长三角对流层甲醛柱浓度时空变化及驱动因素

钱韵; 吴健生; 谭羲; 罗宇航; 陆天华

PDF(1220 KB)

中国环境科学 ›› 2021, Vol. 41 ›› Issue (11) : 4973-4981.

大气污染与控制

长三角对流层甲醛柱浓度时空变化及驱动因素

钱韵¹, 吴健生², 谭羲¹, 罗宇航¹, 陆天华³

作者信息 +

Spatiotemporal variation of tropospheric formaldehyde concentration and its driving factors in Yangtze River

QIAN Yun¹, WU Jian-sheng², TAN Xi¹, LUO Yu-hang¹, LU Tian-hua³

Author information +

文章历史 +

摘要

通过OMI卫星数据分析了2005~2016年长江三角洲对流层甲醛柱浓度的时空变化规律.同时结合2008年和2010年各部门VOCs人为源排放量，利用BP神经网络和RBFN神经网络模型对对流层甲醛柱浓度进行了县域尺度上的回归模拟和各部门排放量贡献度分析.结果表明：长三角城市群对流层甲醛柱浓度在2005~2010年存在着增加趋势，2011~2016年甲醛浓度有下降的趋势.高值区域分布在皖北苏北、上海及其附近，低值区域分布在浙西南一带.人为源排放使得经济发达地区的甲醛柱浓度显著增高.工业源在长三角的分布较为广泛，电力源分布稀疏且VOC排放量远小于工业源排放量，居民源的VOC排放量介于工业源和电力源之间，有明显的南北差异.交通源主要集中在苏南、浙北和上海附近，少部分沿交通线条状分布.机器学习算法可以较好地利用人为源排放数据对甲醛柱浓度进行模拟.神经网络的拟合精度可以达到0.6~0.8，比线性回归的拟合精度超出0.3~0.4.模型变量重要性计算显示各部门中居民源对甲醛柱浓度的贡献程度最高.研究对流层甲醛柱浓度的长期时空变化及其影响因素有利于深入研究臭氧污染，同时也为大气治理和政策制定提供了科学依据.

Abstract

This study uses OMI satellite data to analyse the temporal and spatial changes of the tropospheric formaldehyde column concentration in the Yangtze River Delta from 2005 to 2016. The BP and RBFN neural network models are used to perform regression simulation on the tropospheric formaldehyde column concentration at the county scale and analysis of the proportion of emissions from various departments using non-methane volatile organic compounds (NMVOC) data in 2008 and 2010. The results show that the tropospheric formaldehyde column concentration in the Yangtze River Delta urban agglomeration has an increasing trend from 2005 to 2010 and a downward trend from 2011 to 2016. The concentrations are higher in northern Anhui, northern Jiangsu, Shanghai and nearby areas, while those in southwestern Zhejiang are lower. In addition, NMVOC have significantly increased the concentration of formaldehyde in economically developed areas. The industrial sector's emissions are widely distributed in the Yangtze River Delta, and the VOC emissions from the power sector are much smaller than those from the industrial sector, and the distribution is also very sparse. The amount of VOC emissions generated by residents' lives is between the above two, with a clear North-South differentiation. Those from the transportation sector are mainly concentrated in southern Jiangsu, northern Zhejiang and Shanghai, and are distributed in strips along the transportation lines. What's more, the fitting accuracy of the neural network can reach 0.6~0.8, which is 0.3~0.4 higher than that of the linear regression, which proves that machine learning algorithms can better simulate the concentration of the formaldehyde column with NMVOC. The VOC emissions generated by residents' lives contribute most to the tropospheric formaldehyde column concentration. Studying the long-term temporal and spatial changes of the tropospheric formaldehyde column concentration and its influencing factors is conducive to in-depth study of ozone pollution, and it also provides a scientific basis for atmospheric governance and policy making.

导出引用

钱韵, 吴健生, 谭羲, 罗宇航, 陆天华. 长三角对流层甲醛柱浓度时空变化及驱动因素[J]. 中国环境科学. 2021, 41(11): 4973-4981

QIAN Yun, WU Jian-sheng, TAN Xi, LUO Yu-hang, LU Tian-hua. Spatiotemporal variation of tropospheric formaldehyde concentration and its driving factors in Yangtze River[J]. China Environmental Science. 2021, 41(11): 4973-4981

