Search Results (184)

Surface ozone (O₃) is a multifaceted threat that not only deteriorates the environment but also poses risks to human health. Here, we estimated the seamless hourly surface O₃ in China using Extreme Gradient Boosting (XGBoost) with multisource data fusion to investigate spatiotemporal differences in O₃ during multistage COVID-19, and the response of O₃ variation to meteorology and emissions were explored using Shapley Additive Explanations (SHAP) and WRF-Chem. The results indicate that the optimized model demonstrated higher accuracy, with CV-R² of 0.96–0.97 and RMSE of 4.58–5.00 μg/m³. Benefitting from the full coverage of the dataset, the underestimated O₃ was corrected and hotspots of short-term O₃ pollution events were successfully captured. O₃ increased by 16.8% during the lockdown, with high values clustered in the north and west, attributed to the weakened urban NOx titration resulting from reduced emissions. During the control and regulation period, O₃ levels declined year by year. O₃ exhibited significant fluctuations in the Pearl River Delta but remained stable in western China, with both regions demonstrating high sensitivity to meteorological variability. Among these, solar radiation and temperature were the key meteorological factors. The seamless high-resolution O₃ datasets will enable more insightful analyses regarding the spatiotemporal characterization and cause analysis. Full article

The current climate and energy crises demand innovative approaches to operating buildings more sustainably. HVAC systems, which significantly contribute to a building’s energy consumption, have been a major focus of research aimed at improving operational efficiency. However, a critical factor often overlooked is the seasonal and hourly variation in solar radiation and the resulting solar heat gain, which heats specific rooms differently depending on their orientation, type, and location within the building. This study proposes a simulation-based strategy to reduce HVAC energy use in hotels by allocating guests to rooms with more favorable thermal characteristics depending on the season. A high-resolution building energy model (BEM) was developed to represent a real 17-floor hotel tower in Madrid, incorporating detailed geometry and surrounding shading context. The model includes 439 internal thermal zones and simulates solar radiation using EnergyPlus’ Radiance module. The simulation results revealed large room-by-room differences in thermal energy demand. When applying an energetically optimized guest allocation strategy based on these simulations and using real occupancy data, potential reductions in HVAC energy demand were estimated to reach around 6% during summer and up to 20% in winter. These findings demonstrate that data-driven guest allocation, informed by physics-based building simulations, can provide substantial energy savings without requiring physical renovations or equipment upgrades, offering a promising approach for more sustainable hotel operation. Full article

Power system imbalances pose significant challenges to maintaining grid stability and ensuring efficient market performance, particularly in the context of the Japanese electricity market. The primary drivers of these imbalances are identified as the nonlinear responses of power generation and consumer electricity demand to uncertain variables such as temperature and solar radiation, in addition to complex factors such as planned generator outages and operational constraints. Consequently, the prediction of imbalance signals using linear models is inherently challenging and requires the adaptation of more advanced methods in practice. This study comprehensively analyzes imbalance signal dynamics and develops practical forecasting tools using Machine Learning (ML) techniques. By incorporating a diverse range of features—including lagged imbalance data, weather forecast errors specific to Japan, and temporal patterns—we demonstrate that the prediction accuracy of imbalance signals is significantly improved compared to a baseline reflecting random forecasts based on class distribution observed during the initial training period. Furthermore, the proposed approach identifies the key drivers of hourly imbalance signals, while leveraging out-of-sample forecasting models. Based on these findings, we conclude that the use of multiple predictive models enhances the robustness and reliability of our forecasts, offering actionable tools for improving forecasting accuracy in real-world operations and contributing to a more stable and efficient electricity market. Full article

