Prediction of daily global solar radiation in different climatic conditions using metaheuristic search algorithms: a case study from Türkiye.

Today's many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Agri) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Agri province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.

A Coordinating Small Organic Molecule with Tunable Lower Critical Solution Temperature for Efficient Management of Solar Radiation.

Structurally well-defined small molecules with lower critical solution temperature (LCST) behavior offer enormous prospects for fine-tuning their phase transition properties to be "on-demand" applied in the specific scene but are still underexplored. Herein, a novel amphiphilic small LCST molecule is rationally designed and synthesized. The molecule, namely TG, features a conjugation of multiple short ethylene glycol (EG) chains with the functional coordinating terpyridine (Tpy) moiety. The molecule TG demonstrates excellent LCST behavior down to 0.05 × 10-3 m in a water solution. And a cloud point Tcp = 30.9 °C with a very short thermal hysteresis ΔT = 0.2 °C and good reversibility can be achieved when c = 0.1 × 10-3 m. The excellent LCST properties of TG have enabled its successful performance as the smart window for solar radiation management with the ∆Tlum, ∆TIR, and ∆Tsol being 83.6%, 49.1%, and 67.2%, respectively. Moreover, the presence of Tpy moiety in TG enables its coordination with Ru3+ and the resulting complex also exhibits modulated LCST behavior with different concentration-dependent Tcp. These studies would provide novel small-molecule-based scaffolds for constructing better solar radiation management systems as well as other thermal-responsive smart materials.

Wildfire smoke impacts lake ecosystems.

Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing particles within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and under-recognized. We introduce the concept of the lake smoke-day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke-day exposure in North America from 2019 to 2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes ≥10 ha. An incredible 98.9% of lakes experienced at least 10 smoke-days a year, with 89.6% of lakes receiving over 30 lake smoke-days, and lakes in some regions experiencing up to 4 months of cumulative smoke-days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.

Analytical and experimental analysis of concrete temperature and energy considering open-air environmental variations.

Longwave radiation is an important open-air environmental factor that can significantly affect the temperature of concrete, but it has often been ignored in the temperature analysis of open-air concrete structures. In this article, an improved analytical model of concrete temperature was proposed by considering solar radiation, thermal convection, thermal conduction and especially longwave radiation. Temperature monitoring of an open-air concrete block was carried out to verify the proposed model and analyze the heat energy characteristics of open-air concrete. As demonstrated by the open-air experiment, under the influence of longwave radiation, the temperature at the top of the concrete block could decrease rapidly at night and even become lower than the minimum temperature at its bottom. Compared with the analytical model that ignores longwave radiation, the improved model that includes it better matches the measured temperature. According to the energy analysis, although solar radiation controls the transient variation in heat energy, the heat exchange caused by longwave radiation were more than that caused by convection on sunlit surfaces, which indicates the importance of considering longwave radiation.

Association between solar radiation and mood disorders among Gulf Coast residents.

BACKGROUND: Climate factors such as solar radiation could contribute to mood disorders, but evidence of associations between exposure to solar radiation and mood disorders is mixed and varies by region. OBJECTIVE: To evaluate the association of solar radiation with depression and distress among residents living in U.S. Gulf states. METHODS: We enrolled home-visit participants in the Gulf Long-Term Follow-up Study who completed validated screening questionnaires for depression (Patient Health Questionnaire-9, N = 10,217) and distress (Kessler Psychological Distress Questionnaire, N = 8,765) for the previous 2 weeks. Solar radiation estimates from the Daymet database (1-km grid) were linked to residential addresses. Average solar radiation exposures in the seven (SRAD7), 14 (SRAD14), and 30 days (SRAD30) before the home visit were calculated and categorized into quartiles (Q1-Q4). We used generalized linear mixed models to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for associations between solar radiation and depression/distress. RESULTS: Higher levels of SRAD7 were non-monotonically inversely associated with depression [PRVs.Q1 (95%CI): Q2 = 0.81 (0.68, 0.97), Q3 = 0.80 (0.65, 0.99), Q4 = 0.88 (0.69, 1.15)] and distress [PRVs.Q1 (95%CI): Q2 = 0.76 (0.58, 0.99), Q3 = 0.77 (0.57, 1.06), Q4 = 0.84 (0.58, 1.22)]. Elevated SRAD14 and SRAD30 appeared to be associated with decreasing PRs of distress. For example, for SRAD14, PRs were 0.86 (0.63-1.19), 0.80 (0.55-1.18), and 0.75 (0.48-1.17) for Q2-4 versus Q1. Associations with SRAD7 varied somewhat, though not significantly, by season with increasing PRs of distress in spring and summer and decreasing PRs of depression and distress in fall. IMPACT STATEMENT: Previous research suffered from exposure misclassification, which impacts the validity of their conclusions. By leveraging high-resolution datasets and Gulf Long-term Follow-up Cohort, our findings support an association between increased solar radiation and fewer symptoms of mood disorders.

