Intermediate-volatility organic compounds (IVOCs) emitted from vehicles are important precursors to secondary organic aerosols (SOAs) in urban areas, yet vehicular emission of IVOCs, particularly from on-road fleets, is poorly understood. Here we initiated a field campaign to collect IVOCs with sorption tubes at both the inlet and the outlet in a busy urban tunnel ( >30 000  vehicles per day) in south China for characterizing emissions of IVOCs from on-road vehicles. The average emission factor of IVOCs (EF IVOCs ) was measured to be 16.77±0.89  mg km −1 (average ±95  % CI, confidence interval) for diesel and gasoline vehicles in the fleets, and based on linear regression, the average EF IVOCs was derived to be 62.79±18.37  mg km −1 for diesel vehicles and 13.95±1.13  mg km −1 for gasoline vehicles. The EF IVOCs for diesel vehicles from this study was comparable to that reported previously for non-road engines without after-treatment facilities, while the EF IVOCs for gasoline vehicles from this study was much higher than that recently tested for a China V gasoline vehicle. IVOCs from the on-road fleets did not show significant correlation with the primary organic aerosol (POA) or total non-methane hydrocarbons (NMHCs) as results from previous chassis dynamometer tests. Estimated SOA production from the vehicular IVOCs and VOCs surpassed the POA by a factor of ∼2.4 , and IVOCs dominated over VOCs in estimated SOA production by a factor of ∼7 , suggesting that controlling IVOCs is of greater importance to modulate traffic-related organic aerosol (OA) in urban areas. The results demonstrated that although on-road gasoline vehicles have much lower EF IVOCs , they contribute more IVOCs than on-road diesel vehicles due to its dominance in the on-road fleets. However, due to greater diesel than gasoline fuel consumption in China, emission of IVOCs from diesel engines would be much larger than that from gasoline engines, signaling the overwhelming contribution of IVOC emissions by non-road diesel engines in China.

Fine particulate matter with a diameter of less than 2.5  µm ( PM 2.5 ) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM 2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM 2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM 2.5 . Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM 2.5 dataset for China (i.e., ChinaHigh PM 2.5 ) at a 5  km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM 2.5 estimates (number of data samples  =  1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV- R 2   =  0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49  µg m −3 , respectively. Our model captures well the PM 2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00 LT (GMT + 8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.

The National Air Quality Forecast Capability (NAQFC) operated in the US National Oceanic and Atmospheric Administration (NOAA) provides the operational forecast guidance for ozone and fine particulate matter with aerodynamic diameters less than 2.5  µ m (PM 2.5 ) over the contiguous 48 US states (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. The existing NAQFC uses climatological chemical lateral boundary conditions (CLBCs), which cannot capture pollutant intrusion events originating outside of the model domain. In this study, we developed a model framework to use dynamic CLBCs from the Goddard Earth Observing System Model, version 5 (GEOS) to drive NAQFC. A mapping of the GEOS chemical species to CMAQ's CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species was developed. The utilization of the GEOS dynamic CLBCs in NAQFC showed the best overall performance in simulating the surface observations during the Saharan dust intrusion and Canadian wildfire events in summer 2015. The simulated PM 2.5 was improved from 0.18 to 0.37, and the mean bias was reduced from −6.74  to −2.96   µ g m −3 over CONUS. Although the effect of CLBCs on the PM 2.5 correlation was mainly near the inflow boundary, its impact on the background concentrations reached further inside the domain. The CLBCs could affect background ozone concentrations through the inflows of ozone itself and its precursors, such as CO. It was further found that the aerosol optical thickness (AOT) from satellite retrievals correlated well with the column CO and elemental carbon from GEOS. The satellite-derived AOT CLBCs generally improved the model performance for the wildfire intrusion events during a summer 2018 case study and demonstrated how satellite observations of atmospheric composition could be used as an alternative method to capture the air quality effects of intrusions when the CLBCs of global models, such as GEOS CLBCs, are not available.

