Air pollution is a major contributor to respiratory and cardiovascular diseases, prompting recent studies to adopt AI models for forecasting pollutant levels. In this work, we introduce a hybrid framework—SSA-LSTM-XGBoost—that applies a residual-correction strategy in four stages: (i) data preprocessing, (ii) training an LSTM network optimised with the Sparrow Search Algorithm (SSA), (iii) modelling the residuals with an SSA-optimised XGBoost learner, and (iv) fusing both outputs to obtain the final prediction. The framework is assessed on test and out-of-sample datasets and benchmarked against SVR, BiGRU, random forest, BiLSTM, and GRU. On the test set, SSA-LSTM-XGBoost attains the highest accuracy, recording an R $$^{\varvec{2}}$$ of 0.9554 and the lowest errors (RMSE = 2.5194, MAE = 1.4885, MAPE = 0.0635), amounting to an average error reduction of roughly 8.2% relative to the runner-up SSA-SVR. When validated on unseen data, it remains superior (R $$^{\varvec{2}}$$ = 0.8830, RMSE = 2.4838), achieving an average error decrease of about 3.5% compared with SSA-SVR despite the harsher evaluation conditions. These findings attest to the robustness and strong generalisability of the proposed framework for reliable PM $$_{\varvec{2.5}}$$ forecasting. In practice, such forecasts enable near-real-time hotspot alerts, short-term exposure advisories for vulnerable groups, and preventive traffic or industrial controls, thereby supporting municipal air-quality management and policy decisions in Duitama.

Solar saline water splitting is a promising approach to sustainable hydrogen production, harnessing abundant solar energy and the availability of brine resources, especially in the Atacama Desert. Bischofite salt (MgCl₂·6H₂O) has garnered significant attention due to its wide range of industrial applications. Efficient hydrogen production in arid or hyper arid locations using bischofite solutions is a novel and revolutionary idea. This work studied the electrochemical performance of Mn₂O₃ electrodes using a superposition model based on mixed potential theory and evaluated the superficial performance of this electrode in contact with a 0.5 M bischofite salt solution focusing on the hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR) that occur during saline water splitting. The application of the non-linear superposition model provides valuable electrochemical kinetic parameters that complement the understanding of Mn₂O₃, this being one of the novelties of this work.

Solar saline water splitting is a promising approach for sustainable hydrogen production, harnessing abundant solar energy and the availability of brine resources, especially in the Atacama Desert. Bischofite (MgCl2·6H2O), a naturally occurring salt, has garnered significant attention due to its wide range of industrial applications and the challenges associated with efficient hydrogen production in arid or hyper arid locations. This work discloses the electrochemical performance of Mn2O3 electrodes using a superposition model based on mixed potential theory and evaluates the superficial performance of this electrode in contact with a 0.5 M nature Bischofite salt solution focusing on hydrogen evolution reaction (HER), and oxygen reduction reaction (ORR), that occurs during saline water splitting. The application of the non-linear superposition model provides valuable electrochemical kinetic parameters that complement the understanding of Mn2O3, this being one of the novelties of the manuscript.

Air pollution is a major environmental risk to public health, strongly associated with cardiovascular and respiratory diseases. According to the World Health Organization, at least 99% of the global population lives in areas that do not meet its air-quality guidelines. PM $$_{2.5}$$ 2.5 variability reflects the interaction between local emission sources and meteorological processes that regulate near-surface dispersion and accumulation, producing nonlinear temporal patterns that motivate hybrid modeling approaches. We propose an SSA-SVR-BiLSTM framework for short-term PM $$_{2.5}$$ 2.5 forecasting that combines SSA-optimized SVR and BiLSTM models within a constrained linear ensemble with weights estimated exclusively on the validation partition using Lagrange multipliers. The model is evaluated on 10 months of high-frequency (5-min) air-quality and meteorological data collected at a traffic-influenced monitoring site in Duitama, Colombia. On the held-out test set, the hybrid achieved $$R^2 = 0.9585$$ R 2 = 0.9585 and MAPE $$= 7.62\%$$ = 7.62 % , outperforming five of six SSA-optimized baselines; the difference relative to the best individual model (SSA-SVR was statistically significant but practically negligible ( $$p = 0.0105$$ p = 0.0105 , $$\Delta R^2 = 0.0002$$ Δ R 2 = 0.0002 ), and the value of the hybrid lies primarily in its improved consistency across low and medium concentration regimes rather than in a large global accuracy gain. Stratified evaluation across concentration regimes and normative thresholds (WHO, EPA) revealed systematic underestimation at extreme pollution episodes, a structural limitation shared by all evaluated models. A feature occlusion analysis identified PM $$_5$$ 5 and PM $$_{10}$$ 10 as the dominant exogenous predictors (53.6 and 21.7%), with meteorological variables contributing an additional $${\sim }20\%$$ ∼ 20 % of the total relevance.

The sun represents a potential opportunity for sustainable energy production. The study of solar energy harvesting reinforces this promise, with studies simulating different operating environments by estimating the sun’s orientation toward the south or north, depending on the planet’s location. However, studies for latitudes between 23° South and 23° North are a little-explored area. This research investigates different energy harvesting scenarios in flat-plate collectors, combining different possibilities throughout the year, analyzing the amount of energy captured using algorithms and Monte Carlo simulation. The methodology allows predicting efficiencies above 36% simply by rotating the harvesting devices from north to south or vice versa just twice a year.