Effects of solar radiations on stratum corneum hydration: Part II, protective action of solar filters.

The skin surface lipids (SSLs) film, composed of sebum and keratinocyte membrane lipids, is crucial to the barrier function of the stratum corneum (SC). The first part of this study investigated the impact of solar radiation on the SC based on a novel hydration and dehydration approach using Raman spectroscopy. The SSLs were found to absorb solar light, and thus participate to the protection of the skin surface. However, the protective function of the SSLs may be limited and is dependent to the heterogenous distribution of SSLs over the body surface. To ensure comprehensive protection, synergistic measures such as the application of solar filters are necessary. In this second part of the study, we have evaluated the limits of the protection capacity of SSLs and explored the protective action of a solar filters on both SSLs composition and the water hydration and dehydration kinetics in the SC.

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods.

In the framework of precision viticulture, satellite data have been demonstrated to significantly support many tasks. Specifically, they enable the rapid, large-scale estimation of some viticultural parameters like vine stem water potential (Ψstem) and intercepted solar radiation (ISR) that traditionally require time-consuming ground surveys. The practice of covering table grape vineyards with plastic films introduces an additional challenge for estimation, potentially affecting vine spectral responses and, consequently, the accuracy of estimations from satellites. This study aimed to address these challenges with a special focus on the exploitation of Sentinel-2 Level 2A and meteorological data to monitor a plastic-covered vineyard in Southern Italy. Estimates of Ψstem and ISR were obtained using different algorithms, namely, Ordinary Least Square (OLS), Multivariate Linear Regression (MLR), and machine learning (ML) techniques, which rely on Random Forest Regression, Support Vector Regression, and Partial Least Squares. The results proved that, despite the potential spectral interference from the plastic coverings, ISR and Ψstem can be locally estimated with a satisfying accuracy. In particular, (i) the OLS regression-based approach showed a good performance in providing accurate ISR estimates using the near-infrared spectral bands (RMSE < 8%), and (ii) the MLR and ML algorithms could estimate both the ISR and vine water status with a higher accuracy (RMSE < 7 for ISR and RMSE < 0.14 MPa for Ψstem). These results encourage the adoption of medium-high resolution multispectral satellite imagery for deriving satisfying estimates of key crop parameters even in anomalous situations like the ones where plastic films cover the monitored vineyard, thus marking a significant advancement in precision viticulture.

Investigating the effect of climate factors on fig production efficiency with machine learning approach.

BACKGROUND: This study employs a machine learning approach to investigate the impact of climate change on fig production in Turkey. The eXtreme Gradient Boosting (XGBoost) algorithm is used to analyze production performance and climate variable data from 1988 to 2023. Fig production is a significant component of Turkey's agricultural economy. Therefore, understanding how climate change affects fig production is essential for the development of sustainable agricultural practices. RESULTS: Despite an observed increase in fig production between 2005 and 2020, potential yield may be negatively impacted by climate variables. Identifying the specific climatic factors affecting fig production efficiency remains a challenge. In the study, two different machine learning models are created: one for fig production yield per decare and another for fig production yield per bearing fig sapling. Eight climate variables (16 variables considering day and night values) serve as independent variables in the models. The models reveal that temperature change has the highest impact, with a percentage contribution of 41.30% in the first model and 43.90% in the second model. Thermal radiation (day and night) and 2 m temperature also significantly affect individually fig production. Wind speed, precipitation and humidity contribute to a lesser extent. CONCLUSION: This study illuminates the intricate interrelationship between climate change and fig production in Turkey. The utilization of machine learning as a predictive tool for future production trends and an instrument for informing agricultural practices is a valuable contribution to the field. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Exploring the impact of solar radiation on skin microbiome to develop improved photoprotection strategies.

