In this study, we developed a data assimilation (DA) system for chemical transport model (CTM) simulations using an ensemble Kalman filter (EnKF) technique. This DA technique is easy to implement in an existing system without seriously modifying the original CTM and can provide flow-dependent corrections based on error covariance by short-term ensemble propagations. First, the PM_2.5 observations at ground stations were assimilated in this DA system every 6 h over South Korea for the period of the KORUS–AQ campaign from 1 May to 12 June 2016. The DA performances with the EnKF were then compared to a control run (CTR) without DA and a run with three-dimensional variational (3D-Var) DA. Consistent improvements owing to the initial conditions (ICs) assimilated with the EnKF were found in the DA experiments at a 6 h interval compared to the CTR run and to the run with 3D-Var. In addition, we attempted to assimilate the ground observations from China to examine the impacts of improved boundary conditions (BCs) on the PM_2.5 predictability over South Korea. The contributions of the ICs and BCs to improvements in the PM_2.5 predictability were also quantified. For example, the relative reductions in terms of the normalized mean bias (NMB) were found to be approximately 27.2 % for the 6 h reanalysis run. A series of 24 h PM_2.5 predictions were additionally conducted each day at 00:00 UTC with the optimized ICs. The relative reduction of the NMB was 17.3 % for the 24 h prediction run when the updated ICs were applied at 00:00 UTC. This means that after the application of the updated BCs, an additional 9.0 % reduction in the NMB was achieved for 24 h PM_2.5 predictions in South Korea.

To study regional-scale carbon dioxide (CO₂) transport, temporal variability, and budget over the Southern California Air Basin (SoCAB) during the California Research at the Nexus of Air Quality and Climate Change (CalNex) 2010 campaign period, a model that couples the Weather Research and Forecasting (WRF) Model with the Vegetation Photosynthesis and Respiration Model (VPRM) has been used. Our numerical simulations use anthropogenic CO₂ emissions of the Hestia Project 2010 fossil-fuel CO₂ emissions data products along with optimized VPRM parameters at “FLUXNET” sites, for biospheric CO₂ fluxes over SoCAB. The simulated meteorological conditions have been validated with ground and aircraft observations, as well as with background CO₂ concentrations from the coastal Palos Verdes site. The model captures the temporal pattern of CO₂ concentrations at the ground site at the California Institute of Technology in Pasadena, but it overestimates the magnitude in early daytime. Analysis of CO₂ by wind directions reveals the overestimate is due to advection from the south and southwest, where downtown Los Angeles is located. The model also captures the vertical profile of CO₂ concentrations along with the flight tracks. The optimized VPRM parameters have significantly improved simulated net ecosystem exchange at each vegetation-class site and thus the regional CO₂ budget. The total biospheric contribution ranges approximately from −24% to −20% (daytime) of the total anthropogenic CO₂ emissions during the study period.

With the demand for clean energy increasing, novel research is presented in this paper on providing sustainable, clean energy for a university campus. The Internet of Things (IoT) is now a leading factor in saving energy. With added deep learning for action recognition, IoT sensors implemented in real-time appliances monitor and control the extra usage of energy in buildings. This gives an extra edge on digitizing energy usage and, ultimately, reducing the power load in the electric grid. Here, we present a novel proposal through context-aware architecture for energy saving in classrooms, combining Internet of Things (IoT) sensors and video action recognition. Using this method, we can save a significant amount of energy usage in buildings.

We conducted regional scale CO₂ simulations using the Weather Research and Forecasting model (WRF) coupled with the Vegetation Photosynthesis and Respiration Model (VPRM). We contrasted simulated concentrations with column, ground and aircraft observations during the Korea-United States Air Quality (KORUS-AQ) 2016 field campaign. Overall, WRF-VPRM slightly underestimates CO₂ concentrations at ground and column monitoring sites, but it significantly underestimates at an inland tower measurement site, especially within the stable (nocturnal) boundary layer in nighttime. The model successfully captures the airborne vertical profiles but showed a large offset within the planetary boundary layer (PBL) in the areas surrounding Seoul and around the Taeahn point source emissions in the west coastal area of the Korean Peninsula. A case study flight intended to capture Chinese influence observed no clear signals of long-range transport of CO₂, due mainly to the much larger magnitude of background CO₂ concentrations. The calculated Net Ecosystem Exchange (NEE) with flux measurements at a tower site in the South Korean Peninsula has also been evaluated comparing with CO₂ flux measurements at a flux tower site, resulting in the underestimation by less than a factor of 1.

For the purpose of providing reliable and robust air quality predictions, an air quality prediction system was developed for the main air quality criteria species in South Korea (PM₁₀, PM_2.5, CO, O₃ and SO₂). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multiscale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate (${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ ), nitrate (${\mathrm{NO}}_{\mathrm{3}}^{-}$ ), ammonium (${\mathrm{NH}}_{\mathrm{4}}^{+}$ ), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O₃ and SO₂). In this sense, this new air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM₁₀ (index of agreement IOA =0.60; mean bias MB $=-\mathrm{13.54}$ ) and PM_2.5 (IOA =0.71; MB $=-\mathrm{2.43}$ ) as compared to the BASE RUN for PM₁₀ (IOA =0.51; MB $=-\mathrm{27.18}$ ) and PM_2.5 (IOA =0.67; MB $=-\mathrm{9.9}$ ). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O₃ and SO₂, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO₂ mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO₃ and peroxyacetyl nitrates – PANs – in the ground-observed NO₂ mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLHs) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free-parameter scheme, dust episode prediction and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the 3D variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean air quality prediction system (KAQPS).