Research on Influential Factors of PM2.5 within the Beijing-Tianjin-Hebei Region in China

Table 1

Information on influencing factors and analyzing methods in related papers.

Authors	Research subjects	Influencing factors	Correlation analysis method	Geographical area
Dewangan et al.; (2016) [7]	PM2.5 and PM10	Cultural; ritual	Empirical	Raipur, India
Gilbraith and Powers (2013) [8]	Air pollutant emissions	Residential demand response	Graphical analysis	New York, USA
Amodio et al. (2013) [9]	PM2.5 and PM10	Steel plant	Principal; component; analysis	Europe
Wang et al. (2006) [10]	SO2 and total suspended particles (TSP)	Industry	Sensitivity analyses; Graphical analysis	Beijing, Dalian, Jinan, Chongqing, Liuzhou, China
Jaramillo and Muller (2016) [11]	Air pollution emissions	Energy production	Pearson’s correlation coefficients	USA
Herrera et al. (2013) [12]	Air pollution	Decentralized power generation	Scenario analysis	Santa Clara City, Cuba
Carreras-Sospedra et al. (2010) [13]	Air quality	Central power generation; Distributed generation	Scenario analysis	California, USA
Ma et al.; (2013) [14]	Air pollutant emissions	Wind power generation	Empirical	Xinjiang, China
Genon et al.; (2009) [15]	CO2, NOx, SOx, PM	Small district heating systems	Dispersion model	Italy
Lobscheid et al. (2012) [16]	Air pollutants emitted	On-road vehicles	Graphical analysis	USA
Targino et al. (2016) [17]	Black carbon and PM2.5	Traffic	Graphical analysis	Londrina city,; Brazil
Muresan et al.; (2015) [18]	Exhaust emissions	Earthwork machines	Graphical analysis	France
Gonzalez-de-Soto et al. (2016) [19]	Air pollution	Hybrid-powered robotic; tractors	Graphical analysis	Spain
Megaritis et al.; (2014) [20]	PM2.5	Temperature, wind speed, absolute humidity, precipitation and mixing height	Three-dimensional chemical transport model	Europe