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Tianhao Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

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Journal article
Published: 16 April 2021 in Atmospheric Research
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Fine particle matter (PM2.5) significantly affects the atmospheric environment and human health. The satellite-derived aerosol optical depth (AOD), which could represent the concentration of atmospheric particles to a certain extent, is widely used for estimating ambient PM2.5 concentration, in combination with diverse auxiliary information. However, the general satellite-derived PM2.5 products exhibit limitation in the application and aggregate analysis of PM2.5 in urban areas, because of the moderate spatial resolution to match the urban landscape and low spatial coverage making it hard to describe airmass trajectory. In order to explore the potential application value of PM2.5 concentration products with relatively high spatial coverage and resolution, a two-stage machine learning and geo-statistics coupled model incorporating with a feedback mechanism was proposed in this study. To be specific, we firstly develop a hybrid back-propagation neural network coupled kriging with external drifting approach (BPNN-KED) for estimating 1-km daily PM2.5 concentration maps at high coverage over four urban agglomerations in China. The model performs well, with R2 up to 0.83 and root mean square error of 14.7 μg/m3 from cross-validation. The daily PM2.5 maps display an average spatial coverage exceeding 95%, and on an average, each grid produces 350 days of valid estimations annually. In addition, the extra value of the high-coverage PM2.5 estimates were explored through the more accurate aggregate analysis of urban PM2.5 pollution level. The advantage of the high-coverage PM2.5 estimation is demonstrated through daily PM2.5 hotspot identification over urban areas, providing substantially fine spatially resolved PM2.5 trends, which offers the potential for daily pollutant emission sources location through satellite remote sensing technology. Moreover, the spatiotemporally continuous PM2.5 concentrations possess the ability to capture polluted air mass trajectories, thereby offering observational support not only for evaluating the contribution from exogenous pollutants to local PM2.5 concentrations and but also for providing empirical references for haze warning.

ACS Style

Yusi Huang; Tianhao Zhang; Zhongmin Zhu; Wei Gong; Xinghui Xia. PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application. Atmospheric Research 2021, 258, 105628 .

AMA Style

Yusi Huang, Tianhao Zhang, Zhongmin Zhu, Wei Gong, Xinghui Xia. PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application. Atmospheric Research. 2021; 258 ():105628.

Chicago/Turabian Style

Yusi Huang; Tianhao Zhang; Zhongmin Zhu; Wei Gong; Xinghui Xia. 2021. "PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application." Atmospheric Research 258, no. : 105628.

Journal article
Published: 28 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%-92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.

ACS Style

Xinghui Xia; Bin Zhao; Tianhao Zhang; Luyao Wang; Yu Gu; Kuo-Nan Liou; Feiyue Mao; Boming Liu; Yanchen Bo; Yusi Huang; Jiadan Dong; Wei Gong; Zhongmin Zhu. Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -13.

AMA Style

Xinghui Xia, Bin Zhao, Tianhao Zhang, Luyao Wang, Yu Gu, Kuo-Nan Liou, Feiyue Mao, Boming Liu, Yanchen Bo, Yusi Huang, Jiadan Dong, Wei Gong, Zhongmin Zhu. Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-13.

Chicago/Turabian Style

Xinghui Xia; Bin Zhao; Tianhao Zhang; Luyao Wang; Yu Gu; Kuo-Nan Liou; Feiyue Mao; Boming Liu; Yanchen Bo; Yusi Huang; Jiadan Dong; Wei Gong; Zhongmin Zhu. 2021. "Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-13.

Journal article
Published: 15 September 2020 in Remote Sensing
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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.

ACS Style

Zhao-Yue Chen; Jie-Qi Jin; Rong Zhang; Tian-Hao Zhang; Jin-Jian Chen; Jun Yang; Chun-Quan Ou; Yuming Guo. Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels. Remote Sensing 2020, 12, 3008 .

AMA Style

Zhao-Yue Chen, Jie-Qi Jin, Rong Zhang, Tian-Hao Zhang, Jin-Jian Chen, Jun Yang, Chun-Quan Ou, Yuming Guo. Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels. Remote Sensing. 2020; 12 (18):3008.

Chicago/Turabian Style

Zhao-Yue Chen; Jie-Qi Jin; Rong Zhang; Tian-Hao Zhang; Jin-Jian Chen; Jun Yang; Chun-Quan Ou; Yuming Guo. 2020. "Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels." Remote Sensing 12, no. 18: 3008.

Accepted manuscript
Published: 17 June 2020 in Environmental Research Letters
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Environmental changes induced by ongoing anthropogenic activities have caused severe lake degradation. Because of the lack of long-term records, few studies have investigated the change in Wuhan lakes, and the effect of human activities on regional lake changes prior to 1973 has not been systematically studied yet. Therefore, in this study, historical maps and Landsat images were combined to track these changes from the 1920s to 2015. Three phases could be identified over the nearly 100-year study period. The most dramatic lake reduction (−21.53 km2 yr-1) occurred during Phase II (1950s−1980s) rather than Phase III (after the 1980s), as indicated by previous studies; the decreased lake area in Phase II was almost double that in Phase III. This reduction could be attributed to major hydraulic engineering projects during Phase II based on the watershed-scale analysis. In addition, land-use conversion over the past 45 years was used to quantify the impact of human exploitation on lakes. The shrinkage of lakes was predominately driven by agricultural activities, such as reclamation (39.2%) and aquaculture development (29.0%), and urbanization was a secondary driving force (19.8%), despite the rapid economic development of Wuhan. This study therefore provides a practical guide for lake protection in other areas similar to Wuhan.

