Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals

Zhang, Tianhao; Zhu, Zhenfeng; Gong, Wei; Zhu, Zerun; Sun, Kun; Wang, Lunche; Huang, Yusi; Mao, Feiyue; Shen, Huanfeng; Li, Zhiwei; Kai, Xu

doi:10.1016/j.rse.2018.06.030

Cited by 89 publications

(36 citation statements)

References 72 publications

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“…It was also found that the average AOD levels always rose after interpolation, irrespective of the interpolating method used. This is consistent with the phenomenon that missing more frequently occurs at high MODIS-AOD levels [52,53]. The aerosol hygroscopic growth during missing days results from increased humidity from cloud or humid airflows [54,55].…”

Section: Discussionsupporting

confidence: 80%

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

et al. 2020

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The immense problem of missing satellite aerosol retrievals (Aerosol Optical Depth, (AOD)) detrimentally affects the prediction ability of ground-level PM2.5 concentrations and may lead to unavoidable biases. An appropriate missing-imputation method has not been well developed to date. This study developed a two-stage approach (AOD-imputation stage and PM2.5-prediction stage) to predict short-term PM2.5 exposure in mainland China from 2013–2018. At the AOD-imputation stage, geostatistical methods and machine learning (ML) algorithms were examined to interpolate 1 km satellite aerosol retrievals. At the PM2.5-prediction stage, the daily levels of PM2.5 were predicted at a resolution of 1 km, based on interpolated AOD and meteorological data. The statistical performances of the different interpolation methods were comprehensively compared at each stage. The original coverage of retrieved AOD was 15.46% on average. For the AOD-imputation stage, ML methods produced a higher coverage (98.64%) of AOD than geostatistical methods (21.43–87.31%). Among ML algorithms, random forest (RF) or extreme gradient boosted (XG-interpolated) AOD produced better interpolated quality (CV R2 = 0.89 and 0.85) than other algorithms (0.49–0.78), but XGBoost required only 15% of the computing time of RF. For the PM2.5 predicted stage, neither RF-AOD nor XG-AOD could guarantee higher accuracy in PM2.5 estimations (CV R2 = 0.88 (RF or XG-AOD) compared to 0.85 (original)), or more stable spatial and temporal extrapolation (spatial, (temporal) CV R2 = 0.83 (0.83), 0.82 (0.82), and 0.65 (0.61) for RF, XG, and original). For the AOD-imputation stage, the missing-filled efficiency depended more on external information, while the missing-filled accuracy relied more on model structure. For the PM2.5 predicted stage, efficient AOD interpolation (or the ability to eliminate the missing data) was a precondition for the stable spatial and temporal extrapolation, while the quality of interpolated AOD showed less significant improvements. It was found that XG-AOD is a better choice to estimate daily PM2.5 exposure in health assessments.

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Section: Discussionsupporting

confidence: 80%

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

et al. 2020

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“…Aerosol optical depth (AOD) data retrieved through satellite data inversion are highly correlated with ground PM2.5 concentration so as to achieve continuous spatial monitoring. us, it has been extensively applied in estimating the ground PM2.5 concentration and further reveals the spatiotemporal variation of PM2.5 concentration in urban landscape [11][12][13][14]. Although airborne remote sensing can provide AOD data with higher spatial resolution and continuous spatial coverage, PM2.5 monitoring at ground platforms can provide results with greater accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

Assessing Spatiotemporal Characteristics of Urban PM2.5 Using Fractal Dimensions and Wavelet Analysis

