MODIS AOD sampling rate and its effect on PM2.5 estimation in North China

Song, Zhilong; Fu, Disong; Zhang, Xiaoling; Han, Xinlei; Song, Jingjing; Zhang, Jinqiang; Wang, Jun; Xia, Xiangao

doi:10.1016/j.atmosenv.2019.04.020

Cited by 44 publications

(26 citation statements)

References 38 publications

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“…The AOD uncertainty of the Aerosol Robotic Network (AERONET [16], https://aeronet.gsfc.nasa.gov/) is 0.01~0.02 [17]. Diurnal variation of AOD based on 15 years worthy of AERONET observation data in the North China Plain (NCP) showed that AOD increased gradually from early morning to later afternoon [18]. Based on long-term observations of 50 China Aerosol Remote Sensing Network (CARSNET) sites, Che et al [19] found that annual mean AOD at 440 nm (AOD 440nm) increased from remote/rural sites (0.12) to urban sites (0.79).…”

Section: Introductionmentioning

confidence: 99%

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Song

et al. 2020

Remote Sensing

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Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM2.5 in summer and minimum AOD, but maximum PM2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM2.5 from AOD.

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Section: Introductionmentioning

confidence: 99%

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Song

et al. 2020

Remote Sensing

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“…The DT algorithm has been applied to remotely-sensed data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensors on board the Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board the Suomi National Polar-orbiting Partnership satellite, so as to generate AOD products having various spatial resolutions (10,3, and 1 km for MODIS [18][19][20][21], 6 and 0.75 km for VIIRS [22,23]). The AOD products have been widely used in aerosol and PM 2.5 studies [24][25][26], among which many have found that AOD with a higher spatial resolution can achieve a higher correlation with AErosol RObotic NETwork (AERONET) measurements [27,28] and particle concentrations [29][30][31][32]. However, due to the relatively coarse resolutions (i.e., at kilometer scales) of these AOD products, it is still unclear whether the AOD-PM 2.5 correlation can be further improved by adopting even higher resolution AOD data.…”

Section: Introductionmentioning

confidence: 99%

Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China

Liang

Zhou

et al. 2019

Remote Sensing

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Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015–2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234–0.329 and 0.340–0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1–5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales <1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.

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“…Satellite-based remote sensing has been becoming a widely used technique to monitor the PM 2.5 concentrations [14][15][16]. It can provide a large spatial coverage of PM 2.5 concentrations over long periods, and this distinct advantage is unavailable for ground monitoring stations.…”

Section: Introductionmentioning

confidence: 99%

“…The AOD is a measure of columnar light extinction by aerosol scattering and absorption. The MODIS satellite estimates large-scale coverage of PM 2.5 concentrations via the retrieval model between ground PM 2.5 and the satellite-based AOD [15,24].…”

Section: Introductionmentioning

confidence: 99%

A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

et al. 2019

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Current PM2.5 retrieval maps have many missing values, which seriously hinders their performance in real applications. This paper presents a framework to map full-coverage daily average PM2.5 concentrations from MODIS C6 aerosol optical depth (AOD) products and fill missing pixels in both the AOD and PM2.5 maps. First, a two-stage inversed variance weights (IVW) algorithm was adopted to fuse the MODIS C6 Terra and Aqua AOD products, which fills missing data in MODIS standard AOD data and obtains a high coverage daily average. After that, using the fused MODIS daily average AOD and ground-level PM2.5 in all grid cells, a two-stage generalized additive model (GAM) was implemented to obtain the full-coverage PM2.5 concentrations. Experiments on the Yangtze River Delta (YRD) in 2013–2016 were carefully designed to validate the performance of our proposed framework. The results show that the two-stage IVW could not only improve the spatial coverage of MODIS AOD against the original standard product by 230%, but could also keep its data accuracy. When compared with the ground-level measurements, the two-stage GAM can obtain accurate PM2.5 concentration estimates (R2 = 0.78, RMSE = 19.177 μg/m3, and RPE = 28.9%). Moreover, our method performs better than the inverse distance weighted method and kriging methods in mapping full-coverage daily PM2.5 concentrations. Therefore, the proposed framework provides a good methodology for retrieving full-coverage daily average PM2.5 concentrations from MODIS standard AOD products.

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MODIS AOD sampling rate and its effect on PM2.5 estimation in North China

Cited by 44 publications

References 38 publications

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China

A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

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