Satellite retrievals of the column-averaged dry air mole fractions of CO 2 (XCO 2 ) could help to improve carbon flux estimation due to their good spatial coverage. In this study, in order to assimilate the GOSAT (Greenhouse Gases Observing Satellite) XCO 2 retrievals, the Global Carbon Assimilation System (GCAS) is upgraded with new assimilation algorithms, procedures, a localization scheme, and a higher assimilation parameter resolution. This upgraded system is referred to as GCASv2. Based on this new system, the global terrestrial ecosystem (BIO) and ocean (OCN) carbon fluxes from 1 May 2009 to 31 December 2015 are constrained using the GOSAT ACOS (Atmospheric CO 2 Observations from Space) XCO 2 retrievals (Version 7.3). The posterior carbon fluxes from 2010 to 2015 are independently evaluated using CO 2 observations from 52 surface flask sites. The results show that the posterior carbon fluxes could significantly improve the modeling of atmospheric CO 2 concentrations, with global mean bias decreases from a prior value of 1.6  ±  1.8 ppm to − 0.5  ±  1.8 ppm. The uncertainty reduction (UR) of the global BIO flux is 17 %, and the highest monthly regional UR could reach 51 %. Globally, the mean annual BIO and OCN carbon sinks and their interannual variations inferred in this study are very close to the estimates of CarbonTracker 2017 (CT2017) during the study period, and the inferred mean atmospheric CO 2 growth rate and its interannual changes are also very close to the observations. Regionally, over the northern lands, the strongest carbon sinks are seen in temperate North America, followed by Europe, boreal Asia, and temperate Asia; in the tropics, there are strong sinks in tropical South America and tropical Asia, but a very weak sink in Africa. This pattern is significantly different from the estimates of CT2017, but the estimated carbon sinks for each continent and some key regions like boreal Asia and the Amazon are comparable or within the range of previous bottom-up estimates. The inversion also changes the interannual variations in carbon fluxes in most TransCom land regions, which have a better relationship with the changes in severe drought area (SDA) or leaf area index (LAI), or are more consistent with previous estimates for the impact of drought. These results suggest that the GCASv2 system works well with the GOSAT XCO 2 retrievals and shows good performance with respect to estimating the surface carbon fluxes; meanwhile, our results also indicate that the GOSAT XCO 2 retrievals could help to better understand the interannual variations in regional carbon fluxes.

The National Air Quality Forecast Capability (NAQFC) operated in the US National Oceanic and Atmospheric Administration (NOAA) provides the operational forecast guidance for ozone and fine particulate matter with aerodynamic diameters less than 2.5  µ m (PM 2.5 ) over the contiguous 48 US states (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. The existing NAQFC uses climatological chemical lateral boundary conditions (CLBCs), which cannot capture pollutant intrusion events originating outside of the model domain. In this study, we developed a model framework to use dynamic CLBCs from the Goddard Earth Observing System Model, version 5 (GEOS) to drive NAQFC. A mapping of the GEOS chemical species to CMAQ's CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species was developed. The utilization of the GEOS dynamic CLBCs in NAQFC showed the best overall performance in simulating the surface observations during the Saharan dust intrusion and Canadian wildfire events in summer 2015. The simulated PM 2.5 was improved from 0.18 to 0.37, and the mean bias was reduced from −6.74  to −2.96   µ g m −3 over CONUS. Although the effect of CLBCs on the PM 2.5 correlation was mainly near the inflow boundary, its impact on the background concentrations reached further inside the domain. The CLBCs could affect background ozone concentrations through the inflows of ozone itself and its precursors, such as CO. It was further found that the aerosol optical thickness (AOT) from satellite retrievals correlated well with the column CO and elemental carbon from GEOS. The satellite-derived AOT CLBCs generally improved the model performance for the wildfire intrusion events during a summer 2018 case study and demonstrated how satellite observations of atmospheric composition could be used as an alternative method to capture the air quality effects of intrusions when the CLBCs of global models, such as GEOS CLBCs, are not available.

Fine particulate matter with a diameter of less than 2.5  µm ( PM 2.5 ) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM 2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM 2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM 2.5 . Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM 2.5 dataset for China (i.e., ChinaHigh PM 2.5 ) at a 5  km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM 2.5 estimates (number of data samples  =  1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV- R 2   =  0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49  µg m −3 , respectively. Our model captures well the PM 2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00 LT (GMT + 8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.