Search Results (641)

This investigation focuses on rice husk biochar (RHBC) as a sustainable filler in a polylactic acid (PLA) matrix. This study employs optimization techniques, including central composite design (CCD) and analysis of variance (ANOVA), to systematically evaluate the effects of key 3D printing parameters such as filler content (0 wt.%, 10 wt.%, 20 wt.%), nozzle temperature (190 °C, 200 °C, 210 °C), orientation angle (0°, 60°, 120°), and fill pattern (hexagon, triangle, and 3D infill). Furthermore, machine learning models are used to predict the mechanical properties of PLA/RHBC composites from experimental data. The effects of these parameters on tensile strength, Young’s modulus, and hardness were analyzed. The ANOVA results showed that filler content was the most influential factor for tensile strength and Young’s modulus, contributing 36.47% and 73.25%, respectively, compared to pure PLA. For hardness, both filler content and nozzle temperature were key contributors, with a 44.08% improvement over pure PLA. Machine learning models, including multiple linear regression (MLR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gradient Boosting, were used to predict the mechanical properties. Among these, Gradient Boosting achieved the best performance, with R² values of 97.79% for tensile strength, 98.79% for Young’s modulus, and 96.8% for hardness. This study provides a robust framework that combines experimental analysis, statistical design, and machine learning to optimize RHBC as an eco-friendly filler for the development of PLA composites for adoption in the automotive, sports and aerospace industries. Full article

Background/Objectives: Perioperative chemotherapy is the standard treatment for locally advanced gastric and gastroesophageal junction adenocarcinoma; however, substantial uncertainty remains regarding the optimal management of non-responding patients and the prognostic relevance of biological and inflammatory biomarkers. This study aimed to determine, using real-world data integrated with a comprehensive literature review, whether long-term survival is driven primarily by the choice of chemotherapy regimen or by the tumor’s intrinsic biological aggressiveness and the host’s systemic inflammatory response. Methods: A retrospective analysis was performed of 43 patients with locally advanced gastric cancer who received neoadjuvant chemotherapy. Survival outcomes were stratified by regimen (FLOT versus non-FLOT) and analyzed using Kaplan–Meier methods. The prognostic value of clinicopathological features and systemic inflammatory indices was assessed using multivariate Cox regression models to identify independent predictors of mortality. Results: Although FLOT showed a trend toward improved overall survival (OS) (median not reached vs. 18.9 months), this difference did not reach statistical significance. Univariate analysis linked lymphovascular invasion (LVI) (HR = 4.17; p = 0.003), pan-cytokeratin (panCK) (HR = 2.44; p = 0.032), and monocyte-to-lymphocyte ratio (MLR) (HR = 1.73; p = 0.027) with survival. To minimize overfitting, two multivariate models were constructed. The first confirmed LVI (HR = 7.32; p < 0.001) and panCK (HR = 4.30; p = 0.006) as independent prognostic markers. The second identified MLR (HR = 1.65; p = 0.033) and panCK (HR = 2.42; p = 0.034) as independent adverse factors. Conclusions: Our findings suggest a paradigm shift in prognostic assessment for locally advanced gastric cancer: therapeutic success appears to depend more on underlying tumor biology and the immune microenvironment than on any specific neoadjuvant regimen. High MLR and LVI serve as strong surrogate markers of a biologically aggressive, chemotherapy-resistant phenotype. Consequently, future clinical strategies should move beyond a “one-size-fits-all” chemotherapy approach and prioritize these biomarkers for risk stratification and personalization of multimodal therapy. Full article

Background: Cardiogenic shock is a heterogeneous syndrome in which systemic inflammation may contribute to cardiovascular risk and adverse outcomes beyond hemodynamic compromise alone. Methods: We conducted a retrospective multicenter cohort study using the eICU Collaborative Research Database (2014–2015) to identify inflammatory phenotypes among adults admitted to intensive care units with cardiogenic shock. Inflammatory indices derived from admission hematologic parameters (including NLR, PLR, MLR, NPAR, SII, SIRI, and AISI) were analyzed using principal component analysis, followed by hierarchical and k-means clustering to identify biologically distinct inflammatory phenotypes. Clinical characteristics and short-term outcomes were compared across clusters. Results: Among 419 patients, two phenotypes were identified. Cluster 1 (n = 52) was characterized by older age, a higher prevalence of chronic kidney disease (CKD), more advanced renal and hepatic dysfunction, along with a hyperinflammatory, lymphopenic profile. Cluster 2 (n = 367) exhibited comparatively lower inflammatory indices and less biochemical derangement. There was a significant difference in the prevalence of CKD, the need for mechanical ventilation, and history of malignancy between clusters. Despite clear biological separation, short-term clinical outcomes, including rates of acute kidney injury requiring renal replacement therapy, vasopressor use, hospital length of stay, and in-hospital mortality, were similar across clusters. Conclusions: These findings suggest that cardiogenic shock encompasses distinct inflammatory phenotypes, but inflammatory clustering based on routine admission laboratory data alone may have limited utility for short-term risk stratification. Full article

