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Article
Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD Yuanyu Xie, Yuxuan Wang, Kai Zhang, Wenhao Dong, Baolei Lv, and Yuqi Bai Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b01413 • Publication Date (Web): 27 Aug 2015 Downloaded from http://pubs.acs.org on September 3, 2015
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Environmental Science & Technology
Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD Yuanyu Xie1, Yuxuan Wang*1,2,3, Kai Zhang4, Wenhao Dong1, Baolei Lv1, Yuqi Bai1 1
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth
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System Science, Tsinghua University, Beijing, China. 2
Department of Marine Science, Texas A&M University at Galveston, Galveston,
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TX, 77553, USA. 3
Department of Atmospheric Science, Texas A&M University, College Station,
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TX, 77853, USA. 4
Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas, 77030, USA;
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*Corresponding Author:
[email protected] 1 ACS Paragon Plus Environment
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ABSTRACT
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Estimating exposures to PM2.5 within urban areas requires surface PM2.5
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concentrations at high temporal and spatial resolutions. We developed a mixed effects
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model to derive daily estimations of surface PM2.5 levels in Beijing, using the 3 km
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resolution satellite aerosol optical depth (AOD) calibrated daily by the newly
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available high-density surface measurements. The mixed effects model accounts for
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daily variations of AOD-PM2.5 relationships and shows good performance in model
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predictions (R2 of 0.81~0.83) and cross validations (R2 of 0.75~0.79). Satellite derived
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population-weighted mean PM2.5 for Beijing was 51.2 μg/m3 over the study period
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(Mar 2013 to Apr 2014), 46% higher than China’s annual-mean PM2.5 standard of 35
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μg/m3. We estimated that more than 19.2 million people (98% of Beijing’s population)
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are exposed to harmful level of long-term PM2.5 pollution. During 25% of the days
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with model data, the population-weighted mean PM2.5 exceeded China’s daily PM2.5
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standard of 75 μg/m3. Predicted high-resolution daily PM2.5 maps are useful to identify
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pollution “hot spots” and estimate short- and long-term exposure. We further 2 ACS Paragon Plus Environment
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demonstrated that a good calibration of the satellite data requires a relatively large
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number of ground-level PM2.5 monitoring sites and more are still needed in Beijing.
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Introduction
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Particulate matter (PM) air pollution in China is a major public health problem.
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According to the Global Burden of Disease report, ambient PM air pollution is
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responsible for over 1.2 million premature deaths annually in China, and 50% of its
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1.3 billion population is currently exposed to ambient PM2.5 (particulate matter with
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aerodynamic diameters of less than 2.5 μm) in exceedance of an annual average of 35
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μg/m3 (China’s National Ambient Air Quality Standard, or NAAQS, available at
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http://kjs.mep.gov.cn/).1 In recent years, extremely high PM2.5 concentrations have
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been reported over many urban areas in China, notably in Beijing. 2-5 Some of the
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worst pollution episodes saw PM2.5 concentrations exceeding 500 μg/m3 and lasting
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multiple days.6-7 Accurate assessment of population exposure to such elevated levels
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of PM2.5 over densely populated urban areas requires surface PM2.5 concentrations at
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high temporal and spatial resolutions, but ground-level monitors have been sparse in
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China which hinders our ability to accurately assess the health impact of PM2.5
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pollution.
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Satellite retrievals of AOD provide larger spatial coverage and have been widely
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employed as a proxy to infer surface PM2.5 concentrations.8-13 Early studies obtained
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the AOD-PM2.5 relationships using simple linear regression models or chemical 3 ACS Paragon Plus Environment
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transport models (CTM) at a global or continental scale, including the United States
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(US) and Europe.10-11,14-17 Those studies reported R2, a measure of goodness of fit of
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linear regression, generally between 0.3 and 0.6 and the AOD-PM2.5 relationship was
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typically assumed to be constant in time and location.18 Advanced statistical models
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(e.g. generalized linear model, land use regression model, geographically weighted
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regression, and generalized additive model) have been applied in order to account for
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variability in the AOD-PM2.5 relationships associated with meteorology or land
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use.13,19-22 These advanced models yielded higher prediction performance (R2 at
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0.6~0.8) for large regions such as the northeastern and southeastern US and China,
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but the increased complexity of these models requires additional data as inputs. Lee et
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al23 first introduced an AOD daily calibration approach using a mixed effects model
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to account for day-to-day and location-specific variability in the AOD-PM2.5
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relationships over New England. They demonstrated that daily adjustment of the
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AOD-PM2.5 relationships leads to a significant improvement in model performance
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with R2 reaching 0.92, as compared to the linear regression (R2~0.51) applied to the
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same region. The mixed effects model provides a valuable tool for estimating PM2.5
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concentrations in urban areas where ground monitoring sites are not of sufficient
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density.
