报检验[J].应用气象学报,27(2):160-172
Zhai S,An X,Zhao T,et al.2016.Detecting critical PM2.5 emission sources and their contributions to a heavy haze episode in Beijing,China by using an adjoint model[J].Atmospheric Chemistry & Physics,16:1-20
Ge B,Wang Z,Lin W,et al.2018.Air pollution over the North China Plain and its implication of regional transport: A new sight from the observed evidences[J].International Journal of Molecular Sciences,18(10):29-38
郭淳薇,孙兆彬,李梓铭,等.2017.北京地区近35年大气污染扩散条件变化[J].环境科学,38(6):2202-2210
Hong C,Zhang Q,He K,et al.2017.Variations of china′s emission estimates: response to uncertainties in energy statistics.Atmospheric Chemistry & Physics,17(2): 1-23
李梓铭,孙兆彬,邵勰,等.2017.北京城区PM2.5不同时间尺度周期性研究[J].中国环境科学,37(2):407-415
刘树华,刘振鑫,李炬,等.2009.京津冀地区大气局地环流耦合效应的数值模拟[J].中国科学,39(1):88-98
马学款,张碧辉,桂海林,等.2017.APEC前后北京几次静稳天气边界层特征对比分析[J].气象,43(11):1364-1373
Miao Y,Guo J,Liu S,et al.2018.Impacts of synoptic condition and planetary boundary layer structure on the trans-boundary aerosol transport from Beijing-Tianjin-Hebei region to northeast China[J].Atmospheric Environment,181:1-11
苏福庆,任阵海,高庆先,等.2004a.北京及华北平原边界层大气中污染物的汇聚系统--边界层输送汇[J].环境科学研究,17(1):21-25
苏福庆,高庆先,张志刚,等.2004b.北京边界层外来污染物输送通道[J].环境科学研究,17(1):26-29
孙兆彬,安兴琴,陶燕,等.2012.基于GIS和大气数值模拟技术评估兰州市PM10的人群暴露水平[J].中国环境科学,32(10):1753-1757
孙兆彬,李梓铭,廖晓农,等.2017.北京大气热力和动力结构对污染物输送和扩散条件的影响[J].中国环境科学,37(5):1693-1705
孙兆彬,陶燕,崔甍甍,等.2015.北京地区奥运会期间PM2.5对心脑血管疾病的影响[J].中国环境科学,35(11):3481-3488
Sun Z,An X,Tao Y,et al.2013.Assessment of population exposure to PM10 for respiratory disease in Lanzhou (China) and its health-related economic costs based on GIS[J].Bmc Public Health,13(1):1-9
王占山,李云婷,张大伟,等.2017.2015年“九三阅兵”期间北京市空气质量分析[J].中国环境科学,37(5):1628-1636
Wang Z,Li Y,Chen T,et al.2016.Science-policy interplay: Improvement of air quality from 2008 to 2014 in Beijing and the scientific approach to achieve APEC Blue[J].Bulletin of the American Meteorological Society,97(4):553-559
吴兑,廖碧婷,吴蒙,等.2014.环首都圈霾和雾的长期变化特征与典型个例的近地层输送条件[J].环境科学学报,34(1):1-11
吴进,李琛,孙兆彬,等.2017.北京地区两次重污染过程中PM2.5浓度爆发性增长及维持的气象条件[J].干旱气象,35(5):830-838
熊亚军,廖晓农,李梓铭,等.2015. KNN数据挖掘算法在北京地区霾等级预报中的应用[J].气象,41(1):98-104
徐大海,王郁,朱蓉.2016.大气环境容量系数A值频率曲线拟合及其应用[J].中国环境科学,36(10):2913-2922
徐大海,朱蓉.1993.国家标准GB/T3840-91中城市大气污染物总量控制A-P值法的应用[J].城市环境与城市生态,6(2):36-41
徐大海,李宗恺.1993.城市大气污染物排放总量控制中多源模拟法与国家准GB/T3840-91中A-P值方法的关系[J].气象科学,13(2):146-156
徐敬,马志强,赵秀娟,等.2015.边界层方案对华北低层O3垂直分布模拟的影响[J].应用气象学报,26(5):567-577
徐祥德,王寅钧,赵天良,等.2015.中国大地形东侧霾空间分布“避风港”效应及其“气候调节”影响下的年代际变异[J].科学通报,(12):1132-1143
Yan T,An X,Sun Z,et al.2012.Association between dust weather and number of admissions for patients with respiratory diseases in spring in Lanzhou[J].Science of the Total Environment,423(8):8-11
张恒德,张碧辉,吕梦瑶,等.2017.北京地区静稳天气综合指数的初步构建及其在环境气象中的应用[J].气象,43(8):998-1004
张恒德,吕梦瑶,张碧辉,等.2016.2014年2月下旬京津冀持续重污染过程的静稳天气及传输条件分析[J].环境科学学报,36(12):4340-4351
赵秀娟,徐敬,张自银,等.2016.北京区域环境气象数值预报系统及PM2.5预报检验[J].应用气象学报,27(2):160-172
Zhai S,An X,Zhao T,et al.2016.Detecting critical PM2.5 emission sources and their contributions to a heavy haze episode in Beijing,China by using an adjoint model[J].Atmospheric Chemistry & Physics,16:1-20