ACS Style

Jialin Wang; Xiaobin Cai; Fang Chen; Zhan Zhang; Yufang Zhang; Kun Sun; Tianhao Zhang; Xiaoling Chen. Hundred-year spatial trajectory of lake coverage changes in response to human activities over Wuhan. Environmental Research Letters 2020, 15, 094022 .

AMA Style

Jialin Wang, Xiaobin Cai, Fang Chen, Zhan Zhang, Yufang Zhang, Kun Sun, Tianhao Zhang, Xiaoling Chen. Hundred-year spatial trajectory of lake coverage changes in response to human activities over Wuhan. Environmental Research Letters. 2020; 15 (9):094022.

Chicago/Turabian Style

Jialin Wang; Xiaobin Cai; Fang Chen; Zhan Zhang; Yufang Zhang; Kun Sun; Tianhao Zhang; Xiaoling Chen. 2020. "Hundred-year spatial trajectory of lake coverage changes in response to human activities over Wuhan." Environmental Research Letters 15, no. 9: 094022.

Journal article
Published: 02 April 2020 in Atmospheric Research
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Accurate estimations of the concentration of ambient fine-particle matter with aerodynamic diameters of less than 2.5 μm (PM2.5) are necessary for human health studies. In this study, individual city-scale linear mixed effect models (LME) were employed to accurately estimate ground PM2.5 concentrations considering the spatiotemporal variability of the relationship between PM2.5 and atmospheric, meteorological, and land observations. The contributions of diverse influential factors including aerosol optical depth, planetary boundary layer height, relative humidity, vegetation index, and wind on local PM2.5 pollution were also determined. High correlation coefficient (R2 = 0.89) and low root mean square error (RMSE = 13.1 μg/m3) ensured satisfactory LME model performances in estimating ground-level PM2.5 concentrations. Spatiotemporal analyses of satellite-based PM2.5 showed high concentrations in eastern, southern, and northern Hubei, and low concentrations in the northwest and southeast because of unbalanced development. These analyses also displayed a mitigation trend of PM2.5 concentrations with a mean annual decline rate of 3–12% from 2016 to 2018. Moreover, from the statistical results of the model, the influential factor of aerosol optical depth was positively correlated with PM2.5 concentration, while planetary boundary layer height, relative humidity, and the normalized difference vegetation index were negatively correlated to local PM2.5 pollution. However, the winds had contradictory contributions on PM2.5 pollution; the northerly wind in western Hubei and the southerly and northeasterly winds in eastern Hubei alleviated local PM2.5 pollution, while the westerly wind in eastern Hubei facilitated PM2.5 diffusion between cities and aggravated PM2.5 pollution. The analysis of the spatiotemporal trend of local PM2.5 pollution at a city scale and the identification of the influence of wind on PM2.5 pollution provide a theoretical reference for regional pollution warnings and controls.

ACS Style

Yusi Huang; Yuxi Ji; Zhongmin Zhu; Tianhao Zhang; Wei Gong; Xinghui Xia; Hong Sun; Xiang Zhong; Xiangyang Zhou; Daoqun Chen. Satellite-based spatiotemporal trends of ambient PM2.5 concentrations and influential factors in Hubei, Central China. Atmospheric Research 2020, 241, 104929 .

AMA Style

Yusi Huang, Yuxi Ji, Zhongmin Zhu, Tianhao Zhang, Wei Gong, Xinghui Xia, Hong Sun, Xiang Zhong, Xiangyang Zhou, Daoqun Chen. Satellite-based spatiotemporal trends of ambient PM2.5 concentrations and influential factors in Hubei, Central China. Atmospheric Research. 2020; 241 ():104929.

Chicago/Turabian Style

Yusi Huang; Yuxi Ji; Zhongmin Zhu; Tianhao Zhang; Wei Gong; Xinghui Xia; Hong Sun; Xiang Zhong; Xiangyang Zhou; Daoqun Chen. 2020. "Satellite-based spatiotemporal trends of ambient PM2.5 concentrations and influential factors in Hubei, Central China." Atmospheric Research 241, no. : 104929.