The building energy performance gap (BEPG) remains a significant challenge, undermining the accuracy of energy simulations and complicating efforts to design energy-efficient buildings. This study addresses this issue by developing an adaptive occupant behaviour framework for office buildings, integrating agent-based modelling (ABM) with a building performance simulation (BPS) platform. Conventional BPS models often rely on deterministic assumptions and overlook the dynamic, stochastic nature of occupant interactions, such as window and blind operations. By incorporating occupant-driven behaviours, this research enhances the realism of energy predictions and provides insights into reducing the BEPG. Focusing on a multi-functional office building at the University of Nottingham, the study used empirical data to validate the model. The ABM framework simulated occupant behaviours influenced by factors like indoor and outdoor temperatures, solar radiation, clothing levels, and metabolic rates. Profiles generated by the ABM were integrated into the energy model, creating an Adjust model compared against a Base model with deterministic settings. Validation against measured boiler energy use showed that the Baseline model over-predicted consumption by roughly 45 %, whereas the behaviour-informed Adjust model cut the deviation to about 26 %, albeit under-predicting the total load. Statistical analyses revealed improvements in mean squared error (MSE) and root mean squared error (RMSE), although hourly energy predictions remained a challenge. Additionally, the Adjust model provided a more realistic representation of thermal comfort, reducing variability in the predicted mean vote (PMV) index from extreme values in the Base model to a more stable range in the Adjust model. However, the Adjust model also predicted higher indoor CO₂ concentrations, particularly in individual offices, due to reduced ventilation associated with occupant actions. This study demonstrates the potential of integrating ABM with BPS models to address modelling discrepancies by capturing detailed and dynamic occupant interactions, emphasising the importance of adaptive behaviours in improving prediction accuracy and occupant well-being. Full article

Downward shortwave radiation (DSR) to the Earth’s surface is an essential renewable energy component. Accurate knowledge of solar radiation, i.e., solar energy resource assessment, is a prior requirement for the development of the solar energy industry. In the framework of solar resource assessment, site adaptation refers to leveraging short-term, high-quality ground-based observations as unbiased references to correct long-term, site-specific gridded model datasets, which has been playing an important role in this research area. This study evaluates 12 probabilistic site adaptation (PSA) methods for the correction of the hourly DSR data from multiple gridded DSR products in the Western Sichuan Plateau (WSP). Surface pyranometer observations are used as the reference to adapt predictions from two satellite products and two reanalysis products, collectively. Systematic quantification reveals inherent errors with root mean square errors (RMSEs) > 200 W/m² across all datasets. Through a comparative evaluation of three methodological categories (benchmarking, parametric/non-parametric, and quantile combination approaches), it is demonstrated that quantile-based ensemble methods achieve superior performance. The median ensemble (MED) method delivers optimal error reduction (RMSE: 163.97 W/m², nRMSE: 34.43%). The resulting optimal dataset, with a temporal resolution of 1 h and a spatial resolution of 0.05° × 0.05°, identifies the WSP as a region of exceptional energy potential, characterized by substantial annual total solar radiation (1593.10 kWh/m²/yr) and a stable temporal distribution (negative correlation between the total solar radiation and the coefficient of variation). This methodological framework provides actionable insights for solar resource optimization in complex terrains. Full article

Water systems with pipes embedded in the horizontal concrete core slabs can be used for efficient space heating and cooling of passive and low-energy buildings. ISO 11855-4 describes the hourly simulation method of such systems while recommending to use other simulation tools to assess heat flow by transmission to the ambient environment. As it plays an important role in the thermal balance of a conditioned zone, this paper presents two calculation methods to obtain heat flow through the envelope. They were integrated with a general algorithm given in ISO 11855-4 and the simulation tool was developed. To validate the presented solution measurements were performed in a passive office building during the heating (November) and cooling (July) periods. The total heat transfer coefficient by transmission was measured and compared with the theoretical design value. Both proposed simulation algorithms provided results with very good accuracy. In the first period, the mean absolute of percentage error (MAPE) of the indoor air and floor temperatures amounted to 0.65% and 0.75%, respectively. Simulations showed that heating demand was covered mainly by the floor (28.7%), internal gains (21.7%), and ceiling (18.7%), while heat loss to the environment was mainly due to external partitions (94.0%). In the second period MAE and MAPE did not exceed 0.19 °C and 0.90%, respectively. Floor and ceiling were mainly responsible for heat gains removal (61%). Solar radiation was the main source (91%) of internal gains. The results obtained confirmed the assumptions taken. The simulation programme developed does not require the use of additional tools. Full article

