基于垂直订正和湿度订正方法估算川渝地区逐小时PM<sub>2.5</sub>

赵青琳; 曾巧林; 罗彬; 许丽萍; 范萌; 陈良富; 罗小波

doi:10.11834/jrs.20220232

大气遥感 | 浏览量 : 0 下载量: 194 CSCD: 2 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

基于垂直订正和湿度订正方法估算川渝地区逐小时PM_2.5
Estimation of hourly PM_2.5 concentration by using vertical and humidity correction methods in Sichuan-Chongqing
2022年26卷第10期页码：1946-1962
纸质出版日期： 2022-10-07 ，
DOI： 10.11834/jrs.20220232

扫描看全文

赵青琳，曾巧林，罗彬，许丽萍，范萌，陈良富，罗小波.2022.基于垂直订正和湿度订正方法估算川渝地区逐小时PM_2.5.遥感学报，26（10）： 1946-1962

Zhao Q L，Zeng Q L，Luo B，Xu L P，Fan M，Chen L F and Luo X B. 2022. Estimation of hourly PM_2.5 concentration by using vertical and humidity correction methods in Sichuan-Chongqing. National Remote Sensing Bulletin， 26（10）：1946-1962
赵青琳，曾巧林，罗彬，许丽萍，范萌，陈良富，罗小波.2022.基于垂直订正和湿度订正方法估算川渝地区逐小时PM_2.5.遥感学报，26（10）： 1946-1962 DOI： 10.11834/jrs.20220232.

Zhao Q L，Zeng Q L，Luo B，Xu L P，Fan M，Chen L F and Luo X B. 2022. Estimation of hourly PM_2.5 concentration by using vertical and humidity correction methods in Sichuan-Chongqing. National Remote Sensing Bulletin， 26（10）：1946-1962 DOI： 10.11834/jrs.20220232.

摘要

针对PM

2.5

污染比较严重的川渝地区，本研究利用日本静止葵花卫星（Himawari-8）反演的气溶胶光学厚度（AOD）进行垂直订正和湿度订正估算川渝地区2017年—2018年09：00—16：00时的PM

2.5

浓度。首先，基于气象观测的能见度（

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906173&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906170&type=

3.21733332

2.28600001

）数据与消光系数

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906179&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906177&type=

7.78933382

3.89466691

之间的关系，引入“标高（

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906185&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906182&type=

3.21733332

）”对气溶胶光学厚度进行垂直订正；其次，根据匹配的气象和环境监测数据对每一个站点逐一拟合1—12月份的吸湿增长因子，并采用反距离加权空间插值（IDW）方法构建吸湿订正因子网格，从而进行湿度订正估算。研究结果表明，经垂直订正和湿度订正后，相比AOD与PM

2.5

之间的相关性，“干”消光系数

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906191&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906188&type=

4.48733330

3.21733332

与PM

2.5

的相关性显著提高，相关系数（

）由0.12—0.45提高至0.32—0.69；验证09：00至16：00时卫星估算结果，相关系数（

）均大于0.7，均方根误差（RMSE）为18.59±2.26 μg/m

；提取所有观测站点进行验证，

=0.82，RMSE=18.64 μg/m

。

Abstract

Together with the sustained and rapid development of China’s economy in the past few years

the continuous improvement of the national economy

and the trend of urbanization

is the unceasing decline of China’s air quality. As a result

frequent occurrence of haze pollution weather has seriously affected people’s health. As the main pollutant of haze

2.5

has become a major concern of all people. Studies have shown that people exposed to PM

2.5

for a long time are likely to suffer from respiratory and cardiovascular diseases

and even die prematurely. Therefore

it is of particular importance to monitor and evaluate PM

2.5

effectively and accurately. According to the requirements of national development

Sichuan Province and Chongqing City (hereinafter referred to as Sichuan and Chongqing Area) will work together to build a two-city economic circle in Chengdu-Chongqing Area. Therefore

it is urgent to study and evaluate the regional atmospheric environmental air quality. However

due to the unique topography and meteorological conditions in Sichuan and Chongqing

estimating the near-surface PM

2.5

concentration by using satellite remote sensing method is difficult. Considering the complicated topography in Sichuan and Chongqing Area

“Elevation (

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906196&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906194&type=