Wang

Zhang

Liang

et al. 2020

Mathematical Problems in Engineering

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Due to rapid urbanization and industrialization, atmospheric fine particulate matter (PM2.5) has become a primary urban pollutant, seriously affecting air quality and resident health. Existing airborne remote sensing and ground sensor monitoring can efficiently collect PM2.5 data. It is urgent yet challenging to fully use these two monitoring modes to analyze the spatial distribution and dynamic changes of PM2.5. This paper proposes a method to analyze the spatiotemporal characteristics of urban PM2.5 concentration using airborne and ground monitoring data. The method utilizes the boundary dimension and the radius dimension to describe the spatial distribution characteristics of PM2.5 and then adopts wavelet analysis to detect the fluctuation periods of PM2.5 concentration. Case study was performed based on the moderate-resolution imaging spectroradiometer (MODIS) data and the daily ground monitoring concentration of PM2.5 from 2014 to 2017 in Beijing. The decrease results of boundary dimension indicate that the changes in PM2.5 concentration tend to be slower over time, and the fluctuation amplitude gradually becomes smaller. The increase results closer to 2.0 in the radius dimensions suggest that the PM2.5 distribution becomes more uniform, indicating that the pollution control in Beijing had achieved initial success. The frequency results of wavelet analysis reveal that PM2.5 concentration in Beijing is mainly subject to periodic variation of 190 days. These findings can help us to gain deeper insight into the complex spatiotemporal characteristics of urban PM2.5.

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“…Although in situ PM2.5 concentration data have played critical roles in improving our understanding of regional air quality variations and relevant influential factors (Yang D. et al, 2018;Yang Q. et al, 2019;Zheng et al, 2017), little concern was raised to the quality of such dataset itself (Bai et al, 2019a(Bai et al, , 2019cHe and Huang, 2018;Zhang et al, 2019Zhang et al, , 2018Zou et al, 2016). Meanwhile, few studies provided a detailed description of the accuracy or bias level (uncertainty) of the observed PM2.5 data in recent years (You et al, 2016;Guo et al, 2017;Shen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A homogenized daily <i>in situ</i> PM<sub>2.5</sub> concentration dataset from national air quality monitoring network in China

Bai¹,

Li²,

Wu³

et al. 2020

Preprint

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Abstract. In situ PM2.5 concentration observations have long been used as critical data sources in haze related studies. Due to the frequently occurred haze pollution episodes, China started to establish the national ambient air quality monitoring network in 2012 but without providing a data download interface to the public. In this study, a five-year long homogenized daily in situ PM2.5 concentration dataset was generated on the basis of discrete data records retrieved from the China National Environmental Monitoring Center (CNEMC) via a web crawler. A set of methods for the purposes of gap filling, data merging, homogeneity test, and bias correction were geared up seamlessly to improve the data integrity and to make essential adjustments to the inherent discontinues detected in each PM2.5 concentration record. After excluding those records with limited temporal coverage, a dataset including 1,309 long-term coherent PM2.5 concentration time series at a daily resolution between 2015 and 2019 was carefully compiled. This is the first thrust to homogenize in situ PM2.5 observations in China. The trend estimations derived from the homogenized dataset indicate a spatially homogeneous decreasing tendency of PM2.5 across China at a mean rate of about −7.6 % per year from 2015 to 2019. Compared with the raw PM2.5 concentration dataset, the homogenized dataset not only has a complete temporal coverage but is more consistent over space and time. This homogenized daily in situ PM2.5 concentration dataset is publicly accessible at https://doi.org/10.1594/PANGAEA.917557, which can be used as a significant data source for satellite-based PM2.5 concentration mapping, population exposure risk assessment, and air quality monitoring and management.

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Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals

Cited by 89 publications

References 72 publications

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

Assessing Spatiotemporal Characteristics of Urban PM2.5 Using Fractal Dimensions and Wavelet Analysis

A homogenized daily <i>in situ</i> PM<sub>2.5</sub> concentration dataset from national air quality monitoring network in China

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Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals

Cited by 89 publications

References 72 publications

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

Assessing Spatiotemporal Characteristics of Urban PM2.5 Using Fractal Dimensions and Wavelet Analysis

A homogenized daily &lt;i&gt;in situ&lt;/i&gt; PM&lt;sub&gt;2.5&lt;/sub&gt; concentration dataset from national air quality monitoring network in China

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Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals

A homogenized daily <i>in situ</i> PM<sub>2.5</sub> concentration dataset from national air quality monitoring network in China