Efficient solar energy capture is crucial for renewable energy development, particularly in equatorial regions with consistent solar radiation. This study evaluated the impact of the azimuth angle of the solar panels on photovoltaic energy production in Loja, Ecuador. Three photovoltaic systems with east and west orientations were installed, and data were continuously collected from June 2021 to May 2022. Descriptive and comparative statistical analyses, including one-way ANOVA and Kruskal–Wallis tests, were employed to assess the differences in energy production between the systems. Additionally, an analysis of average hourly energy production was conducted to better understand diurnal variations and their relationship with energy demand. Results showed no significant differences in energy production between east- and west-oriented systems, although east-facing panels showed a slight advantage in certain months, between October and December. Seasonal variations were found to have a greater influence on energy production than orientation, suggesting that climatic factors should be prioritized when designing solar installations in equatorial areas. The findings indicate that azimuth angle is not a decisive factor for optimizing energy efficiency in Loja, Ecuador. Moreover, the diurnal analysis demonstrated a typical daily curve with midday peaks, misaligned with morning and evening demand, which could affect future design strategies. Full article

The global ambition to achieve carbon neutrality by the mid-21st century is driving a transition towards clean energy. Accurately assessing solar energy potential necessitates high-quality observations of hourly surface solar radiation (SSR). The performance of hourly SSR data from two reanalysis products (ERA5 and MERRA-2) and three satellite-derived products (CERES, SARAH-E, and Solcast) is validated against 22 years of continuous surface observations over 96 stations across China. The accuracy (in %) and trend consistency (in % decade⁻¹) of estimates from gridded products in reproducing the diurnal cycle and trend of SSR are generally lower at sunrise and sunset than at noon, and they are also reduced in the cold season (October to next March) compared with the warm season (April to September). Regionally, accuracy is generally lower in the southwestern plateau region, and the trend consistency of most products is lowest in the rugged and cloudy southern part of China. Among the evaluated datasets, Solcast and MERRA-2 exhibit the highest accuracy and trend consistency in capturing the diurnal pattern of SSR, respectively, while CERES demonstrates the best overall performance. Full article

Accurate solar radiation data are crucial for solar energy applications, yet ground-based measurements are limited in many regions. Satellite-derived and reanalysis products offer an alternative, but their accuracy varies across spatial and temporal scales. This study evaluated the performance of four widely used GHI products—CAMS, SARAH-3, ERA5 and MERRA-2—against ground measurements at hourly, daily (summed from hourly) and monthly (averaged from daily) timescales. The analysis also examined how temporal aggregation influenced error characteristics using correlation coefficients, the rMBD, the rRMSD and the combined performance index (CPI). At an hourly scale under clear-sky conditions, satellite products outperformed reanalysis products, with $\mathrm{r}\approx 1$ and ${\mathrm{R}}^2\approx 0.9$ and the rMBD, rRMSD and CPI ranging from 0.1%, 11.4% and 11.8% to −14.7%, 33.3% and 75.1% for CAMS; 0.2%, 11.4% and 10.9% to 13.5%, 22.4% and 120.7% for SARAH-3; −0.2%, 21.6% and 23.8% to 21.5%, 40.9% and 128.8% for MERRA-2; and 0.8%, 14.6% and 16.3% to 22%, 48.2% and 88.3% for ERA5. Under cloudy conditions, all products overestimated GHI, with the rMBD reaching up to 39.7% (SARAH-3), 35.9% (CAMS), 22.9% (MERRA-2) and 28% (ERA5), while the rRMSD exceeded 40% for all. Overcast conditions yielded the poorest performance, with the rMBD ranging from 45.8% to 124.6% and the CPI exceeding 800% in some cases. From the hourly to daily and monthly datasets, aggregation reduced errors for reanalysis products by 5.5% and up to 12.4%, respectively, in clear-sky conditions, but for satellite-based products, deviations slightly increased up to 3.1% for the monthly dataset. Under all-sky conditions, all products showed reductions up to 23%. These results highlight the significant challenges in estimating GHI due to limited knowledge of aerosol and cloud dynamics in the region. They emphasize the need for improved parameterization in models and dedicated measurement campaigns to enhance satellite and reanalysis product accuracy in West Africa. Full article