The skin microbiome undergoes constant exposure to solar radiation (SR), with its effects on health well-documented. However, understanding SR's influence on host-associated skin commensals remains nascent. This review surveys existing knowledge on SR's impact on the skin microbiome and proposes innovative sun protection methods that safeguard both skin integrity and microbiome balance. A team of skin photodamage specialists conducted a comprehensive review of 122 articles sourced from PubMed and Research Gateway. Key terms included skin microbiome, photoprotection, photodamage, skin cancer, ultraviolet radiation, solar radiation, skin commensals, skin protection, and pre/probiotics. Experts offered insights into novel sun protection products designed not only to shield the skin but also to mitigate SR's effects on the skin microbiome. Existing literature on SR's influence on the skin microbiome is limited. SR exposure can alter microbiome composition, potentially leading to dysbiosis, compromised skin barrier function, and immune system activation. Current sun protection methods generally overlook microbiome considerations. Tailored sun protection products that prioritize both skin and microbiome health may offer enhanced defense against SR-induced skin conditions. By safeguarding both skin and microbiota, these specialized products could mitigate dysbiosis risks associated with SR exposure, bolstering skin defense mechanisms and reducing the likelihood of SR-mediated skin issues.

Unique Acute Phytophotodermatitis Reaction and a Visual Diagnostic Guide.

Here, we describe a case of a 48-year-old caucasian female with no significant past medical history who presented to the outpatient dermatology clinic with an extremely painful and significant phytophotodermatitis (PPD) reaction to over 30% of her body surface area. The patient presented after partaking in a ritualistic ceremony where she was exposed to plant and citrus juices and subsequently sunbathed while on a tropical vacation. While not an infrequent diagnosis, this patient presented to the dermatologist in extreme pain after having no education on avoiding such triggers. This diagnosis is relatively underdiagnosed and leads to a lack of patient counseling on the hazards of UV exposure secondary to contact with certain plant and fruit juices. Lack of patient awareness leads to an increase in disease burden. Furthermore, this patient suffered a large body surface area reaction in contrast to the pathognomonic description of phytophotodermatitis secondary to the exposure to lime juice which causes relatively less total body surface area exposure.

Bloom dynamics under the effects of periodic driving forces.

Phytoplankton bloom received considerable attention for many decades. Different approaches have been used to explain the bloom phenomena. In this paper, we study a Nutrient-Phytoplankton-Zooplankton (NPZ) model consisting of a periodic driving force in the growth rate of phytoplankton due to solar radiation and analyse the dynamics of the corresponding autonomous and non-autonomous systems in different parametric regions. Then we introduce a novel aspect to extend the model by incorporating another periodic driving force into the growth term of the phytoplankton due to sea surface temperature (SST), a key point of innovation. Temperature dependency of the maximum growth rate (µmax) of the phytoplankton is modelled by the well-known Q10 formulation: [Formula: see text] , where µ0 is maximum growth at 0oC. Stability conditions for all three equilibrium points are expressed in terms of the new parameter ρ2, which appears due to the incorporation of periodic driving forces. System dynamics is explored through a detailed bifurcation analysis, both mathematically and numerically, with respect to the light and temperature dependent phytoplankton growth response. Bloom phenomenon is explained by the saddle point bloom mechanism even when the co-existing equilibrium point does not exist for some values of ρ2. Solar radiation and SST are modelled using sinusoidal functions constructed from satellite data. Our results of the proposed model describe the initiation of the phytoplankton bloom better than an existing model for the region 25-35° W, 40-45° N of the North Atlantic Ocean. An improvement of 14 days (approximately) is observed in the bloom initiation time. The rate of change method (ROC) is applied to predict the bloom initiation.

