Search Results (8,479)

The Solid Oxide Fuel Cell (SOFC) system is a highly intricate system characterized by multiple variables and couplings. Developing an accurate model for the SOFC independent power generation system is of paramount importance. Conducting experimental studies on the SOFC system is costly, and it carries certain risks due to the requirements for pure hydrogen, high-temperature environments, and other factors. To address these challenges, a high-performing model that precisely reflects the inherent characteristics of the SOFC is essential for dynamic static analysis and the identification of optimal operating points. This paper presents a SOFC system model based on current controls, which was implemented in the MATLAB/Simulink environment, and it utilized a nodal approach for modeling. The model incorporated a cold air bypass, which enabled the more precise control of the SOFC reactor’s inlet and outlet temperatures. Furthermore, a 3D test and verification method are proposed in order to focus on the influence of input parameters on the four electrical characteristics, and four thermal characteristics, of output parameters. By conducting one-dimensional, two-dimensional, and three-dimensional studies of these output parameters, a more intuitive understanding of the system’s response to changes in input parameters was obtained. Under conditions wherein all other variables were kept constant, the entire system attained its maximum efficiency at approximately FU = 0.8, BP = 0, and AR = 6. The outcomes of this study have significant implications for exploring the optimal operating point in the SOFC independent power generation system in an in-depth manner. It provides valuable insights for enhancing the system’s efficiency and performance. Full article

Wind observations at multiple levels (40–200 m) have been conducted over a five-year time period (2016–2020) on Jeju Island of South Korea. This study aims to understand the vertical and temporal characteristics of the lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal wind. The maximum wind speed occurs in the relatively lower altitudes during daytime and is delayed in the relatively higher altitude after sunset in a diurnal cycle. In the summer season, the altitudes appear earlier than in other seasons via the dominant solar radiation effect during daytime, and the altitude after sunset increases up to 160 m. However, the maximum wind speed in the winter season occurs irregularly among altitudes, and it is lower than that in the summer season. This can be attributed to the increase in the mean wind speed in the diurnal cycle caused by the strong northwestern wind in the winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal winds. These results are expected to contribute to site selection criteria for wind farms. Full article

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO₂ concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications. Full article

In this study, an assessment of the FORTH radiative transfer model (RTM) surface solar radiation (SSR) as well as its interdecadal changes (Δ(SSR)), namely global dimming and brightening (GDB), is performed during the 35-year period of 1984–2018. Furthermore, a thorough evaluation of SSR and (Δ(SSR)) is conducted against high-quality reference surface measurements from 1193 Global Energy Balance Archive (GEBA) and 66 Baseline Surface Radiation Network (BSRN) stations. For the first time, the FORTH-RTM Δ(SSR) was evaluated over an extended period of 35 years and with a spatial resolution of 0.5° × 0.625°. The RTM uses state-of-the-art input products such as MERRA-2 and ISCCP-H and computes 35-year-long monthly SSR and GDB, which are compared to a comprehensive dataset of reference measurements from GEBA and BSRN. Overall, the FORTH-RTM deseasonalized SSR anomalies correlate satisfactorily with either GEBA (R equal to 0.72) or BSRN (R equal to 0.80). The percentage of agreement between the sign of computed GEBA and FORTH-RTM Δ(SSR) is equal to 63.5% and the corresponding percentage for FORTH-RTM and BSRN is 54.5%. The obtained results indicate that a considerable and statistically significant increase in SSR (Brightening) took place over Europe, Mexico, Brazil, Argentina, Central and NW African areas, and some parts of the tropical oceans from the early 1980s to the late 2010s. On the other hand, during the same 35-year period, a strong and statistically significant decrease in SSR (Dimming) occurred over the western Tropical Pacific, India, Australia, Southern East China, Northern South America, and some parts of oceans. A statistically significant dimming at the 95% confidence level, equal to −0.063 Wm⁻² year⁻¹ (or −2.22 Wm⁻²) from 1984 to 2018 is found over the entire globe, which was more prevalent over oceanic than over continental regions (−0.07 Wm⁻² year⁻¹ and −0.03 Wm⁻² year⁻¹, statistically significant dimming at the 95% confidence level, respectively) in both hemispheres. Yet, this overall 35-year dimming arose from alternating decadal-scale changes, consisting of dimming during 1984–1989, brightening in the 1990s, turning into dimming over 2000–2009, and brightening during 2010–2018. Full article

