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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.
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 StyleYusi 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 StyleYusi 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.
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.
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 StyleShuqiong 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 StyleShuqiong 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.
Background: Few studies have previously explored the relationship between hand, foot, and mouth disease (HFMD) and meteorological factors with the effect modification of air pollution, and these studies had inconsistent findings. We therefore applied a time-series analysis assessing the effects of temperature and humidity on the incidence of HFMD in Wuhan, China to deepen our understanding of the relationship between meteorological factors and the risk of HFMD. Methods: Daily HFMD cases were retrieved from Hubei Provincial Center for Disease Control and Prevention from 1 February 2013 to 31 January 2017. Daily meteorological data including 24 h average temperature, relative humidity, wind velocity, and atmospheric pressure were obtained from Hubei Meteorological Bureau. Data on Air pollution was collected from 10 national air-monitoring stations in Wuhan city. We adopted a distributed lag non-linear model (DLNM) combined with Poisson regression and time-series analysis to estimate the effects of temperature and relative humidity on the incidence HFMD. Results: We found that the association between temperature and HFMD incidence was non-linear, exhibiting an approximate “M” shape with two peaks occurring at 2.3 °C (RR = 1.760, 95% CI: 1.218–2.542) and 27.9 °C (RR = 1.945, 95% CI: 1.570–2.408), respectively. We observed an inverted “V” shape between relative humidity and HFMD. The risk of HFMD reached a maximum value at a relative humidity of 89.2% (RR = 1.553, 95% CI: 1.322–1.824). The largest delayed cumulative effects occurred at lag 6 for temperature and lag 13 for relative humidity. Conclusions: The non-linear relationship between meteorological factors and the incidence of HFMD on different lag days could be used in the early targeted warning system of infectious diseases, reducing the possible outbreaks and burdens of HFMD among sensitive populations.
Jiayuan Hao; Zhiyi Yang; Wenwen Yang; Shuqiong Huang; Liqiao Tian; Zhongmin Zhu; Yuanan Lu; Hao Xiang; Suyang Liu; Yang. Impact of Ambient Temperature and Relative Humidity on the Incidence of Hand-Foot-Mouth Disease in Wuhan, China. International Journal of Environmental Research and Public Health 2020, 17, 428 .
AMA StyleJiayuan Hao, Zhiyi Yang, Wenwen Yang, Shuqiong Huang, Liqiao Tian, Zhongmin Zhu, Yuanan Lu, Hao Xiang, Suyang Liu, Yang. Impact of Ambient Temperature and Relative Humidity on the Incidence of Hand-Foot-Mouth Disease in Wuhan, China. International Journal of Environmental Research and Public Health. 2020; 17 (2):428.
Chicago/Turabian StyleJiayuan Hao; Zhiyi Yang; Wenwen Yang; Shuqiong Huang; Liqiao Tian; Zhongmin Zhu; Yuanan Lu; Hao Xiang; Suyang Liu; Yang. 2020. "Impact of Ambient Temperature and Relative Humidity on the Incidence of Hand-Foot-Mouth Disease in Wuhan, China." International Journal of Environmental Research and Public Health 17, no. 2: 428.
Previous studies have estimated the association between meteorological factors and mumps outbreaks without assessing the influence of air pollution. In this research, we explored the effects of short-term exposure to air pollution on the incidence of mumps. Our time-series analysis was conducted using data collected in Wuhan, China from 2015 to 2017. Daily number of mumps cases was obtained from Disease Reporting System in Hubei Provincial Center for Disease Control and Prevention. Data on air pollution was obtained from 10 national air quality monitoring stations, including nitrogen dioxide (NO2), sulfur dioxide (SO2), ground-level ozone (O3), particulate matter less than or equal to 10 μm in aerodynamic diameter (PM10), and particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5). Daily meteorological data including temperature and relative humidity were obtained from Hubei Meteorological Bureau. We performed a Poisson regression in generalized additive models (GAM) to explore the association between the incidence of mumps and exposure to air pollution. We observed that the effects of air pollutants were statistically significant mainly in two periods, lag 0 to lag 5 and lag 20 to lag 25, with the strongest effects appearing at lag 2 and lag 23. The cumulative effects were stronger than single-day lag effects. The stratified analysis showed the effect of pollutants during the hot season was stronger than that during the cold season, especially for NO2 and SO2. We found that exposure to NO2 and SO2 was significantly associated with higher risk of developing mumps. Our findings could help deepen the understanding of how air pollution exposure affects the incidence of mumps.