A method based on Long Short-Term Memory (LSTM) networks is proposed to forecast hourly energy consumption. Using an office building in Shanghai as a case study, hourly data on occupancy, weather, and energy consumption were collected. Daily energy consumption was analyzed using single-link clustering, and days were classified into three types. The key input variables significantly influencing energy consumption, solar radiation, occupancy, and outdoor dry bulb temperature are identified by the Pearson correlation coefficient. By comparing five algorithms, it was found that the LSTM model performed the best. After considering the occupancy, the hourly MAPE was reduced from 11% to 9%. Accuracy improvements for each day type were noted as 1% for weekdays, 4% for Saturday, and 7% for Sunday. Further analysis indicated that the model started to predict the time (1:00) and commute time (7:00 and 17:00) with large errors. The model was optimized by varying the time step. For the times 1:00, 7:00, and 17:00, the best optimization of the model was achieved when the time step values were set to 6 h, 24 h, and 18 h with an MAPE of 3%, 6%, and 5%, respectively. As the model time step increased (≤2 weeks), the accuracy of the model decreased to 6%. Full article

In recent years, several studies have evaluated the potential of renewable energy sources in response to climate change and high energy demand. Due to its equatorial location and significant solar and wind potential, Brazil has incorporated alternative sources into its energy matrix, driven by more efficient and economical technologies for solar energy. However, the availability of observed data is still limited, and many studies rely on satellite estimates or extrapolations of in situ observations from other regions, compromising the efficiency of new technologies. This study uses NASA MERRA-2 reanalysis data to evaluate the influence of aerosols and cloudiness on the estimate of solar irradiance in Brazil. INMET stations were chosen in regions representative of the Brazilian climate and geography, with more than 12 years of observational data. MERRA-2 includes aerosol fields that interact with the model’s radiation fields, with a spatial resolution of 0.5° and hourly temporal resolution. Variables used include shortwave radiation fluxes and aerosol optical depth. Statistical indices used in performance analysis include mean bias, mean squared error, and Pearson correlation coefficient. The stations’ diurnal solar irradiance cycles were compared with MERRA-2 reanalysis data, considering different scenarios of aerosol and cloudiness effects. The reanalysis data represented the Bauru and Santa Maria stations well, while others, such as Barreiras and Goiânia, showed underestimation. Monthly cycling was also analyzed, highlighting seasonality, with greater amplitude in Santa Maria and lower in Caicó. In some locations, such as Campo Grande, the influence of aerosols is more significant, especially during the dry months, when forest fires, mainly in the Amazon region, increase the aerosol optical depth. The results show that reanalysis estimates can be used to evaluate the temporal variability of solar irradiation in regions without observational data. In conclusion, the study was able to evaluate the temporal variability of solar irradiation in Brazil using MERRA-2 atmospheric reanalysis data, demonstrating that, although there are differences with observational data, reanalysis estimates are useful in areas without observed data, with values correlation values above 0.8 and reaching values close to 0.95. However, although small, the differences observed between measured and estimated solar irradiation are generally caused by the inability of models to adequately represent the fraction of clouds and aerosols in the atmosphere. Full article

The electrical resistance (ER) method is widely used for atmospheric corrosion measurements and can be used to measure the corrosion rate accurately. However, severe errors occur in environments with temperature fluctuations, such as areas exposed to solar radiation, preventing accurate temporal corrosion rate measurement. To decrease the error, we developed an improved sensor composed of a reference metal film and an overlaid sensor metal film to cancel temperature differences between them. The improved sensor was compared with an existing sensor product in outdoor monitoring experiments. The spike-like error during the daytime was successfully reduced. Furthermore, by utilizing a data-filtering process, we measured the corrosion rate every hour. Hourly corrosion rate measurements were difficult when the average daily corrosion rate was less than 50 µm/year under conditions of 0.05 g/m² salt. Observations showed a strong correlation between corrosion rate and sensor surface humidity. In the future, this method will make it possible to study the relationship between the atmospheric corrosion rate and environmental changes over time. Full article

