Has China been exporting less particulate air pollution over the past decade?

Zhang, Jianglong; Reid, Jeffrey S.; Alfaro-Contreras, R.; Xian, Peng

doi:10.1002/2017gl072617

Cited by 68 publications

(90 citation statements)

References 29 publications

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“…The data in Fig. 12 show a decrease in the MODIS data for the period 2006-2009, as observed by Zhang et al (2017), but the ATSR AOD does not show a clear decrease in that period. The decrease in both data sets in 2008-2009 could be the result of the economic recession as suggested by, e.g., Lin et al (2010), and Zhao et al (2017).…”

Section: Seasonal Variationmentioning

confidence: 56%

“…7 and 8 are similar, except that ATSR underestimates by 0.07 and MODIS overestimates by a similar amount. It is noted that differences in the AOD value such as shown here between ATSR and MODIS are also observed between the two MODIS instruments (with a 4 h difference in overpass time which may influence the results) and between MODIS and MISR Zhang et al, 2017). MISR flies on Terra and has a swath width which is only a little smaller than that of ATSR.…”

Section: Discussionmentioning

confidence: 87%

“…Several authors identify a pivot point around 2006(Kang et al, 2016Zhang et al, 2017;Zhao et al, 2017) after which the AOD fluctuates before the decrease sets in from 2011 . Using a different analysis method, Zhang et al (2017) suggest a decrease starting from about 2006-2008.…”

Section: Seasonal Variationmentioning

confidence: 99%

“…Using a different analysis method, Zhang et al (2017) suggest a decrease starting from about 2006-2008. It is noted that these studies were made for east or SE China with much higher AOD, as shown in Figs.…”

Section: Seasonal Variationmentioning

confidence: 99%

“…Xu et al, 2015;Ma et al, 2016; but also using multiple satellite data (Lin et al, 2010;Guo et al, 2011Guo et al, , 2016bDong et al, 2017;Zhao et al, 2017;Zhang et al, 2017). However, these studies used MODIS C5.1 AOD data and substantial differences exist between C6 and C5.1 (e.g., Levy et al, 2013;Sayer et al, 2014;Tao et al, 2015;Xiao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

et al. 2018

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Abstract. The retrieval of aerosol properties from satellite observations provides their spatial distribution over a wide area in cloud-free conditions. As such, they complement ground-based measurements by providing information over sparsely instrumented areas, albeit that significant differences may exist in both the type of information obtained and the temporal information from satellite and ground-based observations. In this paper, information from different types of satellite-based instruments is used to provide a 3-D climatology of aerosol properties over mainland China, i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a lidar flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the columnintegrated extinction (aerosol optical depth -AOD) available from three radiometers: the European Space Agency (ESA)'s Along-Track Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995-2015. AOD data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS Collection 6 (C6) the AOD data set is used that was obtained from merging the AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further referred to as the DTDB merged AOD product. These data sets are validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data as reference. The results show that, over China, ATSR slightly underestimates the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is overall lower than that from MODIS, and the difference increases with increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD data sets is better than the other one everywhere. However, ATSR ADV has limitations over bright surfaces which the MODIS DB was designed for. To allow for comparison of MODIS C6 results with previous analyses where MODIS Collection 5.1 (C5.1) data were used, also the difference between the C6 and C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed.The AOD data sets show strong seasonal differences and the seasonal features vary with latitude and longitude across China. Two-decadal AOD time series, averaged over all of mainland China, are presented and briefly discussed. Using the 17 years of ATSR data as the basis and MODIS/Terra to follow the temporal evolution in recent years when the environmental satellite Envisat was lost requires a compariPublished by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Seasonal Variationmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 87%

Section: Seasonal Variationmentioning

confidence: 99%

Section: Seasonal Variationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

et al. 2018

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Climatology of aerosol optical depth over Eastern Europe based on 19 years (2000–2018) MODIS TERRA data

Filonchyk

Hurynovich

Yan

et al. 2019

Intl Journal of Climatology

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This study investigates the spatial–temporal evolution of aerosols, their optical properties (aerosol optical depth [AOD] and ångström exponent [AE]) and the trend over a period of 19 years in the Eastern European countries. The data used for the study are from moderate‐resolution imaging spectroradiometer (MODIS) Terra Collection 6.1 aerosol products with the use of Dark Target algorithm for the period from 2000 to 2018. The satellite‐based AOD products were validated against the AERONET AOD measurements in four stations, located in different regions. The results demonstrate high coherence in the setting of high values of correlation (R = 0.8–0.9) and low values of root‐mean‐square error (RMSE [0.066–0.130]) and mean absolute error (MAE [0.048–0.067]). However, of the total amount of data from AERONET stations, only 68.9% of available data fall within expected error (EE) over land. Generally, all countries recorded a low AOD (83.2% less than 0.2) and high AE (82.5% higher than 1). Mean AOD and AE values varied from 0.17 ± 0.12 and 1.31 ± 0.27 (Russia) to 0.24 ± 0.14 and 1.31 ± 0.33 (Czech Republic). There was a gradual decrease in aerosol load in all countries, the highest tendencies of AOD reduction are observed in Czech Republic, Bulgaria, Slovakia and Hungary (by −0.0028, −0.0027, −0.0026 and −0.0025 per year), while the lowest tendencies of reduction are observed in Russia and Moldova (by −0.001 and −0.0006 per year). Seasonal averaged AOD values have maximum values in summer and minimum values in winter. We studied predominant aerosol types, which are based on AOD and AE interrelation, the results showed the predominance of clean continental and mixed aerosols over all countries of the region.

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Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)

Xian¹,

Reid²,

Hyer³

et al. 2019

Quart J Royal Meteoro Soc

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Since the first International Cooperative for Aerosol Prediction (ICAP) multi‐model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP‐MME over 2012–2017, with a focus on June 2016–May 2017. Evaluated with ground‐based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate‐resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP‐MME AOD consensus remains the overall top‐scoring and most consistent performer among all models in terms of root‐mean‐square error (RMSE), bias and correlation for total, fine‐ and coarse‐mode AODs as well as dust AOD; this is similar to the first ICAP‐MME study. Further, over the years, the performance of ICAP‐MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP‐MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP‐MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012–2017 suggests a general tendency for model improvements in fine‐mode AOD, especially over Asia. No significant improvement in coarse‐mode AOD is found overall for this time period.

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Has China been exporting less particulate air pollution over the past decade?

Cited by 68 publications

References 29 publications

Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

Climatology of aerosol optical depth over Eastern Europe based on 19 years (2000–2018) MODIS TERRA data

Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)

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