Jiayuan Hao; Zhiyi Yang; Shuqiong Huang; Wenwen Yang; Zhongmin Zhu; Liqiao Tian; Yuanan Lu; Hao Xiang; Suyang Liu. The association between short-term exposure to ambient air pollution and the incidence of mumps in Wuhan, China: A time-series study. Environmental Research 2019, 177, 108660 .
AMA StyleJiayuan Hao, Zhiyi Yang, Shuqiong Huang, Wenwen Yang, Zhongmin Zhu, Liqiao Tian, Yuanan Lu, Hao Xiang, Suyang Liu. The association between short-term exposure to ambient air pollution and the incidence of mumps in Wuhan, China: A time-series study. Environmental Research. 2019; 177 ():108660.
Chicago/Turabian StyleJiayuan Hao; Zhiyi Yang; Shuqiong Huang; Wenwen Yang; Zhongmin Zhu; Liqiao Tian; Yuanan Lu; Hao Xiang; Suyang Liu. 2019. "The association between short-term exposure to ambient air pollution and the incidence of mumps in Wuhan, China: A time-series study." Environmental Research 177, no. : 108660.
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.
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 StyleZhongmin 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 StyleZhongmin 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.
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.
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 StyleWei 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 StyleWei 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.
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.
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 StyleTianhao 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 StyleTianhao 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.
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.
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 StyleKun 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 StyleKun 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.
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.
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 StyleTianhao 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 StyleTianhao 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.
This study reviewed the prediction of fine particulate matter (PM2.5) from satellite aerosol optical depth (AOD) and summarized the advantages and limitations of these predicting models. A total of 116 articles were included from 1436 records retrieved. The number of such studies has been increasing since 2003. Among these studies, four predicting models were widely used: Multiple Linear Regression (MLR) (25 articles), Mixed-Effect Model (MEM) (23 articles), Chemical Transport Model (CTM) (16 articles) and Geographically Weighted Regression (GWR) (10 articles). We found that there is no so-called best model among them and each has both advantages and limitations. Regarding the prediction accuracy, MEM performs the best, while MLR performs worst. CTM predicts PM2.5 better on a global scale, while GWR tends to perform well on a regional level. Moreover, prediction performance can be significantly improved by combining meteorological variables with land use factors of each region, instead of only considering meteorological variables. In addition, MEM has advantages in dealing with the AOD data with missing values. We recommend that with the help of higher resolution AOD data, future works could be focused on developing satellite-based predicting models for the prediction of historical PM2.5 and other air pollutants.
Yuanyuan Chu; Yisi Liu; Xiangyu Li; Zhiyong Liu; Hanson Lu; Yuanan Lu; Zongfu Mao; Xi Chen; Na Li; Meng Ren; Feifei Liu; Liqiao Tian; Zhongmin Zhu; Hao Xiang. A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth. Atmosphere 2016, 7, 129 .
AMA StyleYuanyuan Chu, Yisi Liu, Xiangyu Li, Zhiyong Liu, Hanson Lu, Yuanan Lu, Zongfu Mao, Xi Chen, Na Li, Meng Ren, Feifei Liu, Liqiao Tian, Zhongmin Zhu, Hao Xiang. A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth. Atmosphere. 2016; 7 (10):129.
Chicago/Turabian StyleYuanyuan Chu; Yisi Liu; Xiangyu Li; Zhiyong Liu; Hanson Lu; Yuanan Lu; Zongfu Mao; Xi Chen; Na Li; Meng Ren; Feifei Liu; Liqiao Tian; Zhongmin Zhu; Hao Xiang. 2016. "A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth." Atmosphere 7, no. 10: 129.