Journal article
Published: 27 February 2020 in International Journal of Environmental Research and Public Health
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Tuberculosis (TB) has a very high mortality rate worldwide. However, only a few studies have examined the associations between short-term exposure to air pollution and TB incidence. Our objectives were to estimate associations between short-term exposure to air pollutants and TB incidence in Wuhan city, China, during the 2015–2016 period. We applied a generalized additive model to access the short-term association of air pollution with TB. Daily exposure to each air pollutant in Wuhan was determined using ordinary kriging. The air pollutants included in the analysis were particulate matter (PM) with an aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5), PM with an aerodynamic diameter less than or equal to 10 micrometers (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ground-level ozone (O3). Daily incident cases of TB were obtained from the Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Both single- and multiple-pollutant models were used to examine the associations between air pollution and TB. Seasonal variation was assessed by splitting the all-year data into warm (May–October) and cold (November–April) seasons. In the single-pollutant model, for a 10 μg/m3 increase in PM2.5, PM10, and O3 at lag 7, the associated TB risk increased by 17.03% (95% CI: 6.39, 28.74), 11.08% (95% CI: 6.39, 28.74), and 16.15% (95% CI: 1.88, 32.42), respectively. In the multi-pollutant model, the effect of PM2.5 on TB remained statistically significant, while the effects of other pollutants were attenuated. The seasonal analysis showed that there was not much difference regarding the impact of air pollution on TB between the warm season and the cold season. Our study reveals that the mechanism linking air pollution and TB is still complex. Further research is warranted to explore the interaction of air pollution and TB.

ACS Style

Shuqiong Huang; Hao Xiang; Wenwen Yang; Zhongmin Zhu; Liqiao Tian; Shiquan Deng; Tianhao Zhang; Yuanan Lu; Feifei Liu; Xiangyu Li; Suyang Liu. Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis. International Journal of Environmental Research and Public Health 2020, 17, 1522 .

AMA Style

Shuqiong Huang, Hao Xiang, Wenwen Yang, Zhongmin Zhu, Liqiao Tian, Shiquan Deng, Tianhao Zhang, Yuanan Lu, Feifei Liu, Xiangyu Li, Suyang Liu. Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis. International Journal of Environmental Research and Public Health. 2020; 17 (5):1522.

Chicago/Turabian Style

Shuqiong Huang; Hao Xiang; Wenwen Yang; Zhongmin Zhu; Liqiao Tian; Shiquan Deng; Tianhao Zhang; Yuanan Lu; Feifei Liu; Xiangyu Li; Suyang Liu. 2020. "Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis." International Journal of Environmental Research and Public Health 17, no. 5: 1522.

Journal article
Published: 04 December 2019 in Earth and Space Science
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Modifications such as degrading the retrieval quality of mixed pixels in the coastline area and revising surface characterization scheme, have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Dark Target (DT) aerosol optical depth (AOD) product to address inaccuracy over urban areas. In this study, comprehensive evaluations of modifications to the MODIS C6.1 DT AOD product, in comparison with Collection 6 (C6), are conducted over mainland China under different spatial distributions, seasons, and air quality for the period 2010‐2017, combined with validation against Aerosol Robotic Network (AERONET) AOD measurements. The preliminary result showed that the C6.1 DT AOD product in China displayed an overall good performance with high R2 (0.87) and low root mean squared error (RMSE =0.23) against ground measurements. Moreover, the C6.1 DT AOD product was an overall improvement over C6, with greater correlation and lower uncertainty against ground measurements, especially for the North China Plain (NCP) and Central China, although this was not the case for Western China. The improvement was also seasonal, being distinct in spring but less pronounced in winter, and negatively correlated with the level of air pollution. Furthermore, the analysis of DT AOD retrievals in different urbanized areas illustrated that the updated DT algorithm worked well in completely urbanized areas, where 97% of C6.1 DT AOD retrievals were an improvement over C6, while approximately 10% of C6.1 DT AOD retrievals were deteriorated in semi‐urbanized areas. Additionally, the DT AOD retrievals in areas with relatively low enhanced vegetation index (EVI) and high surface reflectance were significantly improved, mitigating problems associated with the DT algorithm, further improving the reliability of MODIS DT AOD products.

ACS Style

Y. Huang; B. Zhu; Z. Zhu; T. Zhang; W. Gong; Y. Ji; X. Xia; L. Wang; X. Zhou; D. Chen. Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface. Earth and Space Science 2019, 6, 2575 -2592.

AMA Style

Y. Huang, B. Zhu, Z. Zhu, T. Zhang, W. Gong, Y. Ji, X. Xia, L. Wang, X. Zhou, D. Chen. Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface. Earth and Space Science. 2019; 6 (12):2575-2592.

Chicago/Turabian Style

Y. Huang; B. Zhu; Z. Zhu; T. Zhang; W. Gong; Y. Ji; X. Xia; L. Wang; X. Zhou; D. Chen. 2019. "Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface." Earth and Space Science 6, no. 12: 2575-2592.

Journal article
Published: 06 May 2019 in Atmospheric Pollution Research
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Aerosols can modify cloud properties by affecting the concentration and size of cloud droplets and the strength of cloud convection. Understanding the effects of aerosol on cloud remains challenging because of the different effects of aerosol on various conditions and regions. In this study, we survey the effect of aerosol on cloud base height (CBH) through four-year ground-based observations in Wuhan, China. A robust positive correlation between aerosol loading and CBH can be found via annual and correlation analyses, which suggest the systematic invigoration of clouds through aerosol loading. The statistical analysis of collocated measurements of PM10 concentrations, meteorological factors, and CBHs suggest that temperature, relative humidity and aerosol particles collectively influence CBH variations. The present finding also demonstrate that the influence of aerosol particles on promoting CBH is prominent under the conditions of relatively high humidity and low temperature. This study provides a new perspective for understanding the response of aerosol-cloud interactions in an urban area, which will play important roles in regional climate change and human life.

