Estimation of historical daily PM2.5 concentrations for three Chinese megacities: Insight into the socioeconomic factors affecting PM2.5

[1]  Runan Zhao,et al.  Differences in the Suitable Distribution Area between Northern and Southern China Landscape Plants , 2023, Plants.

[2]  C. Ou,et al.  Quantifying and characterizing the impacts of PM2.5 and humidity on atmospheric visibility in 182 Chinese cities: A nationwide time-series study , 2022, Journal of Cleaner Production.

[3]  Tong Li,et al.  Study on the Spatial and Temporal Distribution Characteristics and Influencing Factors of Particulate Matter Pollution in Coal Production Cities in China , 2022, International journal of environmental research and public health.

[4]  A. Kobbane,et al.  An Exploration of Features Impacting Respiratory Diseases in Urban Areas , 2022, International journal of environmental research and public health.

[5]  Jhoon Kim,et al.  Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD , 2021, Applied Sciences.

[6]  Itai Kloog,et al.  Gaussian Markov random fields improve ensemble predictions of daily 1 km PM2.5 and PM10 across France , 2021 .

[7]  Longwu Liang,et al.  Spatiotemporal evolution of PM2.5 concentrations in urban agglomerations of China , 2021, Journal of Geographical Sciences.

[8]  Y. Geng,et al.  The reallocation effect of China's provincial power transmission and trade on regional heavy metal emissions , 2021, iScience.

[9]  E. Garshick,et al.  Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing , 2021, Environment international.

[10]  Hai Zhang,et al.  Daily and Hourly Surface PM2.5 Estimation From Satellite AOD , 2021, Earth and Space Science.

[11]  M. Bashir,et al.  Analysis of daily and seasonal variation of fine particulate matter (PM2.5) for five cities of China , 2021, Environment, Development and Sustainability.

[12]  Howard H. Chang,et al.  Imputing Satellite-Derived Aerosol Optical Depth Using a Multi-Resolution Spatial Model and Random Forest for PM2.5 Prediction , 2021, Remote. Sens..

[13]  Hailong Wang,et al.  Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980-2019 using a machine learning approach. , 2020, The Science of the total environment.

[14]  K. Gładyszewska-Fiedoruk,et al.  Regression Model of PM2.5 Concentration in a Single-Family House , 2020, Sustainability.

[15]  Hui Xu,et al.  Assessment of the radiation effect of aerosols on maize production in China. , 2020, The Science of the total environment.

[16]  M. Song,et al.  Determinants of changes in electricity generation intensity among different power sectors , 2019, Energy Policy.

[17]  Xiaodong Liu,et al.  PM2.5 Pollution and Inhibitory Effects on Industry Development: A Bidirectional Correlation Effect Mechanism , 2019, International journal of environmental research and public health.

[18]  Renhe Zhang,et al.  The Effects of PM2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing , 2019, Journal of Geophysical Research: Atmospheres.

[19]  Huanfeng Shen,et al.  The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations. , 2019, Environmental pollution.

[20]  Weiqi Zhou,et al.  Urbanization strategy and environmental changes: An insight with relationship between population change and fine particulate pollution. , 2018, The Science of the total environment.

[21]  L. Shao,et al.  Carbon emission imbalances and the structural paths of Chinese regions , 2018 .

[22]  Li Li,et al.  The health economic loss of fine particulate matter (PM2.5) in Beijing , 2017 .

[23]  R. Zou,et al.  Variation in Tree Species Ability to Capture and Retain Airborne Fine Particulate Matter (PM2.5) , 2017, Scientific Reports.

[24]  Barry L. Nelson,et al.  Shapley Effects for Global Sensitivity Analysis: Theory and Computation , 2016, SIAM/ASA J. Uncertain. Quantification.

[25]  Mingxing Chen,et al.  Challenges and the way forward in China’s new-type urbanization , 2016 .

[26]  Zhen-bo Wang,et al.  Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration. , 2016, Chemosphere.

[27]  X. Bi,et al.  Changes in visibility with PM2.5 composition and relative humidity at a background site in the Pearl River Delta region. , 2016, Journal of environmental sciences.

[28]  Qian Zhang,et al.  Retrieving historical ambient PM2.5 concentrations using existing visibility measurements in Xi'an, Northwest China , 2016 .

[29]  Shuqing Zhao,et al.  Comparing the Spatiotemporal Dynamics of Urbanization in Moderately Developed Chinese Cities over the Past Three Decades: Case of Nanjing and Xi’an , 2015 .

[30]  Wei Chen,et al.  Diurnal, weekly and monthly spatial variations of air pollutants and air quality of Beijing , 2015 .

[31]  Judith C. Chow,et al.  Impacts of aerosol compositions on visibility impairment in Xi'an, China , 2012 .

[32]  P. Zhao,et al.  Long-term visibility trends and characteristics in the region of Beijing, Tianjin, and Hebei, China , 2011 .

[33]  I Roberts,et al.  Interventions for increasing pedestrian and cyclist visibility for the prevention of death and injuries. , 2006, The Cochrane database of systematic reviews.

[34]  Mingxing Chen,et al.  Build a people-oriented urbanization: China’s new-type urbanization dream and Anhui model , 2019, Land Use Policy.

[35]  Jing Duan,et al.  Characteristics and Relationship of PM, PM10, PM2.5 Concentration in a Polluted City in Northern China , 2015 .

[36]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[37]  Dag Tjøstheim,et al.  NOTES AND CORRESPONDENCE A Cautionary Note on the Use of the Kolmogorov-Smirnov Test for Normality , 2007 .

[38]  L. Breiman.  Random Forests , 2001, Machine Learning.