基于TROPOMI的淮河流域大气NO<sub>2</sub>干沉降估算

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摘要基于Sentinel-5P卫星TROPOMI数据,利用随机森林方法反演2018~2020年淮河流域地面NO₂浓度,采用推算法获得淮河流域2018~2020年NO₂干沉降通量,并通过划分不同集水区(水域、农田、城区和植被覆盖区)估算大气NO₂干沉降对淮河流域水体氮素的贡献.结果显示,卫星反演地面NO₂浓度与地面站点实测资料一致性较高,相关系数(R)为0.94,平均绝对误差(MAE)为2.7,均方根误差(RSME)为4.1.淮河流域地面NO₂浓度和NO₂干沉降通量均有明显的季节变化,春夏秋冬4个季节地面NO₂平均浓度分别为13.7,12.2,17.6,23.1µg/m³;NO₂平均干沉降通量分别为1.25,1.13,1.61,2.13kg N/(hm²·a).淮河流域地面NO₂浓度和干沉降通量均表现为南北部高,东西部低.农田区域NO₂干沉降对流域水体氮素的贡献最大,占比83.47%.2019年淮河流域大气NO₂干沉降总量为1.34×10⁵t,对水体氮素的贡献为1.36×10⁴t N;2020年大气NO₂干沉降总量为1.25×10⁵t,对水体氮素的贡献为1.18×10⁴t N.

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	刘佳瑜
	王钰
	丘仲锋
	赵冬至
	田野
	武燕
	张渊智

关键词 ： NO₂干沉降, TROPOMI, 随机森林模型, 淮河流域, 氮素贡献

Abstract：Based on Sentinel-5P TROPOMI, we retrieved the surface NO₂ concentration in the Huaihe River Basin (HRB) during 2018~2020 by using random forest (RF), and the estimation method was used to obtain the dry deposition flux of NO₂. We then divided the HRB into four areas (water, agriculture, urban, and vegetation) to estimate the nitrogen contribution of atmospheric NO₂ dry deposition to the HRB water. The results show that the model simulation result is in good agreement with the measured data, achieving a correlation coefficient (R) of 0.94, a mean absolute error (MAE) of 2.7, and a root mean square error (RSME) of 4.1 in surface NO₂ estimation. There is a clear seasonal variation in the NO₂ dry deposition flux and surface NO₂ concentration in the HRB. The average surface NO₂ concentration in spring, summer, autumn, and winter was 13.7, 12.2, 17.6, 23.1µg/m³; the average dry deposition flux of NO₂ was 1.25, 1.13, 1.61, 2.13kg N/(hm²·a). The surface NO₂ concentration and dry deposition flux in HRB are higher in the north and south, and lower in the east and west. NO₂ dry deposition in agriculture was a major contributor with 83.47%. In 2019, the overall atmospheric NO₂ dry deposition in HRB was 1.34×10⁵t, and its contribution to water nitrogen was 1.36×10⁴t N; In 2020, the overall atmospheric NO₂ dry deposition was 1.25×10⁵t, and the contribution of NO₂ to water nitrogen is 1.18×10⁴t N.

Key words： NO₂ dry deposition TROPOMI random forest Huaihe River Basin nitrogen contribution

收稿日期: 2023-09-05

PACS:

X511

基金资助:国家自然科学基金(41976165)

通讯作者: 丘仲锋,教授,zhongfeng.qiu@nuist.edu.cn E-mail: zhongfeng.qiu@nuist.edu.cn

引用本文:

刘佳瑜, 王钰, 丘仲锋, 赵冬至, 田野, 武燕, 张渊智. 基于TROPOMI的淮河流域大气NO₂干沉降估算[J]. 中国环境科学, 2024, 44(4): 1966-1974. LIU Jia-yu, WANG Yu, QIU Zhong-feng, ZHAO Dong-zhi, TIAN Ye, WU Yan, ZHANG Yuan-zhi. Estimation of atmospheric NO₂dry deposition in Huaihe River Basin based on TROPOMI. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(4): 1966-1974.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2024/V44/I4/1966