Trees positively improve the annual thermal comfort of the built environment in tropical areas, where climate change is slight throughout the year. However, for areas with high changes in climate all year, the current studies have only explored the summer cooling performance of trees without the impact of different types of trees on annual thermal comfort, especially in cold seasons. Therefore, to quantify the impacts and scientifically guide the optimization of green space layout in hot summer and cold winter areas, this study selected Changsha City as the study area and analyzed how the annual thermal comfort is affected by evergreen trees and deciduous trees, which are two common types of trees in hot summer and cold winter areas. The analytical results indicated that the difference in the effect of deciduous and evergreen trees on outdoor thermal comfort was insignificant in summer, where the difference in the monthly mean PET for the three summer months was slight, being 0.28 °C, 0.14 °C, and 0.29 °C, respectively. However, evergreen trees greatly exacerbated winter cold compared to deciduous trees, with a monthly mean PET decrease by nearly 1.0 °C and an hourly PET reduced by up to 3.57 °C. The difference is mainly attributed to the absorption and reflection of solar radiation by the tree canopy, as well as the cooling and humidifying effect of the tree leaf. In hot summer and cold winter areas, outdoor thermal comfort is still in the “comfortable” and “slightly warm” acceptable stage despite the warming effect of deciduous trees in the spring and autumn seasons. Planting evergreen trees is an inevitable thermal mitigation choice for tropical areas. However, for the areas with high annual climate change, such as hot summer and cold winter areas in China, a change in empirical tree planting patterns and selecting deciduous trees where appropriate will improve year-round outdoor thermal comfort. Full article

To improve our understanding of the carbon balance, it is significant to study long-term variations of all components of carbon exchange and their driving factors. Gross primary production (GPP), respiration (Re), and net ecosystem productivity (NEP) from the hourly to the annual sums in a subtropical coniferous forest in China during 2003–2017 were calculated using empirical models developed previously in terms of PAR (photosynthetically active radiation), and meteorological parameters, GPP, Re, and NEP were calculated. The calculated GPP, Re, and NEP were in reasonable agreement with the observations, and their seasonal and interannual variations were well reproduced. The model-estimated annual sums of GPP and Re over 2003–2017 were larger than the observations of 11.38% and 5.52%, respectively, and the model-simulated NEP was lower by 34.99%. The GPP, Re, and NEP showed clear interannual variations, and both the calculated and the observed annual sums of GPPs increased on average by 1.04% and 0.93%, respectively, while the Re values increased by 4.57% and 1.06% between 2003 and 2017. The calculated and the observed annual sums of NEPs/NEEs (net ecosystem exchange) decreased/increased by 1.04%/0.93%, respectively, which exhibited an increase of the carbon sink at the experimental site. During the period 2003–2017, the annual averages of PAR and the air temperature decreased by 0.28% and 0.02%, respectively, while the annual average water vapor pressure increased by 0.87%. The increase in water vapor contributed to the increases of GPP, Re, and NEE in 2003–2017. Good linear and non-linear relationships were found between the monthly calculated GPP and the satellite solar-induced fluorescence (SIF) and then applied to compute GPP with relative biases of annual sums of GPP of 5.20% and 4.88%, respectively. Large amounts of CO₂ were produced in a clean atmosphere, indicating a clean atmospheric environment will enhance CO₂ storage in plants, i.e., clean atmosphere is beneficial to human health and carbon sink, as well as slowing down climate warming. Full article

By utilizing top-of-atmosphere radiation (TOAR) data from China’s new generation of geostationary satellites (FY-4A and FY-4B) along with interpretable machine learning models, near-surface particulate matter concentrations in China were estimated, achieving hourly temporal resolution, 4 km spatial resolution, and 100% spatial coverage. First, the cloudless TOAR data were matched and modeled with the solar radiation products from the ERA5 dataset to construct and estimate a fully covered TOAR dataset under assumed clear-sky conditions, which increased coverage from 20–30% to 100%. Subsequently, this dataset was applied to estimate particulate matter. The analysis demonstrated that the fully covered TOAR dataset (R² = 0.83) performed better than the original cloudless dataset (R² = 0.76). Additionally, using feature importance scores and SHAP values, the impact of meteorological factors and air mass trajectories on the increase in PM₁₀ and PM_2.5 during dust events were investigated. The analysis of haze events indicated that the main meteorological factors driving changes in particulate matter included air pressure, temperature, and boundary layer height. The particulate matter concentration products obtained using fully covered TOAR data exhibit high coverage and high spatiotemporal resolution. Combined with data-driven interpretable machine learning, they can effectively reveal the influencing factors of particulate matter in China. Full article