3.55599999

3.04800010

)” was introduced for vertical correction of regional Aerosol Optical Depth. Moreover

considering the large difference of humidity and pollutant emission in different months

the hygroscopic correction factor grid was constructed for humidity correction by fitting the hygroscopic growth factor month by month and site by site. Therefore

first

the retrieval accuracy of AOD of Japanese geostationary Himawari satellite (Himawari-8) was verified in this study. Second

the vertical correction and humidity correction methods were used to estimate the hourly near-surface PM

2.5

concentration from 09:00 to 16:00 in 2017—2018 in the Sichuan-Chongqing Area

and the accuracy was verified. The area PM

2.5

concentration is helpful for the detailed analysis of pollutant generation

migration and dissipation hour by hour

which provides effective data support for regional pollution prevention and control. We arrive at the following conclusions: (1) Owing to the large differences in the hygroscopic growth characteristics of aerosol particles in different regions and time ranges

the four stations

namely

Ziyuan Station in Chengdu City

Rongxian Administrative Center Station in Zigong City

Shangqing Temple Station in Chongqing City

and Jingtan 2nd Road Station in Chongqing City

are selected to analyze the temporal and spatial differences in aerosol hygroscopic growth characteristics. Results show that the aerosol hygroscopic growth capacity of different sites in the same month is different. The aerosol hygroscopic growth capacity of the same site in different months is also different

and the relative humidity of different sites at different times changes greatly. Therefore

the hygroscopic correction factor grid constructed must be used by fitting the hygroscopic growth factor month by month and site by site during the humidity correction to estimate PM

2.5

. (2) Compared with PM

2.5

and AOD

the correlation coefficient between PM

2.5

and

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906202&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=36906199&type=

3.97933316

3.04800010

has significantly improved after vertical correction and humidity correction; it increased from 0.12—0.45 to 0.32—0.69. The correlation coefficient between the satellite estimated value and ground-based observation value is relatively high (

=0.82)

and the RMSE is 18.64 μg/m

which is a good estimation result. A comparison of the hourly and monthly scales shows that the estimation results from the afternoon are better than that from the morning

and that from autumn and winter are better than that from spring and summer. (3) According to the annual average spatial distribution of PM

2.5

in 2017 and 2018

the concentration of PM

2.5

in Sichuan and Chongqing Area is the highest in winter

lowest in summer

followed by that in spring and autumn. The vertical correction and humidity correction methods are used to estimate PM

2.5

mass concentration in Sichuan and Chongqing Area in 2017 and 2018

with high accuracy and good estimation results. However

the topography and aerosol components are not considered in the estimation process. Therefore

the experimental results still needs to be improved.

关键词

AODPM2.5垂直订正湿度订正川渝地区

Keywords

AODPM2.5vertical correctionhumidity correctionSichuan-Chongqing

references

Chen H, Li Q, Zhang Y H, Zhou C Y and Wang Z T. 2016. Estimations of PM2.5 concentrations based on the method of geographically weighted regression. Acta Scientiae Circumstantiae, 36(6): 2142-2151

陈辉, 厉青, 张玉环, 周春艳, 王中挺. 2016. 基于地理加权模型的我国冬季PM2.5遥感估算方法研究. 环境科学学报, 36(6): 2142-2151 [DOI: 10.13671/j.hjkxxb.2015.0780http://dx.doi.org/10.13671/j.hjkxxb.2015.0780]

Dominici F, Peng R D, Bell M L, Pham L, McDermott A, Zeger S L and Samet J M. 2006. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. The Journal of the American Medical Association, 295(10): 1127 [DOI: 10.1001/jama.295.10.1127http://dx.doi.org/10.1001/jama.295.10.1127]

Du Z H, Wu S S, Wang Z Y, Wang Y Y, Zhang F and Liu R Y. 2020. Estimating ground-level PM2.5 concentrations across China using geographically neural network weighted regression. Journal of Geo-Information Science, 22(1): 122-135

杜震洪, 吴森森, 王中一, 汪愿愿, 张丰, 刘仁义. 2020. 基于地理神经网络加权回归的中国PM2.5浓度空间分布估算方法. 地球信息科学学报, 22(1): 122-135 [DOI: 10.12082/dqxxkx.2020.190533http://dx.doi.org/10.12082/dqxxkx.2020.190533]

