17 citations as recorded by crossref.

1. Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method Y. Tian et al. 10.1016/j.chemosphere.2015.12.132
2. Characteristics and source apportionment of PM2.5 in Jiaxing, China Z. Zhao et al. 10.1007/s11356-019-04205-2
3. Large global variations in measured airborne metal concentrations driven by anthropogenic sources J. McNeill et al. 10.1038/s41598-020-78789-y
4. SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications G. Snider et al. 10.5194/amt-8-505-2015
5. Fine particulate matter in the tropical environment: monsoonal effects, source apportionment, and health risk assessment M. Khan et al. 10.5194/acp-16-597-2016
6. Potential sources and processes affecting speciated atmospheric mercury at Kejimkujik National Park, Canada: comparison of receptor models and data treatment methods X. Xu et al. 10.5194/acp-17-1381-2017
7. Hotspots of black carbon and PM2.5 in an urban area and relationships to traffic characteristics A. Targino et al. 10.1016/j.envpol.2016.07.027
8. Assessing contributions of natural surface and anthropogenic emissions to atmospheric mercury in a fast-developing region of eastern China from 2015 to 2018 X. Qin et al. 10.5194/acp-20-10985-2020
9. An iterative method for evaluating the inter-comparability between chemical mass balance and multivariate receptor models G. Argyropoulos et al. 10.1016/j.chemolab.2016.03.032
10. Fire Influences on Atmospheric Composition, Air Quality and Climate A. Voulgarakis & R. Field 10.1007/s40726-015-0007-z
11. Characteristics of atmospheric mercury in a suburban area of east China: sources, formation mechanisms, and regional transport X. Qin et al. 10.5194/acp-19-5923-2019
12. Approaches for identifying PM2.5 source types and source areas at a remote background site of South China in spring K. Zhang et al. 10.1016/j.scitotenv.2019.07.178
13. Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning X. Qin et al. 10.5194/acp-22-15851-2022
14. Evaluating the potential of waste plastics as fuel in cement kilns using bench-scale emissions analysis E. Asamany et al. 10.1016/j.fuel.2016.12.054
15. Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm J. Zhang et al. 10.1016/j.scitotenv.2021.150149
16. Quantifying variation in occupational air pollution exposure within a small metropolitan region of Brazil W. Pattinson et al. 10.1016/j.atmosenv.2018.03.011
17. Simulation and Forecasting Study on the Influential Factors of PM2.5 Related to Energy Consumption in the Beijing–Tianjin–Hebei Region D. Li et al. 10.3390/su16083152