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Dense surface networks and high-resolution satellite AOD data are essential for
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improving the accuracy of exposure assessment models. Since Mar 2013, 35 PM2.5
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monitoring sites have been deployed in Beijing Municipality by Beijing 4 ACS Paragon Plus Environment
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Environmental Protection Bureau, providing hourly PM2.5 concentrations at the urban
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and suburban areas of Beijing. In early 2014, the Moderate Resolution Imaging
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Spectroradiometer (MODIS) team released the new collection 6 (C006) AOD
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products at a 3 km resolution.24 Prior standard AOD products from MODIS have a
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spatial resolution of 10 km or coarser. Most of the previous studies applying the
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MODIS AOD to derive surface PM2.5 concentrations over China focused on the
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regional scale with resolutions ranging from 10 km to 100 km. 12,13,22 Utilizing the
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newly available surface monitoring data in Beijing, this study is the first attempt to
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develop a mixed effects model to estimate ground-level PM2.5 concentrations using
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the 3 km resolution AOD data on a daily basis for a heavily polluted urban
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environment in China. The severe pollution events offer a valuable opportunity to
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assess the performance of the daily AOD calibration approach through this mixed
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effects model. The paper is organized as follows: surface and satellite data as well as
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model development are introduced in the “Materials and Methods” section; the model
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performance, PM2.5 prediction results and uncertainties are provided in the “Results
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and Discussion” section.
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Materials and methods
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Ground-level PM2.5 data. Hourly PM2.5 concentrations observed at 35 monitoring
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stations in Beijing (Figure 1b) were obtained from Beijing Municipal Environmental
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Monitoring Center (http://zx.bjmemc.com.cn/). Surface PM2.5 mass concentrations are 5 ACS Paragon Plus Environment
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measured by the Tapered Element Oscillating Microbalance (TEOM) method, and the
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measurements have undergone the calibration processes and quality controls
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according to the environmental protection standard of China (HJ 618-2011;
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MEPCN).25 Beijing Municipality consists of 16 districts with six urban districts
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located in the center of the city and ten suburban districts surrounding the urban
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regions (Figure 1c). Among the 35 PM2.5 monitors, 17 are located in the urban
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districts and 18 in suburban districts. We averaged the surface concentrations
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measured between 13:00 pm and 14:00 pm local time at each site to derive the daily
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PM2.5 concentrations that match with the over-passing time of the Aqua satellite. The
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study period is from March 26th 2013 to April 23th 2014, spanning a total of 394 days.
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MODIS 3 km AOD products and calibration. MODIS 3 km AOD product.
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MODIS on board the NASA Aqua satellite has been in operation since 2002,
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providing retrieval products of aerosol and cloud properties with nearly daily global
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coverage.26 The dark target algorithms make use of surface reflectance at three
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wavelength channels (0.47 μm, 0.66 μm, and 2.12 μm) for AOD retrievals over land.
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Standard MODIS Level 2 (L2) AOD products are distributed at a 10 km resolution.
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To fulfill the need for higher resolution pollution detection, the most recently released
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MODIS Collection 6 product (MYD04_3K) provides aerosol products at a 3 km
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resolution in addition to the L2 10 km product. The retrieval algorithm of the higher
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resolution product is similar to that of the 10 km standard product, but averages 6 × 6
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pixels in a single retrieval box rather than the 20 × 20 pixels after cloud screening and 6 ACS Paragon Plus Environment
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other surface mask processes. Pixels outside the reflectivity range of the brightest 50%
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and darkest 20% at 0.66 μm are discarded to reduce uncertainty. Validation against
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surface sun photometer shows that two thirds of the 3 km retrievals fall within the
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expected error on a regional comparison but with a high bias of ~0.06 especially over
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urban surface.27
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MODIS AOD validation. The ground-based AOD measurements from three AErosol
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RObotic NETwork (AERONET) sites (http://aeronet.gsfc.nasa.gov/) in Beijing were
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used to validate the satellite-derived AOD. To be comparable with the spectrum
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setting of MODIS, AERONET AOD at 550 nm were calculated by interpolating AOD
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at 440 nm and at 675 nm using the reported angstrom exponent at the respective
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wavelengths. The 3 km MODIS AOD products from Aqua show high temporal
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correlations with the AERONET AOD (Pearson correlation coefficient r of 0.93, 0.93
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and 0.94 at the three sites respectively, Text S1, Supporting Information (SI)). The
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mean AOD difference between MODIS and AERONET at the three sites is 0.29. The
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higher AOD from MODIS could be partly attributed to the reflectance bias over the
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brighter surface of urban areas, which was also observed over other urban regions.
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systematic bias when deriving the PM2.5 to AOD ratio and is not expected to influence
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surface PM2.5 estimation in this study because we calibrated MODIS AOD with
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ground-level PM2.5 measurements on a daily basis.
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Since this bias is persistent for the full range of AOD values, it can be treated as a
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Model development and validation. We selected the site-collocated satellite AOD
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values for each surface site where it falls within a 3 km grid. If there are more than
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one site within a single 3 km grid, the PM2.5 values of those sites are averaged. With
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this process, site #12 and #14 are averaged and there remain 34 pairs of AOD and
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PM2.5 data for model development.