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype associated with a poor prognosis and limited treatment options. Inflammatory and hematological biomarkers have emerged as potential tools for disease characterization, particularly in low-resource settings. Methods: This cross-sectional analytical study was conducted between July 2022 and February 2024 at Dalal Jamm Hospital in Dakar, Senegal, and included 120 women: 40 with TNBC, 40 with hormone-dependent breast cancer (HDBC), and 40 healthy controls. Blood samples were collected at diagnosis before any treatment to measure complete blood counts and C-reactive protein (CRP) levels. Inflammatory ratios—neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR)—were calculated. Results: TNBC patients displayed a distinct inflammatory profile characterized by elevated neutrophil counts, CRP, NLR, and MLR, as well as reduced lymphocyte and basophil percentages compared to healthy controls. NLR > 1.12 demonstrated strong discriminatory ability (AUC = 0.847; sensitivity 90%; specificity 65%). Differences between TNBC and HDBC were less pronounced, except for CRP and basophil levels. Multivariate analysis confirmed independent associations of elevated NLR, CRP, and neutrophils with TNBC. Conclusions: These findings provide new insights into the inflammatory and hematological characteristics of TNBC in this population and support further investigation of accessible biomarkers for early disease stratification in similar settings. Full article

Accurate prediction of the mechanical performance of fiber-reinforced cement mortars (FRCM) is challenging because fiber geometry and properties vary widely and interact with the cement matrix in a non-trivial way. In this study, we propose an interpretable, computationally light framework that combines principal component analysis (PCA) with multiple linear regression (MLR) to predict compressive strength (Cs) and flexural strength (Fs) from mix proportions and fiber parameters. The literature-based dataset of 52 mortar mixes reinforced with polypropylene, steel, coconut, date palm, and hemp fibers was compiled and analyzed, covering Cs = 4.4–78.6 MPa and Fs = 0.75–16.7 MPa, with fiber volume fraction V_f = 0–15% and fiber length F_l = 4.48–60 mm. PCA performed on the full dataset showed that PC1–PC2 explain 53.4% of the total variance; a targeted variable-selection strategy increased the captured variance to 73.0% for the subset used for regression model development. MLR models built using PC1 and PC2 achieved good accuracy in the low-to-mid strength range, while prediction errors increased for higher-strength mixes (approximately Cs ≳ 60 MPa and Fs ≳ 10 MPa). On an independent validation dataset (n = 10), the refined model achieved mean absolute percentage errors of 11.3% for Fs and 18.5% for Cs. The proposed PCA-MLR approach provides a transparent alternative to more complex data-driven predictors, and it can support preliminary screening and optimization of fiber-reinforced mortar designs for durable structural and repair applications. Full article