Industrialization, explosive population growth, anthropogenic activities, and vehicular exhaust deteriorate ambient air quality across the world. The current study aims at assessing the impacts on ambient air quality patterns and their co-relations in one of the world’s most polluted cities, i.e., Lahore, Pakistan, during a strict, moderate, and post-COVID-19 period of 28 months (March 2020–June 2022). The purpose of this study is to monitor and analyze the relationship between criteria air pollutants (SO₂, particulate matter (PM 10 and 2.5), CO, O₃, and NO₂) through a Haz-Scanner 6000 and mobile van (ambient air quality monitoring station) over nine towns in Lahore. The results showed significantly lower concentrations of pollutants during strict lockdown which increased during the moderate and post-COVID-19 lockdown periods. The post-COVID-19 period illustrates a significant increase in the concentrations of SO₂, PM₁₀, PM_2.5, CO, O₃, and NO₂, in a range of 100%, 270%, 500%, 300%, 70%, and 115%, respectively. Major peaks (pollution concentration) for PM₁₀, PM_2.5, NO₂, and SO₂ were found during the winter season. Multi-linear regression models show a significant correlation between PM with NO₂ and SO₂. The ratio of increase in the PM concentration with the increasing NO₂ concentration is nearly 2.5 times higher than SO₂. A significant positive correlation between a mobile van and Haz-Scanner was observed for CO and NO₂ data as well as ground-based observation and satellite data of SO₂, NO₂, and CO. During the strict COVID-19 lockdowns, the reduction in the vehicular and industrial exhaust significantly improved the air quality of nine towns in Lahore. This research sets the ground for further research on the quantification of total emissions and the impacts of vehicular/industrial emissions on human health. Full article

By analyzing the mass concentrations and compositions of atmospheric PM_2.5 in Shaoxing from December 2019 to February 2020, the characteristics of carbon-containing components, water-soluble ions and metal elements were obtained. NO₃⁻, OC, SO₄²⁻ and NH₄⁺ were the main components of PM_2.5 in winter. The OC/EC ratio was 3.27, which proved the existence of SOC. The proportion of SOC in OC was 47.3%, which showed that secondary sources made a significant contribution. The values of OC/EC and NO₃⁻/SO₄²⁻ indicated that vehicle exhaust emissions also made a significant contribution to PM_2.5. Trace elements of Na, Ca, K and Cd had higher enrichment factor values and were enriched due to human activities. Finally, PM_2.5 sources analysis was performed by the positive matrix factorization model. The results showed that secondary inorganic salts (49.3%), motor vehicles and industrial sources (21.3%) and dust sources (17.0%) were the important sources of PM_2.5 pollution. Full article

The study presents seismogenic ULF (ultra-low-frequency) wave effects, as observed at our own new magnetic observatory at Asahi (geographic coordinates: 35.770° N, 140.695° E) in Chiba Prefecture. Our target earthquake (EQ) is a huge one offshore of Fukushima prefecture (37.353° N, 141.603° E) with a magnitude (M) of 7.4, which occurred at 20.59 h on November 21 UT, 2016. As a sampling frequency of 1 Hz was chosen for our induction magnetometer, we could detect both ULF wave effects: ULF radiation from the lithosphere, and the ULF depression effect, indicative of lower ionospheric perturbations. Observing the results of polarization analyses, we detected clear enhancements in ULF (frequency = 0.01–0.03 Hz) lithospheric radiation 14 days, 5 days, and 1 day before the EQ, and also observed a very obvious phenomenon of ULF (0.01–0.03 Hz) depression just 1 day prior to the EQ, which is regarded as the signature of lower ionospheric perturbations. These findings suggest that pre-EQ seismic activity must be present in the lithosphere, and also that the lower ionosphere was very much perturbed by the precursory effects of the Fukushima EQ. These new observational effects from our station have been compared with our previous investigations on different seismogenic topics for the same EQ, including the ULF observations at another magnetic observatory at Kakioka, belonging to the Japan Meteorological Agency (JMA), about 50 km north of our Asahi station, subionospheric VLF/LF propagation data (Japanese and Russian data), AGW (Atmospheric gravity wave) activity in the stratosphere, and satellite observation of particle precipitations. We have found that seismogenic anomalies of different parameters tend to happen just around the EQ day, but mainly before the EQ, and have found the chain-like tendency of the effects of the lithosphere, which seem to propagate upwards the lower ionosphere. Finally, we will try to gain a better understanding of the physical phenomena or mechanisms of the lithosphere–atmosphere–ionosphere coupling (LAIC) process during the EQ preparation phase. Full article