Fotheringham A S, Brunsdon C and Charlton M. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. New York: John Wiley & Sons.: 554-556. [Doi:10.1111/j.0002-9092.2004.600_2.xhttp://dx.doi.org/10.1111/j.0002-9092.2004.600_2.x]

Hoff R M and Christopher S A. 2009. Remote sensing of particulate pollution from space: have we reached the promised land?. Journal of the Air and Waste Management Association, 59(6): 645-675 [DOI: 10.3155/1047-3289.59.6.645http://dx.doi.org/10.3155/1047-3289.59.6.645]

Hu Z Y. 2009. Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease. International Journal of Health Geographics, 8(1): 27 [DOI: 10.1186/1476-072X-8-27http://dx.doi.org/10.1186/1476-072X-8-27]

Husar R B, Husar J D and Martin L. 2000. Distribution of continental surface aerosol extinction based on visual range data. Atmospheric Environment, 34(29/30): 5067-5078 [DOI: 10.1016 /S1352-2310(00)00324-1http://dx.doi.org/10.1016/S1352-2310(00)00324-1]

Koschmieder H. Theorieder horizontalen sichtweite ii: Kontrast und sichtweite beitrage zurphysik derfreien. Beiträge Zur Physik Der Freien Atmosphäre 1925, 12: 171-181.

Kotchenruther R A and Hobbs P V. 1998. Humidification factors of aerosols from biomass burning in Brazil. Journal of Geophysical Research: Atmospheres, 103(D24): 32081-32089 [DOI: 10.1029/98jd00340http://dx.doi.org/10.1029/98jd00340]

Koelemeijer R B A, Homan C D, Matthijsen J. 2006. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmospheric Environment, 40(27): 5304–5315[DOI: 10.1016/j.atmosenv.2006.04.044http://dx.doi.org/10.1016/j.atmosenv.2006.04.044]

Li R, Mei X, Chen L F, Wang Z F, Jing Y Y and Wei L F. 2020. Influence of spatial resolution and retrieval frequency on applicability of satellite-predicted PM2.5 in Northern China. Remote Sensing, 12(4): 736 [DOI: 10.3390/rs12040736http://dx.doi.org/10.3390/rs12040736]

Liu X G, Cheng Y F, Zhang Y H, Jung J, Sugimoto N, Chang S Y, Kim Y J, Fan S J and Zeng L M. 2008. Influences of relative humidity and particle chemical composition on aerosol scattering properties during the 2006 PRD campaign. Atmospheric Environment, 42(7): 1525-1536 [DOI: 10.1016/j.atmosenv.2007.10.077http://dx.doi.org/10.1016/j.atmosenv.2007.10.077]

Ma Z W, Liu R Y, Liu Y and Bi J. 2019. Effects of air pollution control policies on PM2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective. Atmospheric Chemistry and Physics, 19(10): 6861-6877 [DOI: 10.5194/acp-19-6861-2019http://dx.doi.org/10.5194/acp-19-6861-2019]

Mei L L, Strandgren J, Rozanov V, Vountas M, Burrows J P and Wang Y J. 2019. A study of the impact of spatial resolution on the estimation of particle matter concentration from the aerosol optical depth retrieved from satellite observations. International Journal of Remote Sensing, 40(18): 7084-7112 [DOI: 10.1080/01431161.2019.1601279http://dx.doi.org/10.1080/01431161.2019.1601279]

Nessler R, Weingartner E and Baltensperger U. 2005. Effect of humidity on aerosol light absorption and its implications for extinction and the single scattering albedo illustrated for a site in the lower free troposphere. Journal of Aerosol Science, 36(8): 958-972 [DOI: 10.1016/ j.jaerosci.2004.11.012http://dx.doi.org/10.1016/j.jaerosci.2004.11.012]

Ran X X. 2013. Chongqing: continue to implement “blue sky action” and create a new record of blue sky in the main city. Environmental Protection, 41(5): 34-35

冉小霞. 2013. 重庆: 继续实施“蓝天行动”再创主城蓝天历史新高. 环境保护, 41(5): 34-35 [DOI: 10.14026/j.cnki.0253-9705.2013.05.012http://dx.doi.org/10.14026/j.cnki.0253-9705.2013.05.012]