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A linear regression model was first applied to the collocated AOD and PM2.5 datasets. The linear regression model for all the 34 sites follows the form of:
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PM 2.5 AOD
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Where and are the fixed intercept and slope, respectively. The intercept and
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slope do not have spatial and temporal variations since the linear regression model
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assumes that the AOD-PM2.5 relationship is constant for all the sites throughout the
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study period. The log transformed AOD and PM2.5 were also tested, and no significant
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improvement was found in the regression performance. Therefore, we only present
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the regression results with the original AOD and PM2.5 datasets.
(1)
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In reality, the AOD-PM2.5 relationship may exhibit spatial and temporal variations
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due to changing meteorology and other factors. To represent the varying AOD-PM2.5
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relationship, we developed a mixed effects model following the approach proposed by
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Lee et al.23 which takes into consideration daily variations of the AOD-PM2.5
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relationship and derives site-specific parameters for spatial adjustment. The mixed
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effects model estimates surface PM2.5 concentrations for each site i of day j
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( PM 2.5,ij ) from collocated MODIS AOD ( AODij ) in the form of 8 ACS Paragon Plus Environment
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PM 2.5,ij ( u j ) ( v j ) AODij si ij
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(u j v j ) ~ N [(00), ]
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Where and are the fixed intercept and slope independent of time and
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location, u j and v j are the random intercept and slope for all the sites at each day.
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When the subscript i is not present for a parameter in Equation (2), it indicates that
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parameter does not change by site. The random terms ( u j and v j ) reflect the
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day-to-day variations of the AOD-PM2.5 relationship influenced by meteorology,
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satellite retrieval conditions, etc. The si ~N (0, s2 ) is a site term which accounts for
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the spatial difference of the AOD-PM2.5 relationship due to differences in site specific
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characteristics (i.e. surface reflectivity, topography, PM2.5 emissions, and pollution
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transported to the observation sites). The comparison between the mixed effects
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models with and without the site effect si provides a measure of the sensitivity in
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model-derived day-to-day variations of the AOD-PM2.5 relationship to site locations.
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ij represents the error term, and
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day-specific random effects. We excluded days with less than two pairs of
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AOD-PM2.5 data and performed model predictions on the remaining available days.
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Model performances were evaluated by comparing the predictions to ground
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measurements using R2, mean prediction error (MPE), and root mean square error
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(RMSE). The correlation coefficient, r, reported in this study is the Pearson
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correlation coefficient unless stated otherwise.
(2)
is the variance-covariance matrix for the
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A cross-validation (CV) method was implemented to test the performance of the
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linear regression model and mixed effects model. We isolated one site at a time,
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performed model fitting with the remaining 33 sites and validated model
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performances on the isolated site. The process was repeated for each of the 34 sites.
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The CV statistics are represented with R2, MPE and RMSE. We also varied the
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number of isolated sites during cross validation to 5, 10, and 20 as a way of testing the
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sensitivity of the mixed effects model to the density of surface monitors needed.
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Descriptive statistics. Table 1 presents the descriptive statistics of measured PM2.5
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concentrations at the 35 surface sites and site-collocated MODIS AOD during the
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study period. Daily time series of the site-average PM2.5 concentrations and
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site-collocated MODIS AOD (Figure 1a) indicate an overall good correlation between
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them (r = 0.6). Average PM2.5 from the ground-based monitors is 81.04 μg/m3 during
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the study period, more than a factor of 2 higher than China’s NAAQS of 35 μg/m3 for
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annual-mean PM2.5, and the standard deviation (SD) is 73.23 μg/m3. The average
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AOD is 0.68 with a SD of 0.49. The maximum site-average daily mean PM2.5 is
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385.80 μg/m3 and the maximum single-site daily concentration reaches 500 μg/m3.
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Many sites have high AOD values larger than 2.0. The data are further divided into
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the warm season (Apr 15th to Oct 14th) and the cold season (Oct 15th to Apr 14th). The
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average PM2.5 concentrations does not differ much between the two seasons but the
Results and discussion
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AOD values is much lower in the cold season than in the warm season. Besides the
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impact of lower planetary boundary layer (PBL) depths in the cold season as well as
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other meteorological conditions (e.g. lower relative humidity), a lower percent of
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available satellite AOD retrievals over snow also explains the lower mean and SD of
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AOD in the cold season. The detailed statistics for each site and description for
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seasonal comparisons are shown in Text S2 (SI).
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Spatially surface PM2.5 from the ground-level monitors presents a decreasing
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gradient from south to north due to topography and land use difference in Beijing
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(Figure 1b). The south to north PM2.5 gradient generally demonstrates the emission
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difference between suburban and urban regions. In addition, the southern sites are
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more influenced by pollution transported from the cities south of Beijing, while the
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north of Beijing is surrounded by mountains. Satellite AOD averaged for the entire
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study period exhibits a similarly strong spatial gradient (Figure 1c), with highest
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values over the southeast urban districts. The correlation between the period-mean
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PM2.5 and site-collocated AOD is 0.64 across the 35 sites, indicating a relatively tight
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spatial consistency between the two datasets.
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Linear regression model. The linear regression model gives a regression slope of
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58.67 and intercept of 10.08 between measured PM2.5 and site-collocated MODIS
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AOD, with an overall R2 of 0.47 (p
