|
[1]
|
Estimating PM2. 5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017
|
|
2021 |
|
|
|
|
[2]
|
Spatiotemporal Variations in Particulate Matter and Air Quality over China: National, Regional and Urban Scales
|
|
2021 |
|
|
|
|
[3]
|
Relationship between Visibility, Air Pollution Index and Annual Mortality Rate in Association with the Occurrence of Rainfall—A Probabilistic Approach
|
|
Energies,
2021 |
|
|
|
|
[4]
|
Hysteretic effects of meteorological conditions and their interactions on particulate matter in Chinese cities
|
|
2020 |
|
|
|
|
[5]
|
An eigenvector spatial filtering based spatially varying coefficient model for PM2. 5 concentration estimation: A case study in Yangtze River Delta region of China
|
|
2019 |
|
|
|
|
[6]
|
Haze Formation During Winter in Delhi.
|
|
2018 |
|
|
|
|
[7]
|
Haze Formation During Winter in Delhi
|
|
2018 |
|
|
|
|
[8]
|
Investigation of PM10, PM2. 5 and PM1 during Pollution Episodes: Fog and Diwali Festival
|
|
2018 |
|
|
|
|
[9]
|
A method for the spectral analysis and identification of Fog, Haze and Dust storm using MODIS data
|
|
2017 |
|
|
|
|
[10]
|
Assessment of PM2.5 chemical compositions in Delhi: primary vs secondary emissions and contribution to light extinction coefficient and visibility degradation
|
|
2017 |
|
|
|
|
[11]
|
Determination of organic particle composition in Ankara atmosphere and investigation of their contribution to receptor modeling
|
|
2017 |
|
|
|
|
[12]
|
Spatial Distributions, Chemical Properties, and Sources of Ambient Particulate Matters in China
|
|
Air Pollution in Eastern Asia: An Integrated Perspective,
2017 |
|
|
|
|
[13]
|
Selective ensemble based on extreme learning machine and improved discrete artificial fish swarm algorithm for haze forecast
|
|
Applied Intelligence,
2017 |
|
|
|
|
[14]
|
厦门市冬季大气 PM_ (2.5) 中有机碳和元素碳的污染特征
|
|
地球与环境,
2016 |
|
|
|
|
[15]
|
Exploring spatiotemporal patterns of PM2. 5 in China based on ground-level observations for 190 cities
|
|
Environmental Pollution,
2016 |
|
|
|
|
[16]
|
Seasonal Chemical Characteristics of Atmospheric Aerosol Particles and its Light Extinction Coefficients over Pune, India
|
|
2016 |
|
|
|
|
[17]
|
Exploring spatiotemporal patterns of PM 2.5 in China based on ground-level observations for 190 cities
|
|
Environmental Pollution,
2016 |
|
|
|
|
[18]
|
Chemical characterization and source apportionment of atmospheric submicron particles on the western coast of Taiwan Strait, China
|
|
Journal of Environmental Sciences,
2016 |
|
|
|
|
[19]
|
Development of an on-line source-tagged model for sulfate, nitrate and ammonium: A modeling study for highly polluted periods in Shanghai, China
|
|
Environmental Pollution,
2016 |
|
|
|
|
[20]
|
Assessment of PM2. 5 chemical compositions in Delhi: primary vs secondary emissions and contribution to light extinction coefficient and visibility degradation
|
|
Journal of Atmospheric Chemistry,
2016 |
|
|
|
|
[21]
|
Review on the Recent PM 2.5 Studies in China
|
|
2015 |
|
|
|
|
[22]
|
Artificial intelligence based approach to forecast PM 2.5 during haze episodes: A case study of Delhi, India
|
|
Atmospheric Environment,
2015 |
|
|
|
|
[23]
|
Atmospheric Deposition of 7Be in the Southeast of China: A Case Study in Xiamen
|
|
Aerosol and Air Quality Research,
2015 |
|
|
|
|
[24]
|
최근 중국의 초미세먼지 오염 연구 동향
|
|
Journal of Korean Society for Atmospheric Environment,
2015 |
|
|
|
|
[25]
|
Artificial intelligence based approach to forecast PM2. 5 during haze episodes: A case study of Delhi, India
|
|
Atmospheric Environment,
2015 |
|
|
|
|
[26]
|
Review on the Recent PM2.5 Studies in China
|
|
2015 |
|
|
|
|
[27]
|
Impacts of the high loadings of primary and secondary aerosols on light extinction at Delhi during wintertime
|
|
Atmospheric Environment,
2014 |
|
|
|