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.
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 StyleTianhao 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 StyleTianhao 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.
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.
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 StyleTianhao 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 StyleTianhao 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.
Ultrafine particles with a diameter below 1 μm are strongly linked to traffic and industrial emissions, causing a growing global health concern. In order to reveal the characteristics of ultrafine particles in central China, which makes up the sparse research in industrial cities of a developing country, particle number concentrations (PNC) together with meteorological parameters and concentrations of trace gases were measured over one year in Wuhan. The number concentration of ultrafine particles peaked in winter and was the lowest in summer across the entire size range monitored. Further, particles with a diameter smaller than 30 nm increased dramatically in concentration with decreasing diameter. The monthly averaged number concentrations of particles discriminated in three size ranges formed a near- inverse parabolic distribution peaking in January. This trend is supported by a negative correlation between PNC and precipitation, temperature, and mixing layer height, which emphasizes the effect of these meteorological parameters on scouring, convection, and diffusion of particles. However, since wind not only disperses particulate matter but also brings in exogenous particles, wind speed plays an equivocal role in particle number concentrations. The diurnal analysis indicates that hourly measurements of trace gases concentrations could be used as a proxy for dense industrial activities and to reveal some complex chemical reactions. The results of this study offer reasonable estimations of particle impacts and provide references for policymaking of emission control in the industrial cities of developing countries.
Tianhao Zhang; Zhongmin Zhu; Wei Gong; Hao Xiang; Ying Li; Zhenzhen Cui. Characteristics of Ultrafine Particles and Their Relationships with Meteorological Factors and Trace Gases in Wuhan, Central China. Atmosphere 2016, 7, 96 .
AMA StyleTianhao Zhang, Zhongmin Zhu, Wei Gong, Hao Xiang, Ying Li, Zhenzhen Cui. Characteristics of Ultrafine Particles and Their Relationships with Meteorological Factors and Trace Gases in Wuhan, Central China. Atmosphere. 2016; 7 (8):96.
Chicago/Turabian StyleTianhao Zhang; Zhongmin Zhu; Wei Gong; Hao Xiang; Ying Li; Zhenzhen Cui. 2016. "Characteristics of Ultrafine Particles and Their Relationships with Meteorological Factors and Trace Gases in Wuhan, Central China." Atmosphere 7, no. 8: 96.
The estimation of ambient particulate matter with diameter less than 10 µm (PM10) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM10 more accessible to relevant departments and scientific research institutions, a semi-physical geographically weighted regression (GWR) model was established in this study to estimate nationwide mass concentrations of PM10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters. The results demonstrated that applying physics-based corrections could remarkably improve the quality of the dataset for better model performance with the adjusted R2 between PM10 and AOD increasing from 0.08 to 0.43, and the fitted results explained approximately 81% of the variability in the corresponding PM10 mass concentrations. Annual average PM10 concentrations estimated by the semi-physical GWR model indicated that many residential regions suffer from severe particle pollution. Moreover, the deviation in estimation, which primarily results from the frequent changes in elevation, the spatially heterogeneous distribution of monitoring sites, and the limitations of AOD retrieval algorithm, was acceptable. Therefore, the semi-physical GWR model provides us with an effective and efficient method to estimate PM10 at large scale. The results could offer reasonable estimations of health impacts and provide guidance on emission control strategies in China.
Tianhao Zhang; Wei Gong; Zhongmin Zhu; Kun Sun; Yusi Huang; Yuxi Ji. Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model. Atmosphere 2016, 7, 88 .
AMA StyleTianhao Zhang, Wei Gong, Zhongmin Zhu, Kun Sun, Yusi Huang, Yuxi Ji. Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model. Atmosphere. 2016; 7 (7):88.
Chicago/Turabian StyleTianhao Zhang; Wei Gong; Zhongmin Zhu; Kun Sun; Yusi Huang; Yuxi Ji. 2016. "Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model." Atmosphere 7, no. 7: 88.