中图分类号： X515

参考文献

[1] Zhu L, Jacob D J, Keutsch F N, et al. Formaldehyde (HCHO) As a Hazardous Air Pollutant:Mapping Surface Air Concentrations from Satellite and Inferring Cancer Risks in the United States[J]. Environ. Sci. Technol., 2017,51(10):5650-7.
[2] Barkley M P, De Smedt I, Van Roozendael M, et al. Top-down isoprene emissions over tropical South America inferred from SCIAMACHY and OMI formaldehyde columns[J]. J. Geophys. Res.-Atmos., 2013,118(12):6849-68.
[3] Baek K H, Kim J H, Park R J, et al. Validation of OMI HCHO data and its analysis over Asia[J]. Sci. Total. Environ., 2014,490:93-105.
[4] Hong H, Yang J, Kang H, et al. Estimation of HCHO Column Using a Multiple Regression Method with OMI and MODIS Data[J]. 대한원격탐사학회지, 2019,35(4):503-16.
[5] Boeke N L, Marshall J D, Alvarez S, et al. Formaldehyde columns from the Ozone Monitoring Instrument:Urban versus background levels and evaluation using aircraft data and a global model[J]. J. Geophys. Res.-Atmos., 2011,116(D5):1-11.
[6] Liu R, Feng T, Wang S S, et al. OMI satellite observed formaldehyde column from 2006 to 2015 over Xishuangbanna, southwest China, and validation using ground based zenith-sky DOAS[J]. Sci. Total. Environ., 2018,613:168-75.
[7] Fan J C, Ju T Z, Wang Q H, et al. Spatiotemporal variations and potential sources of tropospheric formaldehyde over eastern China based on OMI satellite data[J]. Atmos. Pollut. Res., 2021,12(1):272-85.
[8] 谢顺涛,巨天珍,张惠娥.基于OMI数据的兰州地区对流层甲醛时空变化研究[J]. 环境科学学报, 2017,37(11):4253-4261.Xie S T, Ju T Z, Zhang H E. 2017. Temporal and spatial variations of tropospheric formaldehyde in Lanzhou aera based on remote sensing data. Acta Scientiae Circumstantiae, 37(11):4253-4261.
[9] Zhu S Y, Li X Y, Yu C, et al. Spatiotemporal Variations in Satellite-Based Formaldehyde (HCHO) in the Beijing-Tianjin-Hebei Region in China from 2005 to 2015[J]. Atmosphere-Basel, 2018, 9(1).10.3390/atmos9010005.
[10] 刘宏庆,巨天珍,裴洁,等.黑龙江省近12年甲醛柱浓度时空变化及其影响因素[J]. 中国环境科学, 2018,38(6):2001-11.Liu H Q, Ju T Z, Pei J, et al. Spatial-temporal changes of tropospheric HCHO column density and its impact factors over Heilongjiang Province during 2005~2016[J]. China Environmental Sciencece, 2018, 38(6):2001-2011.
[11] 刘旻霞,宋佳颖,孙瑞弟,等.近14年西北地区甲醛柱浓度的时空变化研究[J]. 中国环境科学, 2020,40(6):2321-34.Liu M X, Song J Y, Sun R D, et al. Spatio-temporal variation of formaldehyde column concentration in Northwest China in the past 14 years[J]. China Environmental Sciencece, 2020,40(6):2321-2334.
[12] 李璐,巨天珍,高海燕,等.陕西省甲醛柱浓度时空变化及影响因子分析[J]. 中国环境科学, 2020,40(7):2802-10.Li L, Ju T Z, Gao H Y, et al. Temporal and spatial variation of formaldehyde column concentration and analysis of its influencing factors in Shaanxi Province[J]. China Environmental Sciencece, 2020, 40(7):2802-2810.
[13] 咸龙,葛建团,徐敏,等.珠江三角洲对流层HCHO柱浓度遥感监测及影响因子[J]. 中国环境科学, 2018,38(9):3221-31.Xian L, Ge J T, Xu M, et al. Remote sensing monitoring of tropospheric HCHO column concentration and influential factors over pearl river delta, China[J]. China Environmental Sciencece, 2018, 38(9):3221-3231.
[14] Karaca F, Nikov A, Alagha O. NN-AirPol:a neural-networks-based method for air pollution evaluation and control[J]. Int. J. Environ. Pollut., 2006,28(3/4):310-25.
[15] 尹晓梅,李梓铭,熊亚军,等.2014~2017北京市气象条件和人为排放变化对空气质量改善的贡献评估.环境科学, 2019,40(3):1011-1023.Yin X M, Li Z M, Xiong Y J, et al. Contribution assessment of meteorology conditions and emission change for air quality improvement in Beijing during 2014~2017[J]. Environmental Science, 2019,40(3):1011-1023.
[16] Yazdi M D, Kuang Z, Dimakopoulou K, et al. Predicting fine particulate matter (PM_2.5) in the Greater London Area:An ensemble approach using machine learning methods[J]. Remote Sens-Basel, 2020,12(6):914.
[17] 李阳,巨天珍,马成,等.长江三角洲近10年甲醛柱浓度变化及影响因素[J]. 中国环境科学, 2019,39(3):897-907.Li Y, Ju T Z, Ma C, et al. The temporal, spatial variation and influencing factor of formaldehyde column concentration in the Yangtze River Delta in the past 10years[J]. China Environmental Sciencece, 2019,39(3):897-907.