[1] Zhao W, Zhao Y, Ma M, et al. Long-term variability in base cation, sulfur and nitrogen deposition and critical load exceedance of terrestrial ecosystems in China[J]. Environmental Pollution, 2021, 289:117974.
[2] 谢迎新,张淑利,冯伟,等.大气氮素沉降研究进展[J]. 中国生态农业学报, 2010,18(4):897-904. Xie Y X, Zhang S L, Feng W, et al. Review of atmospheric nitrogen deposition research[J]. Chinese Journal of Eco-Agriculture, 2010, 18(4):897-904.
[3] Zhan X, Bo Y, Zhou F, et al. Evidence for the importance of atmospheric nitrogen deposition to eutrophic Lake Dianchi, China[J]. Environmental Science & Technology, 2017,51(12):6699-6708.
[4] Geng J, Li H, Chen D, et al. Atmospheric nitrogen deposition and its environmental implications at a headwater catchment of Taihu Lake Basin, China[J]. Atmospheric Research, 2021,256:105566.
[5] Zhang Y, Liu C, Liu X, et al. Atmospheric nitrogen deposition around the Dongting Lake, China[J]. Atmospheric Environment, 2019,207:197-204.
[6] Jiang X, Gao G, Deng J, et al. Nitrogen concentration response to the decline in atmospheric nitrogen deposition in a hypereutrophic lake[J]. Environmental Pollution, 2022,300:118952.
[7] 马胜,杨晓峰,李紫薇.基于GOME-2卫星遥感数据估算近海海域氮沉降通量的方法研究[J]. 海洋环境科学, 2012,31(2):272-276. Ma S, Yang X F, Li Z W. An approach of estimating the atmospheric deposition of nitrogen to the coastal water using GOME2 satellite data[J]. Marine Environmental Science, 2012,31(2):272-276.
[8] Nowlan C R, Martin R V, Philip S, et al. Global dry deposition of nitrogen dioxide and sulfur dioxide inferred from space-based measurements[J]. Global Biogeochemical Cycles, 2014,28(10):1025-1043.
[9] Liu L, Zhang X, Xu W, et al. Estimation of monthly bulk nitrate deposition in China based on satellite NO₂measurement by the Ozone Monitoring Instrument[J]. Remote Sensing of Environment, 2017, 199:93-106.
[10] Kim M, Brunner D, Kuhlmann G. Importance of satellite observations for high-resolution mapping of near-surface NO₂ by machine learning[J]. Remote Sensing of Environment, 2021,264:112573.
[11] 卢鋆镆,曾穗平,曾坚,等.基于随机森林的高分辨率PM_2.5浓度时空变化模拟——以中原城市群核心区为例[J]. 中国环境科学, 2023,43(7):3299-3311. Lu J M, Zeng S P, Zeng J, et al. High resolution simulation of temporal and spatial variation of PM_2.5 concentration based on random forest——A case study of Central Plains Urban Agglomeration Core Area[J]. China Environmental Science, 2023,43(7):3299-3311.
[12] 耿冰,孙义博,曾巧林,等.基于深度学习方法的PM_2.5精细化时空估算模型[J]. 中国环境科学, 2021,41(8):3502-3510. Geng B, Sun Y B, Zeng Q L, et al. Refined spatiotemporal estimation model of PM_2.5 based on deep learning method. CHINA[J]. China Environmental Science, 2021,41(8):3502-3510.
[13] Xu J, Lindqvist H, Liu Q, et al. Estimating the spatial and temporal variability of the ground-level NO₂ concentration in China during 2005~2019 based on satellite remote sensing[J]. Atmospheric Pollution Research, 2021,12(2):57-67.
[14] Chi Y, Fan M, Zhao C, et al. Ground-level NO₂ concentration estimation based on OMI tropospheric NO₂ and its spatiotemporal characteristics in typical regions of China[J]. Atmospheric Research, 2021,264:105821.
[15] Ghahremanlooo M, Lops Y, Choi Y, et al. Deep learning estimation of daily ground-level NO₂ concentrations from remote sensing data[J]. Journal of Geophysical Research:Atmospheres, 2021,126(21):e2021J-e34925J.
[16] 王景芝,袁金国,郭豪,等.基于TROPOMI的京津冀NO₂柱浓度时空变化分析[J]. 环境科学与技术, 2022,45(3):21-31. Wang J Z, Yuan J G, Guo H, et al. Spatio-temporal analysis of NO₂ column density in Beijing-Tianjin-Hebei region based on TROPOMI[J]. Environmental Science & Technology, 2022,45(3):21-31.
[17] Zhang C, Guo H, Huang H, et al. Atmospheric nitrogen deposition and its responses to anthropogenic emissions in a global hotspot region[J]. Atmospheric Research, 2021,248:105137.
[18] Yu G, Jia Y, He N, et al. Stabilization of atmospheric nitrogen deposition in China over the past decade[J]. Nature Geoscience, 2019,12(6):424-429.
[19] 尹兴,张丽娟,刘学军,等.河北平原城市近郊农田大气氮沉降特征[J]. 中国农业科学, 2017,50(4):698-710. Ying X, Zhang L J, Liu X J, et al. Nitrogen deposition in suburban croplands of Hebei Plain[J]. Scientia Agricultura Sinica, 2017,50(4):698-710.
[20] 廖曼,马腾,郑倩琳,等.淮河流域农业生态系统中地下水体氮源追溯[J]. 中国生态农业学报(中英文), 2019,27(5):665-676. Liao M, Ma T, Zheng Q L, et al. Tracing groundwater nitrogen source in Huai River Basin agro-ecosystem[J]. Chinese Journal of Eco-Agriculture, 2019,27(5):665-676.