Changes in wet bulb globe temperature and risk to heat-related hazards in Bangladesh.

The rise in temperatures and changes in other meteorological variables have exposed millions of people to health risks in Bangladesh, a densely populated, hot, and humid country. To better assess the threats climate change poses to human health, the wet bulb globe temperature (WBGT) is an important indicator of human heat stress. This study utilized high-resolution reanalysis data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF ERA5) to analyze the spatiotemporal changes in outdoor WBGT across Bangladesh from 1979 to 2021, employing Liljegren's model. The study revealed an increase in the annual average WBGT by 0.08-0.5 °C per decade throughout the country, with a more pronounced rise in the southeast and northeast regions. Additionally, the number of days with WBGT levels associated with high and extreme risks of heat-related illnesses has shown an upward trend. Specifically, during the monsoon period (June to September), there has been an increase of 2-4 days per decade, and during the pre-monsoon period (March to May), an increase of 1-3 days per decade from 1979 to 2021. Furthermore, the results indicated that the escalation in WBGT has led to a five-fold increase in affected areas and a three-fold increase in days of high and extreme heat stress during the monsoon season in recent years compared to the earlier period. Trend and relative importance analyses of various meteorological variables demonstrated that air temperature is the primary driver behind Bangladesh's rising WBGT and related health risks, followed by specific humidity, wind speed, and solar radiation.

Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19?

The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus's spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity. HIGHLIGHTS: • The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area. • Weather and climate serve as environmental drivers in propagating COVID-19. • Solar radiation influences the spreading of COVID-19. • The correlation between weather and population as the factor in spreading of COVID-19.

On Nucleation Pathways and Particle Size Distribution Evolutions in Stratospheric Aircraft Exhaust Plumes with H2SO4 Enhancement.

Stratospheric aerosol injection (SAI) is proposed as a means of reducing global warming and climate change impacts. Similar to aerosol enhancements produced by volcanic eruptions, introducing particles into the stratosphere would reflect sunlight and reduce the level of warming. However, uncertainties remain about the roles of nucleation mechanisms, ionized molecules, impurities (unevaporated residuals of injected precursors), and ambient conditions in the generation of SAI particles optimally sized to reflect sunlight. Here, we use a kinetic ion-mediated and homogeneous nucleation model to study the formation of H2SO4 particles in aircraft exhaust plumes with direct injection of H2SO4 vapor. We find that under the conditions that produce particles of desired sizes (diameter ∼200-300 nm), nucleation occurs in the nascent (t < 0.01 s), hot (T = 360-445 K), and dry (RH = 0.01-0.1%) plume and is predominantly unary. Nucleation on chemiions occurs first, followed by neutral new particle formation, which converts most of the injected H2SO4 vapor to particles. Coagulation in the aging and diluting plumes governs the subsequent evolution to a narrow (σg = 1.3) particle size distribution. Scavenging by exhaust soot is negligible, but scavenging by acid impurities or incomplete H2SO4 evaporation in the hot exhaust plume and enhanced background aerosols can matter. This research highlights the need to obtain laboratory and/or real-world experiment data to verify the model prediction.

Development of natural dye photosensitizers for dye-sensitized solar cells: a review.

Dye-sensitized solar cell (DSSC) is a photovoltaic device that can be produced from natural source pigments or natural dyes. The selection of natural dyes for DSSC application is currently under research. The utilization of natural dye materials that are easy to obtain, cost-effective, and non-toxic can reduce waste during DSSC fabrication. Natural dyes can be extracted from plants through extraction and chromatography methods. The suitability and viability of utilizing natural dyes as photosensitizers in DSSCs can be predicted using appropriate software simulation by varying related parameters to produce high power conversion efficiency. In this context, the purpose of the review is to highlight the evolution of performance improvement in the development of DSSCs with consideration of natural dye extraction and software simulation. This review also focuses on the results of extracting natural dyes from herbal ingredients, which are still very limited in information, and several parts of herbal plants that can be used as natural dye sources in the future of solid-state DSSCs have been identified. Based on the results of this review, the highest efficiency was obtained for the DSSC that used chlorophyll pigments as natural dyes using Peltophorum pterocarpum leaves with 6.07%, followed by anthocyanin pigments as natural dyes using raspberries (black) fruits with 1.5%, flavonoid pigments as natural dyes using Curcuma longa herbs with 0.64%, and flavonoid pigments as natural dyes using Indigofera tinctoria flowers with 0.46%.