Tropical cyclones have long been known to be powered by turbulent enthalpy fluxes from the ocean’s surface and slowed by turbulent momentum fluxes into the surface. Here, we review evidence that the development and structure of these storms are also partially controlled by turbulence in the outflow near the storm’s top. Finally, we present new research that shows that tropical cyclone-like, low-aspect-ratio vortices are most likely in systems in which the bottom heat flux is controlled by mechanical turbulence, and the top boundary is insulating. Full article

High-resolution reanalysis data are an effective way to evaluate cloud water resources (CWRs). Based on ERA5 reanalysis data and gridded observed precipitation data, combined with the diagnostic quantification method of cloud water resource (CWR-DQ), we analyze and evaluate the CWRs and their distribution characteristics in the Huaihe River Basin from 2011 to 2021. Moreover, we compare and evaluate the CWRs of two typical precipitation processes in summer and winter. The results show that the annual total amount of atmospheric hydrometeor (GM_h) in the Huaihe River Basin is approximately 1537.3 mm. The precipitation (P_s) is 963.5 mm, the cloud water resource (CWR) is 573.8 mm, and the precipitation efficiency of hydrometeor (PE_h) is 62.4%. The CWR in the Huaihe River Basin shows a slow increasing trend from 2011 to 2021.The monthly variations in P_s, CWR, and PE_h show a single peak distribution. The spatial horizontal distributions of the gross mass of water vapor (GM_v), GM_h, and P_s in the Huaihe River Basin are zonal, and the values decrease with increasing latitude. In summer, the hydrometeors are mainly distributed in the middle layer (between 600 and 350 hPa). The hydrometeors in spring, autumn, and winter are mainly below 500 hPa. Two cases reveal that GM_v, the condensation from water vapor to hydrometeors (C_vh), GM_h, P_s, and PE_h in the summer case are significantly higher compared to those in the winter case, while the CWRs are similar. The results are helpful for proposing rational suggestions for the Huaihe River Basin and to provide some beneficial reference for the development of CWRs. Full article

In recent years, the concentrations of PM_2.5 in urban ambient air in China have been declining; however, the strong atmospheric oxidation capacity (AOC) represents challenges to the further reduction of PM_2.5 concentration and the continuous improvement of ambient air quality in China in the future, since the overall AOC is still at a high level. For this paper, based on ground observation data recorded in Beijing from 2016 to 2019, the variation in AOC was characterized according to the concentration of odd oxygen (O_X = O₃ + NO₂). The concentrations of the primary and secondary components of PM_2.5 were analyzed using empirical formulas, the correlation between AOC and the concentrations of secondary PM_2.5 and the secondary inorganic components (SO₄²⁻, NO₃⁻, NH₄⁺, and SNA) in Beijing were explored, the impact of atmospheric photochemical reaction activity on the generation of atmospheric secondary particles was evaluated, and the impact of atmospheric oxidation variations on PM_2.5 concentrations and SNA in Beijing was investigated. The results revealed that O_X concentrations reached their peak in 2016 and reached their lowest point in 2019. The O_X concentrations followed a descending seasonal trend of summer, spring, autumn, and winter, along with a spatial descending trend from urban observation stations to suburban stations and background stations. The degree of photochemical activity and the magnitude of the AOC have a large influence on the production of atmospheric secondary particles. When the photochemical reaction was more active and the AOC was stronger, the mass concentrations of the secondary generated PM_2.5 fraction were higher and accounted for a higher proportion of the total PM_2.5 mass concentrations. In the PM_2.5 fraction, SNA accounted for 50.7% to 94.4% of the total mass concentrations of water-soluble inorganic ions in the field observations. Higher concentrations of the atmospheric oxidant O_X in ambient air corresponded to a higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), suggesting that the increase in AOC could promote the increase of PM_2.5 concentration. Based on a relationship analysis of SOR, NOR, and O_X, it was inferred that the relationship between O_X and SOR and the relationship between O_X and NOR were both nonlinear. Therefore, when establishing PM_2.5 control strategies in Beijing in the future, the impact of the AOC on PM_2.5 generation should be fully considered, and favorable measures should be taken to properly regulate the AOC, which would be more effective when carrying out further control measures regarding PM_2.5 pollution. Full article

