浏览全部资源
扫码关注微信
Received：15 February 2017，
Accepted：26 August 2017，
Published：2018-01
移动端阅览
卫星遥感反演气溶胶光学厚度已被广泛应用于近地面空气污染遥感监测。为揭示福州地区细颗粒物污染的空间分异趋势，利用2014年—2015年的地基监测细颗粒物(PM
2.5
)浓度数据、MODIS 3 km气溶胶光学厚度(AOD)卫星数据以及GEOS-FP气象数据，分别构建了估计福州地区近地面PM
2.5
浓度的日校正模型和站点—日校正模型，并利用十折交叉验证方法对2个模型进行评价验证。结果表明：(1)日校正模型和站点—日校正模型分别能够解释福州地区PM
2.5
浓度76.2%和81.4%的变异，反演的2014年—2015年福州地区近地面PM
2.5
浓度和地面实测站点数据之间的相关性
R
2
分别为0.724(RMSE=10.993 μg·m
–3
)和0.781(RMSE=9.687 μg·m
–3
)；(2)分别针对不同下垫面环境的城市站点和县郊站点数据进行模型拟合验证，两个模型反演的PM
2.5
浓度值与地面实测值之间皆具有良好的相关性，
R
2
最高可达0.808；(3)将模型反演的PM
2.5
浓度季均值与地面实测季均值进行对比分析，结果也显示二者高度相关，据此反演的2015年福州地区年平均PM
2.5
浓度分布图可清晰地揭示福州地区PM
2.5
浓度分布的空间变化情况。由此可见，基于MODIS 3 km AOD产品和气象数据建立的近地面PM
2.5
浓度遥感估算模型能够很好地反演出福州地区近地面PM
2.5
浓度分布情况。
Remote sensing techniques offer a unique opportunity to monitor air quality and are thus crucial for the management and surveillance of the air quality of polluted megacities. MODIS Aerosol Optical Depth (AOD) products with a spatial resolution of 10 km have been widely used to monitor ground-level particulate matters. However
the demands of air quality estimation are difficult to meet in local areas due to the coarse resolution of 10 km AOD. Taking Fuzhou city as an example
this study used the newly released AOD with a spatial resolution of 3 km and meteorological data to map ground-level PM
2.5
concentrations in the city and ultimately reveal the spatial details of the PM
2.5
exposure. Two regression models
namely
the daily calibration model and site daily calibration model
were developed to estimate and map ground-level PM
2.5
concentrations in Fuzhou
China. The MODIS 3 km AOD data for 2014—2015
in situ PM
2.5
concentration data for the same period
and meteorological data of wind speed and relative humidity were used. A simple linear model was also derived and used for comparison with the two calibration models. Results showed that the PM
2.5
concentrations and AOD had an extremely low agreement when a linear fit was applied
with the
R
2
value being 0.117 and the RMSE being 19.510 μg/m
-3
. Strong correlations were obtained with the use of the daily calibration model
which yielded an
R
2
of 0.762 and RMSE of 10.146 μg/m
–3
. A relatively high degree of agreement was achieved when the site daily calibration model was used;
R
2
was 0.814
and RMSE was 8.965 μg/m
–3
. Ten-fold Cross Validation (CV) was conducted to evaluate the performance of the regression models. The CV results showed that the site daily calibration model performed better than the daily calibration model. Correlation coefficients (
R
2
) of the estimated PM
2.5
concentrations with the in situ data were 0.781 (RMSE=9.687 μg·m
–3
) and 0.724 (RMSE=10.993 μg·m
–3
). In addition
the PM
2.5
concentrations estimated by the site daily calibration model had a better agreement with the observed values for all seasons from 2014 to 2015. The
R
2
of the estimated and observed values of the seasonal average PM
2.5
concentrations for the two models were 0.999 and 0.995
respectively
indicating that both models could reflect daily variations in the relationship among AOD
meteorological data
and PM
2.5
concentrations. In this study
we proposed a daily calibration model and a site daily calibration model using the newly released MODIS 3 km AOD product and meteorological data to estimate ground-level PM
2.5
concentrations in Fuzhou
China. The daily calibration model was used to retrieve the distribution of PM
2.5
concentrations in Fuzhou
as the site effect parameters needed for the site daily calibration model is not available for every 3 km grid. Nevertheless
these two models perform similarly in PM
2.5
estimation. The spatial distribution of PM
2.5
concentrations in Fuzhou derived from the MODIS 3 km AOD exhibits high concentrations over central urban areas and low values over suburban districts. These results clearly reveal the spatial variation of PM
2.5
in the area. This study indicated that the satellite-derived model based on the MODIS 3 km AOD product could work effectively in estimating PM
2.5
concentrations on a local scale.