Strawa A W, Chatfield R B, Legg M, Scarnato B and Esswein R. 2013. Improving retrievals of regional fine particulate matter concentrations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) multisatellite observations. Journal of the Air and Waste Management Association, 63(12): 1434-1446 [DOI: 10.1080/10962247.2013.822838http://dx.doi.org/10.1080/10962247.2013.822838]

Sun Y B, Zeng Q L, Geng B, Lin X W, Sude B and Chen L F. 2019. Deep learning architecture for estimating hourly ground-level PM2.5 using satellite remote sensing. IEEE Geoscience and Remote Sensing Letters, 16(9): 1343-1347 [DOI: 10.1109/LGRS.2019.2900270http://dx.doi.org/10.1109/LGRS.2019.2900270]

Tao J H, Zhang M G, Chen L F, Wang Z F, Su L, Ge C, Han X and Zou M M. 2013. A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness. Science China: Earth Sciences, 56(8): 1422-1433

陶金花, 张美根, 陈良富, 王子峰, 苏林, 葛萃, 韩霄, 邹铭敏. 2013. 一种基于卫星遥感AOT估算近地面颗粒物的方法. 中国科学: 地球科学, 43(1): 143-154 [DOI: 10.1007/s11430-012-4503-3http://dx.doi.org/10.1007/s11430-012-4503-3]

Tao M H, Chen L F, Su L and Tao J H. 2012. Satellite observation of regional haze pollution over the North China Plain. Journal of Geophysical Research: Atmospheres, 117(D12): D12203 [DOI: 10.1029/2012JD017915http://dx.doi.org/10.1029/2012JD017915]

Van D A, Martin R V, Brauer M, Kahn R, Levy R, Verduzco C and Villeneuve P J. 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environmental Health Perspectives, 118(6): 847-855 [DOI: 10.1289/ehp.0901623http://dx.doi.org/10.1289/ehp.0901623]

Wang Q X, Zeng Q L, Tao J H, Sun L, Zhang L, Gu T Y, Wang Z F and Chen L F. 2019. Estimating PM2.5 concentrations based on MODIS AOD and NAQPMS data over Beijing-Tianjin-Hebei. Sensors, 19(5): 1207 [DOI: 10.3390/s19051207http://dx.doi.org/10.3390/s19051207]

Wang X Y, Guo Y M, Li G X, Zhang Y J, Westerdahl D, Jin X B, Pan X C and Chen L F. 2016. Spatiotemporal analysis for the effect of ambient particulate matter on cause-specific respiratory mortality in Beijing, China. Environmental Science and Pollution Research, 23(11): 10946-10956 [DOI: 10.1007/s11356-016-6273-5http://dx.doi.org/10.1007/s11356-016-6273-5]

Wang Y. 2018. Aerosol Optical Depth Retrieval Research over Land for the VIIRS and AHI Satellite Data. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences

汪洋. 2018. 基于VIIRS和AHI数据的陆地气溶胶反演算法研究. 北京: 中国科学院遥感与数字地球研究所

Wang Z F, Chen L F, Tao J H, Liu Y, Hu X F and Tao M H. 2014. An empirical method of RH correction for satellite estimation of ground-level PM concentrations. Atmospheric Environment, 95: 71-81 [DOI: 10.1016/j.atmosenv.2014.05.030http://dx.doi.org/10.1016/j.atmosenv.2014.05.030]

Wang Z F, Chen L F, Tao J H, Zhang Y and Su L. 2010. Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. Remote Sensing of Environment, 114(1): 50-63 [DOI: 10.1016/j.rse.2009.08.009http://dx.doi.org/10.1016/j.rse.2009.08.009]

Wang Z F, Zeng Q L, Chen L F, Tao J H, Fan M and Zhang Z Y. 2019. Research progress of methodology and applications of PM2.5 estimation using satellite remote sensing. Environmental Monitoring and Forewarning, 11(5): 33-38

王子峰, 曾巧林, 陈良富, 陶金花, 范萌, 张泽宇. 2019. 利用卫星遥感数据估算PM2.5浓度的应用研究进展. 环境监控与预警, 11(5): 33-38 [DOI: 10.3969/j.issn.1674-6732.2019.05.006http://dx.doi.org/10.3969/j.issn.1674-6732.2019.05.006]

