To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using computational fluid dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to design and verify the 3D CFD model for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification of the CFD models was conducted using the improved grid independence test (GIT) and wall Y⁺ approaches. The findings revealed that a grid resolution of 0.8 m and a first-layer height of 0.04 m were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R²) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. Subsequently, the aerodynamic characteristics and regional ventilation efficiency were analyzed in a 3D greenhouse model for developing a new large-scale greenhouse complex.

Piggery farming is the largest source of livestock manure in South Korea, yet greenhouse gas (GHG) data from piggery wastewater treatment systems remain limited. This study quantified methane (CH₄) and nitrous oxide (N₂O) fluxes from a full-scale treatment facility to develop stage-, seasonal-, and diurnal-specific emission factors. Continuous laser-based monitoring using a PVC air-pool chamber was applied across raw wastewater storage, an anoxic nitrogen-conversion reactor, and strongly aerated nitrification units. Mean CH₄ fluxes ranged from 1.1 to 15.6 mg s⁻¹ m⁻² peaking in summer, while N₂O fluxes ranged from 0.01 to 17,971 mg s⁻¹ m⁻², with maxima in fall. Emissions were dominated by two functional zones: aerated basins where vigorous mixing enhanced CH₄ stripping, and an upstream anoxic reactor where oxygen instability and nitrite accumulation produced extreme N₂O peaks. Derived emission factors were 0.11 kg CH₄ head⁻¹ yr⁻¹ and 45.2 kg N₂O head⁻¹ yr⁻¹, equivalent to 3.1 and 12,300 kg CO₂-eq head⁻¹ yr⁻¹. CH₄ variability was controlled mainly by treatment stage and temperature, whereas N₂O was governed by internal redox conditions. These results refine emission factors for inventories and underscore the need for improved aeration stability and denitrification control to reduce GHG emissions from piggery wastewater systems.

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using Computational Fluid Dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to develop a guideline for verifying CFD models for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification processes of the CFD models were conducted using 2D and 3D iterative simulations of a 2-hectare greenhouse model, using the improved Grid Independence Test (GIT) and wall Y+ approaches. Subsequently, the aerodynamic characteristics were analyzed in a 3D greenhouse model to access its performance when the wind direction was 90° in summer season. The findings revealed that a grid resolution of 0.8 meters and a first layer height of 0.04 meters were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R2) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. These results will serve as design standards for large-scale greenhouses.

Fine dust can have serious effects on human health and crop growth. Fodder crops can reduce airborne dust by coagulating soil particles and reducing wind speed on the surface and have the effect of reducing fine dust by adsorbing it on the crop surface. In this study, the dust reduction coefficient of crops was derived through a self-manufactured wind tunnel experiment to quantitatively assess the dust reduction effect of crops by type and planting density. Additionally, a dust reduction formula considering crop growth and weather conditions during the cultivation period was derived. The dust reduction coefficient was measured by the gravimetric method and the real-time size distribution of dust concentration before and after the crop. The PM reduction coefficient showed triticale at PM-2.5 82.2 mg/m³, PM-10 120 mg/m³, and barley at PM-2.5 14.5 mg/m³, PM-10 26.9 mg/m³ under moderate planting density. During the general planting density cultivation period of triticale and barley, PM-10 was reduced by 37.8 kg/ha and 8.5 kg/ha, respectively, and PM-2.5 was reduced by 25.9 kg/ha and 4.6 kg/ha. The dust reduction effect during the cultivation period was up to 126.1 kg/ha in terms of PM-10 when triticale was cultivated with densely sowing planting density.

Piggery farming is the largest source of livestock manure in South Korea, generating about 40% of total livestock waste annually. Yet greenhouse gas (GHG) emission data from piggery wastewater treatment systems remain limited, with most studies focused on farm slurry storage rather than process-level emissions. This study quantified methane (CH₄) and nitrous oxide (N₂O) fluxes from a full-scale piggery wastewater treatment facility in, to develop process, season, specific and diurnal specific emission fluxes. Continuous monitoring with a laser-based gas analyzer and customized PVC air-pool chamber was conducted across raw, anaerobic, and aerobic wastewater treatment stages. Mean CH₄ fluxes ranged 1.1-15.6 mg s⁻¹ m⁻², peaking in summer, while N₂O fluxes ranged 0.01-17971 mg s⁻¹ m⁻², with maxima in fall. Aeration tank II and Anaerobic tank I were the dominant emission stages, with night and intra-day peaks. Statistical analysis identified treatment stage and temperature as the main controls on CH₄ variability (p = 0.006 to 0.014), whereas N₂O showed weaker climatic sensitivity. The results provide refined emission factors and emphasize that aeration optimization and denitrification control are key to reducing GHG emissions from livestock wastewater systems in warm, humid regions.