[18] Wang M, Chen W T, Zhang L, et al. Ozone pollution characteristics and sensitivity analysis using an observation-based model in Nanjing, Yangtze River Delta Region of China[J]. J. Environ. Sci., 2020,93:13-22.
[19] Jin X M, Holloway T. Spatial and temporal variability of ozone sensitivity over China observed from the Ozone Monitoring Instrument[J]. J. Geophys. Res.-Atmos., 2015,120(14):7229-46.
[20] Muller J F, Stavrakou T. Inversion of CO and NO_x emissions using the adjoint of the IMAGES model[J]. Atmos Chem Phys, 2005,5:1157-86.
[21] De Smedt I, Muller J F, Stavrakou T, et al. Twelve years of global observations of formaldehyde in the troposphere using GOME and SCIAMACHY sensors[J]. Atmos. Chem. Phys., 2008,8(16):4947-63.
[22] De Smedt I, Van Roozendael M, Stavrakou T, et al. Improved retrieval of global tropospheric formaldehyde columns from GOME-2/MetOp-A addressing noise reduction and instrumental degradation issues[J]. Atmos. Meas. Tech., 2012,5(11):2933-49.
[23] De Smedt I, Stavrakou T, Hendrick F, et al. Diurnal, seasonal and long-term variations of global formaldehyde columns inferred from combined OMI and GOME-2observations[J]. Atmos. Chem. Phys., 2015,15(21):12519-45.
[24] Li M, Zhang Q, Kurokawa J, et al. MIX:a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP[J]. Atmos. Chem. Phys., 2017,17(2):935-63.
[25] 倪深海,白玉慧.BP神经网络模型在地下水水质评价中的应用[J]. 系统工程理论与实践, 2000,(8):124-127.Ni S H, Bai Y H. Application of BP neural network model in groundwater quality evaluation[J]. Systems Engineering-Theory & Practice, 2000,(8):124-127.
[26] 魏加华,李宇,张建立,等.人工神经网络在水源地影响评价中的应用[J]. 地球学报, 2001,(3):283-288.Wei J H, Li Y, Zheng J L, et al. The application of BP neural network to the evaluation of wellhead field influence[J]. Acta Geoscientica Sinica, 2001,(3):283-288.
[27] Li X M, Xiao R B, Yuan S H, et al. Urban total ecological footprint forecasting by using radial basis function neural network:A case study of Wuhan city, China[J]. Ecological Indicators, 2010,10(2):241-8.
[28] 刘金龙,马程,王阳,等.基于径向基函数网络的京津冀地区生态系统服务脆弱性评估[J]. 北京大学学报(自然科学版), 2013,49(6):1040-1046.Liu J L, Ma C, Wang Y, et al. Assessing the vulnerability of ecosystem services of Beijing-Tianjin-Hebei Area based on radial basis function network[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2013,49(6):1040-1046.
[29] 徐富强,刘相国.基于优化的RBF神经网络的变量筛选方法[J]. 计算机系统应用, 2012,21(3):206-8.Xu F Q, Liu X G. Variables screening methods based on the optimization of RBF neural network[J]. Computer Systems & Applications, 2012,21(3):206-8.
[30] 黄嫣旻,魏海萍,段玉森,等.上海世博会环境空气质量状况和原因分析[J]. 中国环境监测, 2013,29(5):58-63.Huang Y M, Wei H P, Duan Y S, et al. Ambient air quality status and reason analysis of Shanghai World Expo[J]. Environmental Monitoring in China, 2013,29(5):58-63.
[31] 古珊,杨显玉,吕世华,等.基于OMI数据的四川盆地对流层甲醛时空分布特征.环境科学学报, 2019,39(9):2860-2872.Gu S, Yang X Y, Lü S H, et al. Spatiotemporal variations in OMI satellite-based tropospheric formaldehyde in Sichuan Basin. Acta Scientiae Circumstantiae, 2019,39(9):2860-2872.
[32] Shen L, Jacob D J, Zhu L, et al. The 2005~2016 Trends of formaldehyde columns over China observed by satellites:Increasing anthropogenic emissions of volatile organic compounds and decreasing agricultural fire emissions[J]. Geophys. Res. Lett., 2019, 46(8):4468-75.
[33] 李金珂,曹静.集中供暖对中国空气污染影响的实证研究[J]. 经济学报, 2017,4(4):138-150.Li J K, Cao J. Empirical analysis of the effect of central heating on air pollution in Chian[J]. China Journal of Economics, 2017,4(4):138-150.
[34] 陈智海,杨显玉,古珊,等.基于OMI数据研究中国对流层甲醛时空分布特征及变化趋势.环境科学学报, 2019,39(9):2852-2859.Chen Z H, Yang X Y, Gu S, et al. Spatiotemporal variations and trend analysis of OMI satellite-based tropospheric formaldehyde over China. Acta Scientiae Circumstantiae, 2019,39(9):2852-2859.
[35] Hao J M, He D Q, Wu Y, et al. A study of the emission and concentration distribution of vehicular pollutants in the urban area of Beijing[J]. Atmos. Environ., 2000,34(3):453-65.
[36] 高文康,唐贵谦,辛金元,等.京津冀地区严重光化学污染时段O₃的时空分布特征[J]. 环境科学研究, 2016,29(5):654-663.Gao W K, Tang G Q, Xin J Y, et al. Spatial-Temporal Variations of Ozone during Severe Photochemical Pollution over the Beijing-Tianjin-Hebei Region[J]. Research of Environmental Sciences, 2016, 29(5):654-663.