Bates D, Mäechler M, Bolker B M and Walker S C. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1): 1–48
陈良富, 陈水森, 钟流举, 陶金花, 王子峰. 2015. 卫星数据和地面观测结合的珠三角地区颗粒物质量浓度统计估算方法. 热带地理, 35(1): 7–12
Chen L F, Chen S S, Zhong L J, Tao J H and Wang Z F. 2015. Statistic method of particulate matter concentration based on the satellite observations combining with ground measurements in PRD. Tropical Geography, 35(1): 7–12 (
Cheng Z, Jiang J K, Fajardo O, Wang S X and Hao J M. 2013. Characteristics and health impacts of particulate matter pollution in China (2001-2011). Atmospheric Environment, 65: 186–194
Engle-Cox J A, Holloman C H, Coutant B W and Hoff R M. 2004. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric Environment, 38(16): 2495–2509
Franklin M, Zeka A and Schwartz J. 2007. Association between PM 2.5 and all-cause and specific-cause mortality in 27 US communities. Journal of Exposure Science and Environmental Epidemiology, 17(3): 279–287
Ghotbi S, Sotoudeheian S and Arhami M. 2016. Estimating urban ground-level PM 10 using MODIS 3 km AOD product and meteorological parameters from WRF model. Atmospheric Environment, 141: 333–346
Gupta P and Christopher S A. 2009. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: multiple regression approach. Journal of Geophysical Research: Atmospheres, 114(D14): D14205
贾松林, 苏林, 陶金花, 王子峰, 陈良富, 尚华哲. 2014. 卫星遥感监测近地表细颗粒物多元回归方法研究. 中国环境科学, 34(3): 565–573
Jia S L, Su L, Tao J H, Wang Z F, Chen L F and Shang H Z. 2014. A study of multiple regression method for estimating concentration of fine particulate matter using satellite remote sensing. China Environmental Science, 34(3): 565–573 (
Kaufman Y J, Wald A E, Remer L A, Gao B C, Li R R and Flynn L. 1997. The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1286–1298
Kloog I, Koutrakis P, Coull B A, Lee H J and Schwartz J. 2011. Assessing temporally and spatially resolved PM 2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmospheric Environment, 45(35): 6267–6275
Koelemeijer R B A, Homan C D and Matthijsen J. 2006. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmospheric Environment, 40(27): 5304–5315
Lee H J, Liu Y, Coull B A, Schwartz J and Koutrakis P. 2011. A novel calibration approach of MODIS AOD data to predict PM 2.5 concentrations. Atmospheric Chemistry and Physics, 11(15): 7991–8002
Levy R C, Remer L A and Dubovik O. 2007. Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land. Journal of Geophysical Research: Atmospheres, 112(D13): D13210
李成才, 毛节泰, 刘启汉, 刘晓阳, 刘桂青, 朱爱华. 2003. 利用MODIS光学厚度遥感产品研究北京及周边地区的大气污染. 大气科学, 27(5): 869–880
Li C C, Mao J T, Liu Q H, Liu X Y, Liu G Q and Zhu A H. 2003. Research on the air pollution in Beijing and its surroundings with MODIS AOD products. Chinese Journal of Atmospheric Sciences, 27(5): 869–880 (
Li R, Gong J H, Chen L F and Wang Z F. 2015. Estimating ground-level PM 2.5 using fine-resolution satellite data in the megacity of Beijing, China. Aerosol and Air Quality Research, 15(4): 1347–1356
李正强, 许华, 张莹, 张玉环, 陈澄, 李东辉, 李莉, 侯伟真, 吕阳, 顾行发. 2013. 北京区域2013严重灰霾污染的主被动遥感监测. 遥感学报, 17(4): 919–928
Li Z Q, Xu H, Zhang Y, Zhang Y H, Chen C, Li D H, Li L, Hou W Z, Lyu Y and Gu X F. 2013. Joint use of active and passive remote sensing for monitoring of severe haze pollution in Beijing 2013. Journal of Remote Sensing, 17(4): 919–928 (
Li Z Q, Zhang Y, Shao J, Li B S, Hong J, Liu D, Li D H, Wei P, Li W, Li L, Zhang F X, Guo J, Deng Q, Wang B X, Cui C L, Zhang W C, Wang Z Z, Lv Y, Xu H, Chen X F, Li L and Qie L L. 2016. Remote sensing of atmospheric particulate mass of dry PM 2.5 near the ground: method validation using ground-based measurements. Remote Sensing of Environment, 173: 59–68
Lin C Q, Li Y, Yuan Z B, Lau A K H, Li C C and Fung J C H. 2015. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM 2.5 . Remote Sensing of Environment, 156: 117–128