ACS Style

Wei Wang; Tianhao Zhang; Zengxin Pan. Four-year ground-based observations of the aerosol effects on cloud base height in Wuhan, China. Atmospheric Pollution Research 2019, 10, 1531 -1535.

AMA Style

Wei Wang, Tianhao Zhang, Zengxin Pan. Four-year ground-based observations of the aerosol effects on cloud base height in Wuhan, China. Atmospheric Pollution Research. 2019; 10 (5):1531-1535.

Chicago/Turabian Style

Wei Wang; Tianhao Zhang; Zengxin Pan. 2019. "Four-year ground-based observations of the aerosol effects on cloud base height in Wuhan, China." Atmospheric Pollution Research 10, no. 5: 1531-1535.

Journal article
Published: 22 January 2019 in Atmospheric Environment
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Several studies have attempted to predict ground PM2.5 concentrations using satellite aerosol optical depth (AOD) retrieval. However, over 70%-90% of aerosol retrievals are non-random missing, which limits and biases the estimation. To the best of our knowledge, this issue has not been well resolved to date. The aim of this study was to develop an interpolation technique to handle the missing data retrieval problem and to estimate the daily PM2.5 for a high coverage dataset with 3-km resolution in China by fitting the complex temporal and spatial variations. We developed a two-step interpolation method (i.e., the mixed-effect model and inverse distance weighting technology) to replace the missing values in AOD. Next, the extreme gradient boosting (XGBoost) technique that includes a non-linear exposure-lag-response model (NELRM) was proposed and validated to estimate the daily levels of PM2.5 across China during 2014-2015. After two steps of interpolation, the missing value rate of daily AOD data was reduced from 87.91% to 13.83%. The cross-validation (CV) R-square, root mean square error (RMSE) and mean absolute percentage prediction error (MAPE) of the interpolation were 0.76, 0.10 and 21.41%, respectively. The cross-validation for the prediction of daily PM2.5 resulted in R2=0.86, RMSE=14.98, and MAPE=23.72%. The results of this study indicate that the two-step interpolation method can largely resolve the non-random missing data problem and that the combined XGBoost methods have a good ability to estimate fine particulate matter concentrations.

ACS Style

Zhaoyue Chen; Tian-Hao Zhang; Rong Zhang; Zhong-Min Zhu; Jun Yang; Ping-Yan Chen; Chun-Quan Ou; Yuming Guo. Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China. Atmospheric Environment 2019, 202, 180 -189.

AMA Style

Zhaoyue Chen, Tian-Hao Zhang, Rong Zhang, Zhong-Min Zhu, Jun Yang, Ping-Yan Chen, Chun-Quan Ou, Yuming Guo. Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China. Atmospheric Environment. 2019; 202 ():180-189.

Chicago/Turabian Style

Zhaoyue Chen; Tian-Hao Zhang; Rong Zhang; Zhong-Min Zhu; Jun Yang; Ping-Yan Chen; Chun-Quan Ou; Yuming Guo. 2019. "Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China." Atmospheric Environment 202, no. : 180-189.

Journal article
Published: 01 October 2018 in Remote Sensing of Environment
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Satellite-derived aerosol optical depth (AOD) has been widely used to estimate ground-level PM2.5 concentrations due to its spatially continuous observation. However, the coarse spatial resolutions (e.g., 3 km, 6 km, or 10 km) of the primary satellite AOD products have weakness to capture the characteristics of urban-scale PM2.5 patterns. Moreover, high-resolution (e.g., 1 km) PM2.5 estimations are still unable to be related to the urban landscape or to small geographical units, which is crucial for analyzing the urban pollution structure. In this study, the daily PM2.5 concentrations were estimated using the new AOD data with a 160 m spatial resolution retrieved by the Gaofen-1 (GF) wide field of view (WFV) along with the nested linear mixed effects model and ancillary variables from the Weather Research & Forecasting (WRF) meteorological simulation data. The experiment was conducted in Wuhan, Beijing, and Shanghai, which suffers from severe atmospheric fine particle pollution in recent years. The proposed model performed well for both GF and Moderate Resolution Imaging Spectroradiometer (MODIS), with slight over-fitting and little spatial autocorrelation. Regarding to the GF PM2.5 estimation, model fitting yielded R2 values of 0.96, 0.91 and 0.95 and mean prediction error (MPE) of 10.13, 11.89 and 7.34 μg/m3 for Wuhan, Beijing and Shanghai, respectively. The site-based cross validation achieved R2 values of 0.92, 0.88 and 0.89, and MPE of 13.69, 16.76 and 12.59 μg/m3 for Wuhan, Beijing and Shanghai, respectively. The day-of-years based cross validation resulted in R2 of 0.54, 0.58 and 0.50, and MPE of 30.46, 27.12 and 31.58 μg/m3 for Wuhan, Beijing and Shanghai, respectively, indicating that it was practicable to estimate the GF PM2.5 in the days without enough AOD-PM2.5 matchups. The ultrahigh resolution PM2.5 estimations offer substantial advantages for providing finer spatially resolved PM2.5 trends. Additionally, it offers new approaches to locate main PM2.5 emission sources, evaluate the local PM2.5 contribution proportion, and quantify the daily PM2.5 emission level via remote sensing techniques. Along with the joint observations via other high-resolution satellites, the temporal resolution of GF PM2.5 will be further improved. In all, this study not only provides possibilities for further applications in the precise analysis of urban inner PM2.5 pollution patterns but also establishes a foundation for constructing a high-resolution satellite air monitoring network in China.

