Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment

[1] Hankey S(2015)Land use regression models of on-road particulate air pollution (Particle Number, Black Carbon, PM2.5, Particle Size) using mobile monitoring Environ Sci Technol 49 9194-202

[2] Marshall JD(2017)High-resolution air pollution mapping with google street view cars: Exploiting big data Environ Sci Technol 51 6999-7008

[3] Apte JS(2017)Robustness of land-use regression models developed from mobile air pollutant measurements Environ Sci Technol 51 3938-47

[4] Messier KP(2016)Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model Environ Sci Technol 50 12894-902

[5] Gani S(2017)Robustness of land-use regression models developed from mobile air pollutant measurements Environ Sci Technol 51 3938-47

[6] Brauer M(2018)Mapping air pollution with google street view cars: Efficient approaches with mobile monitoring and land use regression Environ Sci Technol 52 12563-72

[7] Kirchstetter TW(2016)A novel mobile monitoring approach to characterize spatial and temporal variation in traffic-related air pollutants in an urban community Atmos Environ 141 161-73

[8] Lunden MM(2015)Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses Atmos Environ 107 351-63

[9] Hatzopoulou M(2021)Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring Environ Int 154 106569-9

[10] Valois MF(2018)Development and comparison of air pollution exposure surfaces derived from on-road mobile monitoring and short-term stationary sidewalk measurements Environ Sci Technol 52 3512-25

[1] Hankey S(2015)Land use regression models of on-road particulate air pollution (Particle Number, Black Carbon, PM2.5, Particle Size) using mobile monitoring Environ Sci Technol 49 9194-202

[2] Marshall JD(2017)High-resolution air pollution mapping with google street view cars: Exploiting big data Environ Sci Technol 51 6999-7008

[3] Apte JS(2017)Robustness of land-use regression models developed from mobile air pollutant measurements Environ Sci Technol 51 3938-47

[4] Messier KP(2016)Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model Environ Sci Technol 50 12894-902

[5] Gani S(2017)Robustness of land-use regression models developed from mobile air pollutant measurements Environ Sci Technol 51 3938-47

[6] Brauer M(2018)Mapping air pollution with google street view cars: Efficient approaches with mobile monitoring and land use regression Environ Sci Technol 52 12563-72

[7] Kirchstetter TW(2016)A novel mobile monitoring approach to characterize spatial and temporal variation in traffic-related air pollutants in an urban community Atmos Environ 141 161-73

[8] Lunden MM(2015)Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses Atmos Environ 107 351-63

[9] Hatzopoulou M(2021)Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring Environ Int 154 106569-9

[10] Valois MF(2018)Development and comparison of air pollution exposure surfaces derived from on-road mobile monitoring and short-term stationary sidewalk measurements Environ Sci Technol 52 3512-25