Actors, legitimacy, and governance challenges facing negative emissions and solar geoengineering technologies.

Institutional theory, behavioral science, sociology and even political science all emphasize the importance of actors in achieving social change. Despite this salience, the actors involved in researching, promoting, or deploying negative emissions and solar geoengineering technologies remain underexplored within the literature. In this study, based on a rigorous sample of semi-structured expert interviews (N = 125), we empirically explore the types of actors and groups associated with both negative emissions and solar geoengineering research and deployment. We investigate emergent knowledge networks and patterns of involvement across space and scale. We examine actors in terms of their support of, opposition to, or ambiguity regarding both types of climate interventions. We reveal incipient and perhaps unforeseen collections of actors; determine which sorts of actors are associated with different technology pathways to comprehend the locations of actor groups and potential patterns of elitism; and assess relative degrees of social acceptance, legitimacy, and governance.

Optimal placement and operation of soft open points, capacitors, and renewable distributed generators in distribution power networks to reduce total one-year energy loss.

The paper optimizes the placement of soft open points (SOPs) devices, shunt capacitor banks (SCBs), and distributed generators (DGs) in the IEEE 69-node distribution power grid for reducing the power loss of a single hour and total energy losses of one year. EO is proven to be more effective than previous methods and three other applied algorithms, including the Coot optimization algorithm (COOT), Modified weight inertia factor and modified acceleration coefficients-based particle swarm optimization (CFPSO), and Tunicate swarm algorithm (TSA). So, EO is applied for the last case considering one SOPs, one wind turbine (WT), two solar photovoltaic systems (PVs), and two SCBs over one year with twelve months and 24 h each month. The study reaches the smallest power loss compared to previous studies in the first case with one SOPs device. The results from the second to the fourth cases indicate that the power grid needs the placement of SCBs and DGs first and SOPs devices to reach the lowest power loss. Case 5 indicates that the hybrid system with one WT and two PVs suffers higher power losses than the base system at hours with high generation from renewable sources; however, integrating the SOPs and SCBs into the hybrid system can reach smaller losses than the base system at these hours. Thus, using SOPs and SCBs in integrated distribution power grids with renewable energies can greatly benefit energy loss reduction.

Validation of a 3D Local-Scale Adaptive Solar Radiation Model by Using Pyranometer Measurements and a High-Resolution Digital Elevation Model.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterization techniques and allows the generation of high-quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data, and its resolution is two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

The impact of meteorological factors on tuberculosis incidence in Spain: a spatiotemporal analysis.

Tuberculosis (TB) remains a global leading cause of death, necessitating an investigation into its unequal distribution. Sun exposure, linked to vitamin D (VD) synthesis, has been proposed as a protective factor. This study aimed to analyse TB rates in Spain over time and space and explore their relationship with sunlight exposure. An ecological study examined the associations between rainfall, sunshine hours, and TB incidence in Spain. Data from the National Epidemiological Surveillance Network (RENAVE in Spanish) and the Spanish Meteorological Agency (AEMET in Spanish) from 2012 to 2020 were utilized. Correlation and spatial regression analyses were conducted. Between 2012 and 2020, 43,419 non-imported TB cases were reported. A geographic pattern (north-south) and distinct seasonality (spring peaks and autumn troughs) were observed. Sunshine hours and rainfall displayed a strong negative correlation. Spatial regression and seasonal models identified a negative correlation between TB incidence and sunshine hours, with a four-month lag. A clear spatiotemporal association between TB incidence and sunshine hours emerged in Spain from 2012 to 2020. VD levels likely mediate this relationship, being influenced by sunlight exposure and TB development. Further research is warranted to elucidate the causal pathway and inform public health strategies for improved TB control.