ACS Style

Tianhao Zhang; Zhongmin Zhu; Wei Gong; Zerun Zhu; Kun Sun; Lunche Wang; Yusi Huang; Feiyue Mao; Huanfeng Shen; Zhiwei Li; Kai Xu. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals. Remote Sensing of Environment 2018, 216, 91 -104.

AMA Style

Tianhao Zhang, Zhongmin Zhu, Wei Gong, Zerun Zhu, Kun Sun, Lunche Wang, Yusi Huang, Feiyue Mao, Huanfeng Shen, Zhiwei Li, Kai Xu. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals. Remote Sensing of Environment. 2018; 216 ():91-104.

Chicago/Turabian Style

Tianhao Zhang; Zhongmin Zhu; Wei Gong; Zerun Zhu; Kun Sun; Lunche Wang; Yusi Huang; Feiyue Mao; Huanfeng Shen; Zhiwei Li; Kai Xu. 2018. "Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals." Remote Sensing of Environment 216, no. : 91-104.

Journal article
Published: 01 August 2018 in Atmospheric Research
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Planetary boundary layer height (PBLH) has important implications for human health, weather forecasting, ecology, and climate change. This study aims to investigate the characteristics of the PBLH above Wuhan, China. We propose a new procedure (wavelet covariance and the ideal curve-fitting algorithm) to reveal PBLHs based on the Cloud-Aerosol LIDAR and Infrared Pathfinder Satellite Observations (CALIPSO) attenuated backscatter ratio. Under cloud situation, the results of PBLHs revealed from CALIPSO show a relatively low correlation (R2 = 0.55) with PBLHs determined using a thermodynamic method. And the results show a significant correlation coefficient (R2 = 0.86) when the cloudy scenarios are eliminated. Because CALIPSO could have mistakenly classified cloud tops as PBLHs during the formation of stratocumulus clouds. Characteristics of annual and seasonal variations of the PBLH for all sky conditions from June 2006 to September 2013 were also studied. Because of the climatic and geographic characteristics of Wuhan City, the PBLHs display clear annual and seasonal variations. Warmer seasons have deeper PBLHs, while colder seasons are characterized by shallower PBLHs. Over 90% of daytime PBLHs in Wuhan are between 400 and 1800 m, while over 90% of nocturnal PBLHs clustered between 200 m and 1000 m. This research will contribute to improving PBLH input parameters for numerical models and enhance the understanding of the urban planetary boundary layer.

ACS Style

Zhongmin Zhu; Miao Zhang; Yusi Huang; Bo Zhu; Ge Han; Tianhao Zhang; Boming Liu. Characteristics of the planetary boundary layer above Wuhan, China based on CALIPSO. Atmospheric Research 2018, 214, 204 -212.

AMA Style

Zhongmin Zhu, Miao Zhang, Yusi Huang, Bo Zhu, Ge Han, Tianhao Zhang, Boming Liu. Characteristics of the planetary boundary layer above Wuhan, China based on CALIPSO. Atmospheric Research. 2018; 214 ():204-212.

Chicago/Turabian Style

Zhongmin Zhu; Miao Zhang; Yusi Huang; Bo Zhu; Ge Han; Tianhao Zhang; Boming Liu. 2018. "Characteristics of the planetary boundary layer above Wuhan, China based on CALIPSO." Atmospheric Research 214, no. : 204-212.

Conference paper
Published: 01 July 2018 in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Comprehensive research was conducted to analyze the characteristics of haze pollution during winter in Wuhan based on data for winter 2014-2015. The results demonstrated that haze pollution could be divided into two types. Type-1 lasted for 1-2 days and peak values of PM 2.5 exceeded 200 ug.m -3 , Type-2 displayed a long duration of 5-6 days, and the hourly concentrations of PM2.5 ranged from 100 to 200 ug·m -3 . Meanwhile, our results showed that type-1 haze pollution was mainly due to photochemical pollution process caused by high relative humidity (RH). Type-2 haze pollution was mainly caused by the accumulation of anthropogenic pollutants near the surface. Both haze pollution in winter was mainly fine-mode particles, and sometimes coarse-mode particles appeared. The characteristics of haze pollution revealed in this study can be used in regional climate modeling and can provide guidance to the government regarding prevention of haze pollution over central China.

