Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count

You, Wei; Zang, Zengliang; Zhang, Lifeng; Li, Zhijin; Chen, Dan; Gui, Zhang

doi:10.1016/j.rse.2015.07.020

Cited by 79 publications

(55 citation statements)

References 51 publications

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“…The averaged AOD from 23:30 to 00:30 UTC was matched with the radiosonde at 00:00 UTC. In addition, if the standard deviation of the average AOD was greater than 0.5, the average AOD was removed to reduce the possibility of spurious AOD values [13,37].…”

Section: Aeronet Aod Datamentioning

confidence: 99%

Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods

et al. 2017

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Abstract:The aerosol optical depth (AOD) from satellites or ground-based sun photometer spectral observations has been widely used to estimate ground-level PM 2.5 concentrations by regression methods. The boundary layer height (BLH) is a popular factor in the regression model of AOD and PM 2.5 , but its effect is often uncertain. This may result from the structures between the stable and convective BLHs and from the calculation methods of the BLH. In this study, the boundary layer is divided into two types of stable and convective boundary layer, and the BLH is calculated using different methods from radiosonde data and National Centers for Environmental Prediction (NCEP) reanalysis data for the station in Beijing, China during 2014-2015. The BLH values from these methods show significant differences for both the stable and convective boundary layer. Then, these BLHs were introduced into the regression model of AOD-PM 2.5 to seek the respective optimal BLH for the two types of boundary layer. It was found that the optimal BLH for the stable boundary layer is determined using the method of surface-based inversion, and the optimal BLH for the convective layer is determined using the method of elevated inversion. Finally, the optimal BLH and other meteorological parameters were combined to predict the PM 2.5 concentrations using the stepwise regression method. The results indicate that for the stable boundary layer, the optimal stepwise regression model includes the factors of surface relative humidity, BLH, and surface temperature. These three factors can significantly enhance the prediction accuracy of ground-level PM 2.5 concentrations, with an increase of determination coefficient from 0.50 to 0.68. For the convective boundary layer, however, the optimal stepwise regression model includes the factors of BLH and surface wind speed. These two factors improve the determination coefficient, with a relatively low increase from 0.65 to 0.70. It is found that the regression coefficients of the BLH are positive and negative in the stable and convective regression models, respectively. Moreover, the effects of meteorological factors are indeed related to the types of BLHs.

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Section: Aeronet Aod Datamentioning

confidence: 99%

Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods

et al. 2017

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“…With the limited number of samples, classification training and testing for both RF and MCC were performed with 10-fold cross-validation [56,57] to assess the predictive performance of both classification algorithms. Theoretically, there is no need for cross-validation or a separate test set to get an estimate of the test set error when using RF because the OOB error estimate is generated internally.…”

Section: Cross-validationmentioning

confidence: 99%

Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data

Zhang

Kazakova

Moskal

et al. 2016

Forests

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Abstract:In precision forestry, tree species identification is key to evaluating the role of forest ecosystems in the provision of ecosystem services, such as carbon sequestration and assessing their effects on climate regulation and climate change. In this study, we investigated the effectiveness of tree species classification of urban forests using aerial-based HyMap hyperspectral imagery and light detection and ranging (LiDAR) data. First, we conducted an object-based image analysis (OBIA) to segment individual tree crowns present in LiDAR-derived Canopy Height Models (CHMs). Then, hyperspectral values for individual trees were extracted from HyMap data for band reduction through Minimum Noise Fraction (MNF) transformation which allowed us to reduce the data to 20 significant bands out of 118 bands acquired. Finally, we compared several different classifications using Random Forest (RF) and Multi Class Classifier (MCC) methods. Seven tree species were classified using all 118 bands which resulted in 46.3% overall classification accuracy for RF versus 79.6% for MCC. Using only the 20 optimal bands extracted through MNF, both RF and MCC achieved an increase in overall accuracy to 87.0% and 88.9%, respectively. Thus, the MNF band selection process is a preferable approach for tree species classification when using hyperspectral data. Further, our work also suggests that RF is heavily disadvantaged by the high-dimensionality and noise present in hyperspectral data, while MCC is more robust when handling high-dimensional datasets with small sample sizes. Our overall results indicated that individual tree species identification in urban forests can be accomplished with the fusion of object-based LiDAR segmentation of crowns and hyperspectral characterization.

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“…5 Airborne particulate matter (PM) is a complex mixture of ammonium, sulfates, nitrates, mineral dust, trace elements, and organic and elemental carbon (OC and EC), and it has become an important air quality indicator of ambient air because of its closely linked to air quality, human health problems, regional visibility, and global climate change, 2,[6][7][8][9][10] and much research has been done on PM. [11][12][13][14][15][16][17] Tianjin has been facing the serious PM2.5 pollution especially in winter. Sulfate, nitrate, ammonium, organic and element carbon have been found to comprise the major fraction of PM2.5.…”

Section: Introductionmentioning

confidence: 99%

Sensing the Instant Corrosivity of Haze Using Electrochemical Probes by Electrochemical Noise Technique

Ma¹,

Xia²,

Zhang³

et al. 2017

Electrochemistry

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Electrochemical noise (EN) was used to study the corrosivity of haze under Tianjin's urban atmosphere by using two electrochemical corrosion probes made by T91 and Q235B steels. Experimental results indicate that temperature, relative humidity and concentrations of pollutants (PM10, PM2.5, SO 2 , NO 2 and O 3 ) can affect the corrosion of T91 and Q235B atmospheric corrosion monitoring probes. However, the atmospheric humidity has a major effect on corrosion activities. The corrosivity of haze under low temperature and high relative humidity has a high level, which can promote the corrosion of T91 and Q235B probes; the effect of atmospheric pollutant concentration on atmospheric corrosivity is not apparent due to theirs low concentrations. T91 probe is suitable for long term corrosion monitoring, while Q235B probe is suitable for short time monitoring and has high detection precision.

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Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count

Cited by 79 publications

References 51 publications

Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods

Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods

Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data

Sensing the Instant Corrosivity of Haze Using Electrochemical Probes by Electrochemical Noise Technique

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