In response to the nonlinear fitting difficulty of the traditional weighted average temperature (T_m) modeling, this paper proposed four machine learning (ML)-based T_m models. Based on the seven radiosondes in the Yangtze River Delta region from 2014 to 2019, four forecasting ML-based T_m models were constructed using Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Tree (CART) algorithms. The surface temperature (T_s), water vapor pressure (E_s), and atmospheric pressure (P_s) were identified as crucial influencing factors after analyzing their correlations to the T_m. The ML-based T_m models were trained using seven radiosondes from 2014 to 2018. Then, the mean bias and root mean square error (RMSE) of the 2019 dataset were used to evaluate the accuracy of the ML-based T_m models. Experimental results show that the overall accuracy of the LightGBM-based T_m model is superior to the SVM, CART, and RF-based T_m models under different temporal variations. The mean RMSE of the daily LightGBM-based T_m model is reduced by 0.07 K, 0.04 K, and 0.13 K compared to the other three ML-based models, respectively. The mean RMSE of the monthly LightGBM-based T_m model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean RMSE of the quarterly LightGBM-based T_m model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean bias of the LightGBM-based T_m model is also smaller than that of the other ML-based T_m models. Therefore, the LightGBM-based T_m model can provide more accurate T_m and is more suitable for obtaining GNSS precipitable water vapor in the Yangtze River Delta region. Full article

With increasing computational power, the regional climate modeling community is moving to higher resolutions of a few kilometers, named convection-permitting (CP) simulations. This study aims to present an assessment of precipitation metrics simulated with the non-hydrostatic regional climate model RegCM-4.7.1 at CP scale for a decade-long period (2001–2010) for Bulgaria. The regional climate simulation at 15 km grid spacing uses ERA-Interim (0.75° × 0.75°) re-analysis as the driving data and parametrized deep convection. The kilometer-scale simulation at 3 km horizontal grid spacing is nested into regional climate simulation using parametrized shallow convection only. The CP simulation is evaluated against daily and hourly datasets available for the selected period and is compared with the coarser resolution driving simulation. The results show that the model represents well the spatial distribution of mean precipitation at the regional and kilometer scale for the territory of Bulgaria. However, the CP_RegCM_3km model produces too much precipitation over the mountains and shows the largest biases in the summer season (above 100%). At the daily scale, improvements are found in CP simulation for precipitation wet-day intensity and extreme precipitation in the autumn and for wet-day frequency in the summer. At the hourly scale, the kilometer-scale simulation improved the performance of wet-hour precipitation intensity in all seasons compared with coarse-resolution simulation (−23% vs. −46% in MAM; −10% vs. −37% in JJA; −47% vs. −53% in SON; −54% vs. −62% in DJF) and extreme precipitation in the autumn (−7% vs. −51%) and winter (−34% vs. −58%). The representation of wet-hour frequency was improved by CP_RegCM_3km in all seasons, except summer (−3.1% vs. −6.7% in spring; 0.5% vs. −3.8% in autumn and −7.7% vs. −11.5% in winter). Full article

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. Full article

Air pollution has emerged as a pressing global issue in recent decades. While criteria pollutants and greenhouse gases contribute to the problem, this article explicitly addresses hazardous air pollutants (HAPs). This work estimates the country-wide cumulative human health impacts from exposure to HAPs. Kuwait is used as the case study due to data availability and non-fragmentation of data. At present, the evaluation of multi-pathway human health risks arising from exposure to HAPs is incomplete, as indirect pathways have not been considered. Furthermore, only a few HAPs, such as benzene, have established ambient air quality standards specifically intended to safeguard human health, leaving many HAPs unregulated. This study considers several pathways (both direct and indirect) and various environmental media (air, water, plants, soil, and animal tissue). The findings indicate that cumulative health risks in the coastal air quality zone are within acceptable limits but are notably higher when compared to the other air quality zones. For cancer risks, only the Ahmadi Hospital, with a cancer risk of 1.09 × 10⁻⁵ for the resident adult exposure scenario, slightly exceeds the acceptable risk level of 1 × 10⁻⁵. The proposed methodology integrates the results from a country-wide emissions inventory composed of different air quality zones, air dispersion and deposition modeling, multi-pathway transport-and-fate analysis, exposure quantification, and health risk and hazard characterization. It also extends and adapts EPA methodologies initially designed for hazardous waste combustion facilities to additional emission sources and provides a case study for a region seldom subjected to such human health risk assessments. Full article