ACS Style

Boming Liu; Yingying Ma; Wei Gong; Tianhao Zhang; Yifan Shi. Study of Haze Pollution During Winter in Wuhan, China. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 7568 -7571.

AMA Style

Boming Liu, Yingying Ma, Wei Gong, Tianhao Zhang, Yifan Shi. Study of Haze Pollution During Winter in Wuhan, China. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():7568-7571.

Chicago/Turabian Style

Boming Liu; Yingying Ma; Wei Gong; Tianhao Zhang; Yifan Shi. 2018. "Study of Haze Pollution During Winter in Wuhan, China." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 7568-7571.

Journal article
Published: 11 October 2017 in Remote Sensing
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As China is suffering from severe fine particle pollution from dense industrialization and urbanization, satellite-derived aerosol optical depth (AOD) has been widely used for estimating particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5). However, the correlation between satellite AOD and ground-level PM2.5 could be influenced by aerosol vertical distribution, as satellite AOD represents the entire column, rather than just ground-level concentration. Here, a new column-to-surface vertical correction scheme is proposed to improve separation of the near-surface and elevated aerosol layers, based on the ratio of the integrated extinction coefficient within 200–500 m above ground level (AGL), using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)) aerosol profile products. There are distinct differences in climate, meteorology, terrain, and aerosol transmission throughout China, so comparisons between vertical correction via CALIOP ratio and planetary boundary layer height (PBLH) were conducted in different regions from 2014 to 2015, combined with the original Pearson coefficient between satellite AOD and ground-level PM2.5 for reference. Furthermore, the best vertical correction scheme was suggested for different regions to achieve optimal correlation with PM2.5, based on the analysis and discussion of regional and seasonal characteristics of aerosol vertical distribution. According to our results and discussions, vertical correction via PBLH is recommended in northwestern China, where the PBLH varies dramatically, stretching or compressing the surface aerosol layer; vertical correction via the CALIOP ratio is recommended in northeastern China, southwestern China, Central China (excluding summer), North China Plain (excluding Beijing), and the spring in the southeast coast, areas that are susceptible to exogenous aerosols and exhibit the elevated aerosol layer; and original AOD without vertical correction is recommended in Beijing and the southeast coast (excluding spring), where the elevated aerosol layer rarely occurs and a large proportion of aerosol is aggregated in near-surface. Moreover, validation experiments in 2016 agreed well with our discussions and conclusions drawn from the experiments of the first two years. Furthermore, suggested vertical correction scheme was applied into linear mixed effect (LME) model, and high cross validation (CV) R2 (~85%) and relatively low root mean square errors (RMSE, ~20 μg/m3) were achieved, which demonstrated that the PM2.5 estimation agreed well with the measurements. When compared to the original situation, CV R2 values and RMSE after vertical correction both presented improvement to a certain extent, proving that the suggested vertical correction schemes could further improve the estimation accuracy of PM2.5 based on sophisticated model in China. Estimating PM2.5 with better accuracy could contribute to a more precise research of ecology and epidemiology, and provide a reliable reference for environmental policy making by governments.

ACS Style

Wei Gong; Yusi Huang; Tianhao Zhang; Zhongmin Zhu; Yuxi Ji; Hao Xiang. Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China. Remote Sensing 2017, 9, 1038 .

AMA Style

Wei Gong, Yusi Huang, Tianhao Zhang, Zhongmin Zhu, Yuxi Ji, Hao Xiang. Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China. Remote Sensing. 2017; 9 (10):1038.

Chicago/Turabian Style

Wei Gong; Yusi Huang; Tianhao Zhang; Zhongmin Zhu; Yuxi Ji; Hao Xiang. 2017. "Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China." Remote Sensing 9, no. 10: 1038.

Journal article
Published: 26 July 2017 in Remote Sensing
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Active remote sensing of atmospheric XCO2 has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO2-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO2 globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system’s retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO2-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading.

ACS Style

Ge Han; Xin Ma; Ailin Liang; Tianhao Zhang; Yannan Zhao; Miao Zhang; Wei Gong. Performance Evaluation for China’s Planned CO2-IPDA. Remote Sensing 2017, 9, 768 .

AMA Style

Ge Han, Xin Ma, Ailin Liang, Tianhao Zhang, Yannan Zhao, Miao Zhang, Wei Gong. Performance Evaluation for China’s Planned CO2-IPDA. Remote Sensing. 2017; 9 (8):768.

Chicago/Turabian Style

Ge Han; Xin Ma; Ailin Liang; Tianhao Zhang; Yannan Zhao; Miao Zhang; Wei Gong. 2017. "Performance Evaluation for China’s Planned CO2-IPDA." Remote Sensing 9, no. 8: 768.