Yang F K, Wang Y, Tao J H, Wang Z F, Fan M, De Leeuw G and Chen L F. 2018. Preliminary investigation of a new AHI Aerosol Optical Depth (AOD) retrieval algorithm and evaluation with multiple source AOD measurements in China. Remote Sensing, 10(5): 748 [DOI: 10.3390/rs10050748http://dx.doi.org/10.3390/rs10050748]

Yang L. 2018. The Characteristics of Air Pollution in Typical Cities of Sichuan Basin and the Influence of Precipitation on It. Chengdu: Chengdu University of Information Technology

杨柳. 2018. 四川盆地典型城市空气污染特征及降水对其影响的研究. 成都: 成都信息工程大学

Zeng Q L. 2018. Improvement and Applicability Evaluation of PM 2.5 Satellite Estimation Methods. Beijing: University of Chinese Academy of Sciences

曾巧林. 2018. PM2.5卫星估算方法改进与适用性评价. 北京：中国科学院大学

Zeng Q L, Chen L F, Zhu H, Wang Z F, Wang X H, Zhang L, Gu T Y, Zhu G Y and Zhang Y. 2018. Satellite-based estimation of hourly PM2.5 concentrations using a vertical-humidity correction method from Himawari-AOD in Hebei. Sensors, 18(10): 3456 [DOI: 10.3390/s18103456http://dx.doi.org/10.3390/s18103456]

Zeng Q L, Tao J H, Chen L F, Zhu H, Zhu S Y and Wang Y. 2020. Estimating ground-level particulate matter in five regions of China using aerosol optical depth. Remote Sensing, 12(5): 881 [DOI: 10.3390/rs12050881http://dx.doi.org/10.3390/rs12050881]

Zhang D Y. 2010. The ambient air quality trend and pollution control measures in Chongqing's city proper. Environmental Science and Technology, 33(S1): 43-45, 57

张大元. 2010. 重庆主城区空气质量变化趋势与污染控制对策. 环境科学与技术, 33(S1): 43-45, 57 [DOI: CNKI:SUN:FJKS.0.2010-S1-014http://dx.doi.org/CNKI:SUN:FJKS.0.2010-S1-014]

Zhang L, Zhang W W and Liu J R. 2019. Effect of PM2.5 exposure on the microecology of human pharynx. Chinese Archives of Otolaryngology-Head and Neck Surgery, 26(7): 391-393

张莉, 张雯雯, 刘佳荣. 2019. PM2.5暴露对人类咽部微生态的影响. 中国耳鼻咽喉头颈外科, 26(7): 391-393 [DOI: 10.16066/j.1672-7002.2019.07.014http://dx.doi.org/10.16066/j.1672-7002.2019.07.014]

Zhang X Y, Chu Y Y, Wang Y X and Zhang K. 2018. Predicting daily PM2.5, concentrations in Texas using high-resolution satellite aerosol optical depth. Science of the Total Environment, 631-632: 904-911 [DOI: 10.1016/j.scitotenv.2018.02.255http://dx.doi.org/10.1016/j.scitotenv.2018.02.255]

Zhu H, Wang Y Q, Tao J H, Zhang Z L, Wang Z F, Zeng Q L and Chen L F. 2019. Analysis of aerosol hygroscopic growth based on meteorological factors: a case study of Zhejiang Province. Climatic and Environmental Research, 24(2): 186-198

祝好, 王永前, 陶金花, 张自力, 王子峰, 曾巧林, 陈良富. 2019. 基于气象要素的气溶胶吸湿增长分析: 以浙江省为例. 气候与环境研究, 24(2): 186-198 [DOI: 10.3878/j.issn.1006-9585.2018.17142http://dx.doi.org/10.3878/j.issn.1006-9585.2018.17142]

文章被引用时，请邮件提醒。

提交

基于卫星遥感的近地面PM_2.5浓度反演进展

2000年—2018年中国和印度的长期PM_2.5污染暴露的疾病负担研究

基于深度学习与随机森林的PM_2.5浓度预测模型

MODIS MAIAC高分辨率气溶胶光学厚度产品在干旱区的适用性研究