Atmospheric ammonia (NH₃) plays a critical role in PM_2.5 pollution. Data on atmospheric NH₃ are scanty; thus, the role of NH₃ in the formation of ammonium ions (NH₄⁺) in various environments is understudied. Herein, we measured concentrations of NH₃, PM_2.5, and its water-soluble SO₄²⁻, NO₃⁻, and NH₄⁺ ions (SNA) at an urban site in Jeonju, South Korea from May 2019 to April 2020. During the measurement period, the average concentrations of NH₃ and PM_2.5 were 10.5 ± 4.8 ppb and 24.0 ± 12.8 μg/m³, respectively, and SNA amounted to 4.3 ± 3.1, 4.4 ± 4.9, and 1.6 ± 1.8 μg/m³, respectively. A three-dimensional photochemical model analysis revealed that a major portion of NH₃, more than 88%, originated from Korea. The enhancement of the ammonium-to-total ratio of NH₃, NH_X (NHR = [NH₄⁺]/[NH₄⁺] + [NH₃]) was observed up to ~0.61 during the increase of PM_2.5 concentration (PM_2.5 ≥ 25 μg/m³) under low temperature and high relative humidity conditions, particularly in winter. The PM_2.5 and SNA concentrations increased exponentially as NHR increased, indicating that NH₃ contributed significantly to SNA formation by gas-to-particle conversion. Our study provided experimental evidence that atmospheric NH₃ in the urban area significantly contributed to SNA formation through gas-to-particle conversion during PM_2.5 pollution episodes.

Ammonia (NH₃) plays an important role in air quality and atmospheric chemistry, yet studies on the characteristics and impacts of NH₃ are limited. Herein, we revealed the spatial distribution of atmospheric NH₃, as measured by passive samplers, at three different sites (R1, R2, and R3) in the rural area (livestock environment) of Jeongeup, South Korea, from September 2019 to August 2020. At site R1, the boundary of a large-scale pig farm, dramatically high daily mean concentrations of NH₃ were observed (118.7 ppb), whereas sites R2 and R3, located ~1 km from R1, exhibited lower concentrations of 18.2 and 30.4 ppb, respectively. In the rural environment, the monthly NH₃ variations showed a peak in June (34.2 ppb), which was significantly higher than in the urban and remote areas. To examine the impact of NH₃ from the rural area on a nearby urban area in June 2020, simultaneous measurements were performed using a real-time instrument in Jeonju. When high NH₃ events occurred in the urban area in June, the results for the NH₃ concentrations and observed meteorological conditions in the rural and urban areas showed that the rural area influenced the NH₃ levels in the adjacent urban area.

Conventional methods for monitoring ammonia (NH₃) emissions from livestock farms have several challenges, such as a poor environment for measurement, difficulty in accessing livestock, and problems with long-term measurement. To address these issues, we applied various neural network models for the long-term prediction of NH₃ concentrations from sow farms in this study. Environmental parameters, including temperature, humidity, ventilation rate, and past records of NH₃ concentrations, were given as inputs to the models. These neural network models took the encoder or the feature extracting parts from the representative deep learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Transformer, to encode temporal patterns of time series. However, all of these models adopted dense layers for the decoder to format the task of long-term prediction as a regression problem. Due to their regression nature, all models showed a robust performance in predicting long-term NH₃ concentrations at a scale of weeks or even months despite there being a relatively short period of input signals (a few days to a week). Given one week of input, LSTM showed the minimum mean absolute errors (MAE) of 1.83, 1.78, and 1.87 ppm for the prediction of one, two, and three weeks, respectively, whereas Transformer performed best with a MAE of 1.73 ppm for a four-week prediction. In the long-term estimation of spanning months, LSTM showed the minimum MAEs of 1.95 and 1.90 ppm when trained on predicting two and three weeks of windows. At the same condition, Transformer gave the minimum MAEs of 1.87 and 1.83 when trained on predicting one and four weeks of windows. Overall, the neural network models can facilitate the prediction of national-level NH₃ emissions, the development of mitigation strategies for NH₃-derived air pollutants, odor management, and the monitoring of animal-rearing environments. Further, their integration of real-time measurement devices can significantly prolong device longevity and offer substantial cost savings.

Despite being recognized as toxic in anaerobic systems, sulfide’s potential to enhance hydrogen fermentation via microbial modulation remains underexplored. This study evaluated the combined effects of sulfide concentration (0–800 mg S/L) and heat pretreatment on hydrogen production during dark fermentation (DF). Without pretreatment, hydrogen yield reached 83 ± 2 mL/g COD at 0 mg S/L but declined with increasing sulfide, becoming negligible at 800 mg S/L. In contrast, heat-pretreated inocula showed markedly improved performance: peak cumulative production (4628 ± 17 mL) and yield (231 ± 1 mL/g COD) were attained at 200 mg S/L, while the maximum production rate (1462 ± 64 mL/h) occurred at 400 mg S/L. These enhancements coincided with elevated acetic and butyric acids, indicating a metabolic shift toward hydrogen-producing pathways. The microbial analysis of heat-pretreated samples revealed an enrichment of Clostridium butyricum (from 73.1% to 87.5%) and Clostridium perfringens, which peaked at 13.5% at 400 mg S/L. This species contributed to butyric acid synthesis. At 800 mg S/L, Clostridium perfringens declined sharply to 0.6%, while non-hydrogenogenic Levilinea saccharolytica proliferated, correlating with reduced butyric acid and hydrogen output. These findings indicate that sulfide supplementation, when combined with heat pretreatment, selectively restructures microbial communities and metabolic pathways, enhancing DF performance.

Among the 316,299 tons of ammonia emissions in Korea, the highest emission is from livestock manure. Therefore, ammonia emission control in the pig industry is an urgent priority. In this study, the ammonia concentration of a mechanically-ventilation sow house and diurnal patterns were analyzed to calculate ammonia emission factors. This study was carried out for 91 days from September to November 2021 in a pig house located in Danyang, Chungcheongbuk-do. For repeated experiments, three pig rooms with the same environmental conditions were selected and monitored for the same period. The average ammonia concentration in each room was measured to be 11.09 ppm, 13.30 ppm, and 12.73 ppm, and the ventilation rates were 50.14, 61.88, and 66.82 m³/h/pig. The ammonia emission factor was 8.2 ± 1.3, 12.7 ± 2.7, and 12.8 ± 2.2 g/day/pig in rooms 1 to 3, respectively. In this study, the average value of 11.2 g/day/pig was calculated as the final ammonia emission factor for a sow house in fall.