Proceedings article
Published: 01 July 2017 in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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To evaluate the performance of the Orbiting Carbon Observatory 2 (OCO-2) Lite File Product (Lite_FP) which has the highest amount of data and the highest utilization efficiency among the three products of OCO-2, we compared global atmospheric CO2 observations for 20 months (September 2014 to April 2016) with GGG2014 data from the Total Carbon Column Observing Network (TCCON). We considered the latitude distribution of the TCCON sites and performed a site-by-site comparison at different latitude zones. The result demonstrated that the seasonal fluctuation of XCO2 from Lite_FP is consistent with TCCON, and the biases of XCO2 measurements ranged from -3 ppm to 4 ppm, with a 1% precision. Bias distribution differed in terms of latitude zones and observing modes. In addition, we analyzed the distribution characteristic of the bias of XCO2 observations under land target mode in detail combined with surface and atmospheric properties.

ACS Style

Ailin Liang; Ge Han; Hao Xu; Wei Gong; Tianhao Zhang. Evaluation of XCO2 from OCO-2 Lite File Product compared with TCCON data. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017, 2070 -2073.

AMA Style

Ailin Liang, Ge Han, Hao Xu, Wei Gong, Tianhao Zhang. Evaluation of XCO2 from OCO-2 Lite File Product compared with TCCON data. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017; ():2070-2073.

Chicago/Turabian Style

Ailin Liang; Ge Han; Hao Xu; Wei Gong; Tianhao Zhang. 2017. "Evaluation of XCO2 from OCO-2 Lite File Product compared with TCCON data." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 2070-2073.

Journal article
Published: 02 April 2017 in Remote Sensing
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The Moderate Resolution Imaging Spectroradiometer (MODIS) provides widespread Aerosol Optical Depth (AOD) datasets for climatological and environmental health research. Since MODIS AOD clearly lacks coverage in orbit-scanning gaps and cloud obscuration, some applications will benefit from data recovery using multi-temporal AOD. Aimed at qualitatively describing the relationship between multi-temporal AOD, AOD loadings and Normalized Difference Vegetation Index (NDVI) have been considered based on the mechanism of satellite AOD retrieval. Accordingly, the NDVI-based Weighted Linear Regression (NWLR) has been proposed to recover AOD by synthetically weighing AOD similarity, spatial proximity, and NDVI similarity. To evaluate the performance of AOD recovery, simulated experiments applying gap and window masks were conducted in South Asia and Beijing, respectively. The evaluation results demonstrated that the linear regression R2 achieved 0.8 and the absolute relative errors remained steady. Further validation was conducted between the recovered and actual AODs using 56 Aerosol Robotic Network (AERONET) sites in East and South Asia from 2013 to 2015, which demonstrated that over 41% of recovered AODs fell within the expected error (EE) envelope. Additional validation conducted in South Asia and Beijing showed that recovery by NWLR did not expand satellite-derived AOD errors, and the accuracy of recovered AOD was consistent with the accuracy of the original Aqua MODIS Deep Blue (DB) AOD. The recovery results illustrated that AOD coverage was improved in most regions, especially in North China, Mongolia, and South Asia, which could provide better support in aerosol spatio-temporal analysis and aerosol data assimilation.

ACS Style

Tianhao Zhang; Chao Zeng; Wei Gong; Lunche Wang; Kun Sun; Huanfeng Shen; Zhongmin Zhu; Zerun Zhu. Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis. Remote Sensing 2017, 9, 340 .

AMA Style

Tianhao Zhang, Chao Zeng, Wei Gong, Lunche Wang, Kun Sun, Huanfeng Shen, Zhongmin Zhu, Zerun Zhu. Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis. Remote Sensing. 2017; 9 (4):340.

Chicago/Turabian Style

Tianhao Zhang; Chao Zeng; Wei Gong; Lunche Wang; Kun Sun; Huanfeng Shen; Zhongmin Zhu; Zerun Zhu. 2017. "Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis." Remote Sensing 9, no. 4: 340.

Journal article
Published: 19 January 2017 in Remote Sensing
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Aerosol Optical Depth (AOD) is crucial for urban air quality assessment. However, the frequently used moderate-resolution imaging spectroradiometer (MODIS) AOD product at 10 km resolution is too coarse to be applied in a regional-scale study. Gaofen-1 (GF-1) wide-field-of-view (WFV) camera data, with high spatial and temporal resolution, has great potential in estimation of AOD. Due to the lack of shortwave infrared (SWIR) band and complex surface reflectivity brought from high spatial resolution, it is difficult to retrieve AOD from GF-1 WFV data with traditional methods. In this paper, we propose an improved AOD retrieval algorithm for GF-1 WFV data. The retrieved AOD has a spatial resolution of 160 m and covers all land surface types. Significant improvements in the algorithm include: (1) adopting an improved clear sky composite method by using the MODIS AOD product to identify the clearest days and correct the background atmospheric effect; and (2) obtaining local aerosol models from long-term CIMEL sun-photometer measurements. Validation against MODIS AOD and ground measurements showed that the GF-1 WFV AOD has a good relationship with MODIS AOD (R2 = 0.66; RMSE = 0.27) and ground measurements (R2 = 0.80; RMSE = 0.25). Nevertheless, the proposed algorithm was found to overestimate AOD in some cases, which will need to be improved upon in future research.

ACS Style

Kun Sun; Xiaoling Chen; Zhongmin Zhu; Tianhao Zhang. High Resolution Aerosol Optical Depth Retrieval Using Gaofen-1 WFV Camera Data. Remote Sensing 2017, 9, 89 .