Liu Y, Sarnat J A, Kilaru V, Jacob D J and Koutrakis P. 2005. Estimating ground-level PM 2.5 in the eastern United States using satellite remote sensing. Environmental Science and Technology, 39(9): 3269–3278
Ma Z W, Liu Y, Zhao Q Y, Liu M M, Zhou Y C and Bi J. 2016. Satellite-derived high resolution PM 2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model. Atmospheric Environment, 133: 156–164
Munchak L A, Levy R C, Mattoo S, Remer L A, Holben B N, Schafer J S, Hostetler C A and Ferrare R A. 2013. MODIS 3 km aerosol product: applications over land in an urban/suburban region. Atmospheric Measurement Techniques, 6(7): 1747–1759
Peng J, Chen S, Lv H L, Liu Y X and Wu J S. 2016. Spatiotemporal patterns of remotely sensed PM 2.5 concentration in China from 1999 to 2011. Remote Sensing of Environment, 174: 109–121
Sorek-Hamer M, Kloog I, Koutrakis P, Strawa A W, Chatfield R, Cohen A, Ridgway W L and Broday D M. 2015. Assessment of PM 2.5 concentrations over bright surfaces using MODIS satellite observations. Remote Sensing of Environment, 163: 180–185
Sorek-Hamer M, Strawa A W, Chatfield R B, Esswein R, Cohen A and Broday D M. 2013. Improved retrieval of PM 2.5 from satellite data products using non-linear methods. Environmental Pollution, 182: 417–423
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
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, 43(1): 143–154 (
van Donkelaar A, Martin R V, Brauer M and Boys B L. 2015. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environmental Health Perspectives, 123(2): 135–143
van Donkelaar A, Martin R V, Brauer M, Hsu N C, Kahn R A, Levy R C, Lyapustin A, Sayer A M and Winker D M. 2016. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental Science and Technology, 50(7): 3762–3772
王宏, 陈晓秋, 余永江, 陈彬彬, 隋平. 2014. 福州市PM 2.5 、PM 2.5 /PM 10 分布特征及与气象条件关系的初步分析. 热带气象学报, 30(2): 387–391
Wang H, Chen X Q, Yu Y J, Chen B B and Sui P. 2014. Preliminary analyses on distribution characteristics of PM 2.5 、PM 2.5 /PM 10 and its relationship with meteorological conditions in Fuzhou. Journal of Tropical Meteorology, 30(2): 387–391 (
Wang J and Christopher S A. 2003. Intercomparison between satellite-derived aerosol optical thickness and PM 2.5 mass: implications for air quality studies. Geophysical Research Letters, 30(21): 2095
Wang J, Xu X G, Spurr R, Wang Y X and Drury E. 2010. Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: implications for air quality monitoring in China. Remote Sensing of Environment, 114(11): 2575–2583
Wu J S, Yao F, Li W F and Si M L. 2016. VIIRS-based remote sensing estimation of ground-level PM 2.5 concentrations in Beijing-Tianjin-Hebei: a spatiotemporal statistical model. Remote Sensing of Environment, 184: 316–328
Xie Y Y, Wang Y X, Zhang K, Dong W H, Lv B L and Bai Y Q. 2015. Daily estimation of ground-level PM 2.5 concentrations over Beijing using 3 km resolution MODIS AOD. Environmental Science and Technology, 49(20): 12280–12288
Xu X G, Wang J, Henze D K, Qu W J and Kopacz M. 2013. Constraints on aerosol sources using GEOS-Chem adjoint and MODIS radiances, and evaluation with multisensor (OMI, MISR) data. Journal of Geophysical Research: Atmospheres, 118(12): 6396–6413
杨维, 赵文吉, 宫兆宁, 赵文慧, 唐涛. 2013. 北京城区可吸入颗粒物分布与呼吸系统疾病相关分析. 环境科学, 34(1): 237–243
Yang W, Zhao W J, Gong Z N, Zhao W H and Tang T. 2013. Spatial distribution of inhalable particulate and association with respiratory disease in Beijing city. Environmental Science, 34(1): 237–243 (
张莹, 李正强. 2013. 利用细模态气溶胶光学厚度估计PM 2.5 . 遥感学报, 17(4): 929–943
Zhang Y and Li Z Q. 2013. Estimation of PM 2.5 from fine-mode aerosol optical depth. Journal of Remote Sensing, 17(4): 929–943 (
Zhang Y and Li Z Q. 2015. Remote sensing of atmospheric fine particulate matter (PM 2.5 ) mass concentration near the ground from satellite observation. Remote Sensing of Environment, 160: 252–262
相关作者
相关机构
京公网安备11010802024621