Satellite Observations of PM2.5 Changes and Driving Factors Based Forecasting Over China 2000–2025
"> Figure 1
<p>Study area and location of validation sites. The small grey triangles indicate in situ fine particulate matter (PM<sub>2.5</sub>) stations, and the larger black dots pinpoint the AERONET ground-based sites. The boxes represent the polluted areas selected in China, including Beijing-Tianjing-Hebei (JJJ), Hebei-Shandong-Henan (HSN), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Si-Chuan Basin (SCB).</p> "> Figure 2
<p>Distribution of the hygroscopic parameter (<span class="html-italic">κ</span>) over China obtained by kriging space interpolation from measurements at 17 locations, indicated by the circles. The values of <span class="html-italic">κ</span> at these locations were calculated from aerosol chemical components or hygroscopic parameters from Yeung et al. (2014), Zhang et al. (2012) and Fu et al. (2015) [<a href="#B37-remotesensing-12-02518" class="html-bibr">37</a>,<a href="#B38-remotesensing-12-02518" class="html-bibr">38</a>,<a href="#B39-remotesensing-12-02518" class="html-bibr">39</a>].</p> "> Figure 3
<p>Comparison of monthly MODIS (<b>a</b>) aerosol optical depth (AOD) and (<b>b</b>) fine-mode fraction (FMF) data with AERONET measurements. The periods for which AERONET data are available vary at different sites and we used all data available from site establishment to 2015. The line in (<b>b</b>) indicates a power law fit of the FMF data (excluding the Nam Co, Hong Kong and Dongsha Island sites) and the relation describing the fit is indicated at the top of the figure, at the right.</p> "> Figure 4
<p>(<b>a</b>) Scatter density plot of the corrected MODIS fine-mode AOD (<span class="html-italic">AOD<sub>f</sub></span>) versus POLDER GRASP <span class="html-italic">AOD<sub>f</sub></span> (monthly averages). The color represents the number of cases (color bar). The open circles are the bin-averaged MODIS <span class="html-italic">AOD<sub>f</sub></span>-corrected values and standard deviations, in POLDER GRASP <span class="html-italic">AOD<sub>f</sub></span> bins with a width of 0.1. “MODIS <span class="html-italic">AOD<sub>f</sub></span>-corrected” represent the product of MODIS AOD and FMF corrected by the nonlinear fitting function in <a href="#remotesensing-12-02518-f003" class="html-fig">Figure 3</a>b as described in the text. The dashed line is the 1:1 line. (<b>b</b>) Time series of the monthly averaged mean absolute error (MAE) and bias of <span class="html-italic">AOD<sub>f</sub></span>; (<b>c</b>) mean MAE and bias for each season. The subscript “corr” represents the MODIS corrected <span class="html-italic">AOD<sub>f</sub></span>.</p> "> Figure 5
<p>(<b>a</b>) Scatter density plots of monthly satellite-derived PM<sub>2.5</sub> versus ground-based observations binned in 3 μg m<sup>−3</sup> intervals. (<b>b</b>) Comparison of the satellite-derived mean PM<sub>2.5</sub> with ground-based measurements, both averaged over the study period (see text), binned in PM<sub>2.5</sub> intervals of 8 μg m<sup>−3</sup>. The color represents the number of cases (color bar). The lines are 1:1 (solid line) and the estimated error (dash lines) with ± (15 μg m<sup>−3</sup> +3 0%).</p> "> Figure 6
<p>Maps of satellite-derived surface PM<sub>2.5</sub> mass concentrations over China for sixteen years (2000–2015) and, at the bottom, the average over all years. The color bar at the bottom indicates the PM<sub>2.5</sub> concentrations in µg m<sup>−3</sup>.</p> "> Figure 7
<p>Time series of annual mean PM<sub>2.5</sub> mass concentrations over China, for five selected regions, indicated in the legend at the top, for 2000 to 2015. The polluted mean is the average over all five regions. Error bars indicate the standard deviation of the monthly mean. Trends are indicated at the bottom of the figure.</p> "> Figure 8
<p>Time series of key parameters ((<b>a</b>) AOD<sub>dry</sub>, (<b>c</b>) FMF and (<b>e</b>) planet boundary layer height (PBLH)) and their variability (<span class="html-italic">Var</span>) (<b>b</b>,<b>d</b>,<b>f</b>) in the particulate matter remote sensing (PMRS) model over China, for five selected regions, and the averages over all five polluted regions and over all China as indicated at the bottom of the figure. The unit of Var<sub>AOD,dry</sub> and Var<sub>FMF</sub> is μg m<sup>−3</sup>, that of Var<sub>PBLH</sub> is μg m<sup>−3</sup> km<sup>−1</sup>.</p> "> Figure 9