AMA Style

Kun Sun, Xiaoling Chen, Zhongmin Zhu, Tianhao Zhang. High Resolution Aerosol Optical Depth Retrieval Using Gaofen-1 WFV Camera Data. Remote Sensing. 2017; 9 (1):89.

Chicago/Turabian Style

Kun Sun; Xiaoling Chen; Zhongmin Zhu; Tianhao Zhang. 2017. "High Resolution Aerosol Optical Depth Retrieval Using Gaofen-1 WFV Camera Data." Remote Sensing 9, no. 1: 89.

Journal article
Published: 07 December 2016 in International Journal of Environmental Research and Public Health
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Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM2.5) is currently quite limited in China. By introducing NO2 and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO2 and EVI, where cross-validation R2 increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO2 and EVI in GWR models could more effectively estimate PM2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China.

ACS Style

Tianhao Zhang; Wei Gong; Wei Wang; Yuxi Ji; Zhongmin Zhu; Yusi Huang. Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI). International Journal of Environmental Research and Public Health 2016, 13, 1215 .

AMA Style

Tianhao Zhang, Wei Gong, Wei Wang, Yuxi Ji, Zhongmin Zhu, Yusi Huang. Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI). International Journal of Environmental Research and Public Health. 2016; 13 (12):1215.

Chicago/Turabian Style

Tianhao Zhang; Wei Gong; Wei Wang; Yuxi Ji; Zhongmin Zhu; Yusi Huang. 2016. "Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI)." International Journal of Environmental Research and Public Health 13, no. 12: 1215.

Journal article
Published: 30 September 2016 in International Journal of Environmental Research and Public Health
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The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China.

ACS Style

Tianhao Zhang; Gang Liu; Zhongmin Zhu; Wei Gong; Yuxi Ji; Yusi Huang. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model. International Journal of Environmental Research and Public Health 2016, 13, 974 .

AMA Style

Tianhao Zhang, Gang Liu, Zhongmin Zhu, Wei Gong, Yuxi Ji, Yusi Huang. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model. International Journal of Environmental Research and Public Health. 2016; 13 (10):974.

Chicago/Turabian Style

Tianhao Zhang; Gang Liu; Zhongmin Zhu; Wei Gong; Yuxi Ji; Yusi Huang. 2016. "Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model." International Journal of Environmental Research and Public Health 13, no. 10: 974.

Journal article
Published: 10 August 2016 in International Journal of Environmental Research and Public Health
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Atmospheric fine particles (diameter < 1 μm) attract a growing global health concern and have increased in urban areas that have a strong link to nucleation, traffic emissions, and industrial emissions. To reveal the characteristics of fine particles in an industrial city of a developing country, two-year measurements of particle number size distribution (15.1 nm–661 nm), meteorological parameters, and trace gases were made in the city of Wuhan located in central China from June 2012 to May 2014. The annual average particle number concentrations in the nucleation mode (15.1 nm–30 nm), Aitken mode (30 nm–100 nm), and accumulation mode (100 nm–661 nm) reached 4923 cm−3, 12193 cm−3 and 4801 cm−3, respectively. Based on Pearson coefficients between particle number concentrations and meteorological parameters, precipitation and temperature both had significantly negative relationships with particle number concentrations, whereas atmospheric pressure was positively correlated with the particle number concentrations. The diurnal variation of number concentration in nucleation mode particles correlated closely with photochemical processes in all four seasons. At the same time, distinct growth of particles from nucleation mode to Aitken mode was only found in spring, summer, and autumn. The two peaks of Aitken mode and accumulation mode particles in morning and evening corresponded obviously to traffic exhaust emissions peaks. A phenomenon of “repeated, short-lived” nucleation events have been created to explain the durability of high particle concentrations, which was instigated by exogenous pollutants, during winter in a case analysis of Wuhan. Measurements of hourly trace gases and segmental meteorological factors were applied as proxies for complex chemical reactions and dense industrial activities. The results of this study offer reasonable estimations of particle impacts and provide references for emissions control strategies in industrial cities of developing countries.

ACS Style

Tianhao Zhang; Zhongmin Zhu; Wei Gong; Hao Xiang; Ruimin Fang. Characteristics of Fine Particles in an Urban Atmosphere—Relationships with Meteorological Parameters and Trace Gases. International Journal of Environmental Research and Public Health 2016, 13, 807 .

AMA Style

Tianhao Zhang, Zhongmin Zhu, Wei Gong, Hao Xiang, Ruimin Fang. Characteristics of Fine Particles in an Urban Atmosphere—Relationships with Meteorological Parameters and Trace Gases. International Journal of Environmental Research and Public Health. 2016; 13 (8):807.

Chicago/Turabian Style

Tianhao Zhang; Zhongmin Zhu; Wei Gong; Hao Xiang; Ruimin Fang. 2016. "Characteristics of Fine Particles in an Urban Atmosphere—Relationships with Meteorological Parameters and Trace Gases." International Journal of Environmental Research and Public Health 13, no. 8: 807.