[21] Li L, Wu J. Spatiotemporal estimation of satellite-borne and ground-level NO₂ using full residual deep networks[J]. Remote Sensing of Environment, 2021,254:112257.
[22] S Piramanayagam, W Schwartzkopf, F W Koehler, et al. Classification of remote sensed images using random forests and deep learning framework[C]. Proc. SPIE, 2016,10004.
[23] Al Yammahi A, Aung Z. Forecasting the concentration of NO₂ using statistical and machine learning methods:A case study in the UAE[J]. Heliyon, 2023,9(2):e12584.
[24] Adon M, Galy-Lacaux C, Delon C, et al. Dry deposition of nitrogen compounds (NO₂, HNO₃, NH₃), sulfur dioxide and ozone in west and central African ecosystems using the inferential method[J]. Atmos. Chem. Phys., 2013,13(22):11351-11374.
[25] Jia Y, Yu G, Gao Y, et al. Global inorganic nitrogen dry deposition inferred from ground -and space-based measurements[J]. Scientific Reports, 2016,6(1):19810.
[26] Liu T, Abd-Elrahman A, Morton J, et al. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system[J]. GIScience & Remote Sensing, 2018,55(2):243-264.
[27] Liu M, Wang M, Wang J, et al. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification:Application to the recognition of orange beverage and Chinese vinegar[J]. Sensors and Actuators B:Chemical, 2013,177:970-980.
[28] Zhang X Y, Lu X H, Liu L, et al. Dry deposition of NO₂ over China inferred from OMI columnar NO₂ and atmospheric chemistry transport model[J]. Atmospheric Environment, 2017,169:238-249.
[29] Gu J, Chen L, Yu C, et al. Ground-level NO₂ concentrations over China inferred from the satellite OMI and CMAQ model simulations[J]. Remote Sensing, 2017,9(6):519-536.
[30] Fu Y, Wang W, Han M, et al. Atmospheric dry and bulk nitrogen deposition to forest environment in the North China Plain[J]. Atmospheric Pollution Research, 2019,10(5):1636-1642.
[31] 刘平,刘学军,骆晓声,等.山西北部农村区域大气活性氮沉降特征[J]. 生态学报, 2016,36(17):5353-5359. Liu P, Liu X J, Luo X S, et al. The atmospheric deposition characteristics of reactive nitrogen (N_r) species in Shuozhou area[J]. Acta Ecologica Sinica, 2016,36(17):5353-5359.
[32] Li R, Cui L, Zhao Y, et al. Long-term trends of ambient nitrate (NO₃^-) concentrations across China based on ensemble machine-learning models[J]. Earth Syst. Sci. Data, 2021,13(5):2147-2163.
[33] Chen K, Wang M, Huang C, et al. Air pollution reduction and mortality benefit during the COVID-19 outbreak in China[J]. The Lancet Planetary Health, 2020,4(6):e210-e212.
[34] Ding J, Van Der A R J, Eskes H J, et al. NO_x emissions reduction and rebound in China due to the COVID-19 crisis[J]. Geophysical Research Letters, 2020,47(19):e2020G-e89912G.
[35] Zheng B, Tong D, Li M, et al. Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions[J]. Atmos. Chem. Phys., 2018,18(19):14095-14111.
[36] Wu Q, Li T, Zhang S, et al. Evaluation of NO_x emissions before, during, and after the COVID-19lockdowns in China:A comparison of meteorological normalization methods[J]. Atmospheric Environment, 2022,278:119083.
[37] 王锦旗,宋玉芝,黄进.大气氮沉降对流域总贡献量估算方法研究[J]. 江苏农业科学, 2020,48(11):246-250. Wang J Q, Song Y Z, Huang J. Estimation method of total contribution of atmospheric nitrogen deposition to watershed[J]. Jiangsu Agricultural Sciences, 2020,48(11):246-250.
[38] 张欢,李恒鹏,李新艳,等.太湖流域典型农业区氮平衡时间变化特征及驱动因素[J]. 土壤通报, 2014,45(5):1119-1129. Zhang H, Li H P, Li X Y, et al. Temporal changes of nitrogen balance and their driving factors in typical agricultural area of Lake Tai Basin[J]. Chinese Journal of Soil Science, 2014,45(5):1119-1129.
[39] 段雷,郝吉明,谢绍东,等.用稳态法确定中国土壤的硫沉降和氮沉降临界负荷[J]. 环境科学, 2002,23(2):7-12. Duan L, Hao J M, Xie S D, et al. Estimating critical loads of sulfur and nitrogen for Chinese soils by steady state method[J]. Environmental Science, 2002,23(2):7-12.
[40] 郝吉明,齐超龙,段雷,等.用SMB法确定中国土壤的营养氮沉降临界负荷[J]. 清华大学学报(自然科学版), 2003,43(6):849-853. Hao J M, Qi C L, Duan L, et al. Evaluating critical loads of nutrient nitrogen on soils in China using the SMB method[J]. Journal of Tsinghua University (Science and Technology), 2003,43(6):849-853.
[41] 段雷,黄永梅,郝吉明,等.中国植被对氮和盐基阳离子吸收速率及其在土壤酸化中的作用[J]. 环境科学, 2002,23(3):68-74. Duan L, Huang Y M, Hao J M, et al. Vegetation uptake of nitrogen and base cation in China and its role in soil acidification[J]. Environmental Science, 2002, 23(3):68-74.
[42] Zhao Y, Zhang L, Chen Y, et al. Atmospheric nitrogen deposition to China:A model analysis on nitrogen budget and critical load exceedance[J]. Atmospheric Environment, 2017,153:32-40.