<p>Year-by-year contributions to PM<sub>2.5</sub> variations (ΔPM<sub>2.5</sub>) from anthropogenic and meteorological factors averaged over all five polluted regions shown in <a href="#remotesensing-12-02518-f001" class="html-fig">Figure 1</a>, for the period 2000–2015. <span class="html-italic">ε</span> is the residual. The shadowed area represents the variation range of China’s carbon emission (ΔCCE) interannual changes derived from 24 emission inventories of fossil fuel combustion as reported in Liu et al. (2015) [<a href="#B51-remotesensing-12-02518" class="html-bibr">51</a>].</p> "> Figure 10
<p>Satellite-derived annual mean PM<sub>2.5</sub> mass concentrations averaged over the five polluted areas for 2010 to 2015 (navy-blue points). These data were extrapolated to 2025 for two different emission control scenarios (NEMS and FEMS, with different meteorological factors), as described in the text. Red points are the annual mean PM<sub>2.5</sub> from ground-based measurements in 2016–2018 for comparison with the scenarios. The shaded area is slightly higher than the satellite-derived annual mean PM<sub>2.5</sub> for 2015 due to the superposition of anthropogenic and meteorological trend predictions for different periods during 2000 to 2015.</p> "> Figure A1
<p>The changes of relative variability (RV) as a function of typical ranges of key factors ((<b>a</b>) AOD<sub>dry</sub>, (<b>b</b>) FMF, (<b>c</b>) PBLH, (<b>d</b>) ρ<sub>f,dry</sub>) in the PMRS model.</p> ">
Abstract
:1. Introduction
2. Methods
2.1. PM2.5 Remote Sensing (PMRS) Model
2.2. Differential form of PM2.5 and Its Contributing Factors
2.3. Anthropogenic and Meteorological Factors Contributing to PM2.5
3. Data and Validation
3.1. Satellite and Ancillary Data
3.2. Satellite-Derived PM2.5 and Validation
4. Results
4.1. Spatial Distribution of PM2.5 over China
4.2. Variation of PM2.5 in Polluted Regions for the Years 2000–2015
4.3. Trend Comparison of PM2.5 over 2000–2015
5. Discussion
5.1. 2000–2015 Variation of Key Parameters and Variabilities
5.2. Temporal Changes of Driving Factors’ Contributions to PM2.5
5.3. PM2.5 Prediction up to 2025
5.4. Strengths and Limitations
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Abbr. | Full Name or Definition | Unit | |
---|---|---|---|
PM2.5 | Mass concentration of dry (i.e., at low RH) particulate matter with in situ (i.e., in ambient RH) aerodynamic diameter smaller than 2.5 μm | μg m−3 | |
Model key parameters | AOD | Aerosol optical depth, i.e., column-integrated aerosol extinction | - |
FMF | Fine-mode fraction (fraction of fine-mode contribution to total AOD) | - | |
PBLH | Planetary boundary layer height | km | |
RH | Relative humidity | % | |
ρ2.5,dry | Effective density of dry particulates of PM2.5 | g cm−3 | |
VEf | Volume-to-extinction ratio of fine particulates | μm3 μm−2 | |
f(RH) | Particle volume drying factor | - | |
Var | Partial derivative, Jacobian, or variability of the model factor to PM2.5 | μg m−3 Factor−1 | |
RV | Relative variability, i.e., Var/PM2.5 | Factor−1 | |
Δ | Variation or change of variables | - | |
ε | Residual of the PM2.5 contribution separation process | μg m−3 |
Appendix B
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Site | Period | TDGB | MGB | TDSD | MSD |
---|---|---|---|---|---|
Hongkong | 2000–2004 | 3.13 | 35.3 | 3.46 | 35.0 |
2005–2011 | −1.77 | 35.0 | −1.07 | 38.0 | |
2012–2015 | −0.92 | 28.0 | −1.30 | 32.5 | |
Beijing | 2008–2015 | −3.39 | 88.7 | −2.40 | 56.0 |
Shanghai | 2012–2015 | −1.55 | 56.1 | −2.25 | 55.3 |
Chengdu | 2012–2015 | −6.02 | 73.9 | −4.08 | 50.8 |
Guangzhou | 2012–2015 | −5.96 | 49.3 | −1.97 | 46.2 |
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Zhang, Y.; Li, Z.; Chang, W.; Zhang, Y.; de Leeuw, G.; Schauer, J.J. Satellite Observations of PM2.5 Changes and Driving Factors Based Forecasting Over China 2000–2025. Remote Sens. 2020, 12, 2518. https://doi.org/10.3390/rs12162518
Zhang Y, Li Z, Chang W, Zhang Y, de Leeuw G, Schauer JJ. Satellite Observations of PM2.5 Changes and Driving Factors Based Forecasting Over China 2000–2025. Remote Sensing. 2020; 12(16):2518. https://doi.org/10.3390/rs12162518
Chicago/Turabian StyleZhang, Ying, Zhengqiang Li, Wenyuan Chang, Yuanxun Zhang, Gerrit de Leeuw, and James J. Schauer. 2020. "Satellite Observations of PM2.5 Changes and Driving Factors Based Forecasting Over China 2000–2025" Remote Sensing 12, no. 16: 2518. https://doi.org/10.3390/rs12162518