Background/Objectives: Fetal health is essential in prenatal care, influencing both maternal and fetal outcomes. Cardiotocography (CTG) monitors uterine contractions and fetal heart rate, yet manual interpretation exhibits significant inter-examiner variability. Machine learning offers automated alternatives; however, class imbalance in CTG datasets where pathological cases constitute less than 10% leads to poor detection of minority classes. This study aims to provide the first systematic benchmark comparing five resampling strategies across seven classifier families for multi-class CTG classification, evaluated using imbalance-aware metrics rather than overall accuracy alone. Methods: Seven machine learning models were employed: Naïve Bayes (NB), Random Forest (RF), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), and Multi-Layer Perceptron (MLP). To address class imbalance, we evaluated the original unbalanced dataset (base) and five resampling methods: SMOTE, BSMOTE, ADASYN, NearMiss, and SCUT. Performance was evaluated on a held-out test set using Balanced Accuracy (BACC), Macro-F1, the Macro-Matthews Correlation Coefficient (Macro-MCC), and Macro-Averaged ROC-AUC. We also report per-class ROC curves. Results: Among all models, RF proved most reliable. Training on the original distribution (base) yielded the highest BACC (0.9118), whereas RF combined with BSMOTE provided the strongest class-balanced performance (Macro-MCC = 0.8533, Macro-F1 = 0.9073) with a near-perfect ROC-AUC (approximately 0.986–0.989). Overall, resampling effects proved model dependent. While some classifiers achieved optimal performance on the natural class distribution, oversampling techniques, particularly SMOTE and BSMOTE, demonstrated significant improvements in minority class discrimination and class-balanced metrics across multiple model families. Notably, certain models benefited substantially from resampling, exhibiting enhanced Macro-F1, BACC, and minority class recall without sacrificing overall accuracy. Conclusions: These findings establish robust, model-agnostic baselines for CTG-based fetal health screening. They highlight that strategic oversampling can translate improved minority class discrimination into clinically meaningful performance gains, supporting deployment in cost-sensitive and threshold-aware clinical settings. Full article

Background: Unmeasured ions (UIs) contribute significantly to acid–base disturbances in critically ill patients, yet the optimal parameter for their estimation remains uncertain. The most widely used indicators are the albumin-corrected anion gap (AGc), the strong ion gap (SIG), and the base excess gap (BEGap). Methods: In this retrospective cohort study, a total of 2274 ICU patients (2018–2022) were included in the development cohort, and an independent external validation cohort of 1202 patients (2023–2025) was used to assess temporal generalizability. Three approaches to blood gas analysis—traditional (PaCO₂, HCO₃⁻, AGc), Stewart (PaCO₂, SIDa, ATOT, SIG), and partitioned base excess (PaCO₂, BECl, BEAlb, BELac, BEGap)—were evaluated. Multivariable linear regression (MLR) and machine learning (ML, random forest [RF], extreme gradient boosting [XGBoost], and support vector regression [SVR]) were applied to evaluate the explanatory performance of analytical approaches with respect to arterial pH. Model performance was assessed using adjusted R², RMSE, and MAE. Variable importance was quantified with tree-based methods, SHAP values, and permutation importance. All modeling and reporting steps followed the PROBAST-AI guideline. Results: In multiple linear regression (MLR), the partitioned base excess (BE) approach achieved the highest explanatory performance (adjusted R² = 0.949), followed by the traditional (0.929) and Stewart approaches (0.926). In ML analyses, model fit was high across all approaches. For the traditional approach, R² values were 0.979 with RF, 0.974 with XGBoost, and 0.934 with SVR. The Stewart’s approach showed lower overall explanatory performance, with R² values of 0.876 (RF), 0.967 (XGBoost), and 0.996 (SVR). The partitioned BE approach again demonstrated the strongest explanatory performance, achieving R² values of 0.975 with XGBoost and 0.989 with SVR. Across all analytical models, BEGap consistently emerged as a strong and independent determinant of arterial pH, outperforming SIG and AGc. SIG showed an intermediate contribution, whereas AGc provided minimal independent explanatory value. Among ML models, XGBoost showed the most stable and accurate explanatory performance across approaches. Conclusions: This study demonstrates that BEGap is a practical, physiologically informative, and bedside-applicable parameter for assessing unmeasured ions, outperforming both AGc and SIG across linear and non-linear analytical models. Full article

Background and Objectives: The relationship between psychiatric disorders and systemic inflammation remains incompletely understood. Increasing evidence suggests that inflammatory processes may play a role in the biological mechanisms underlying suicidal behavior. This study aimed to investigate the association between classical inflammatory markers and hemogram-derived inflammatory indices in patients who attempted suicide by oral drug overdose. Materials and Methods: This retrospective observational comparative study included 343 patients hospitalized following a suicide attempt by oral medication overdose and 421 age- and sex-matched healthy individuals. Serum C-reactive protein (CRP), albumin levels, complete blood count parameters, and derived inflammatory indices, including the CRP-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), systemic immune–inflammation index (SIII), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), were analyzed. Results: Patients with suicide attempts had significantly higher CRP, leukocyte, neutrophil, and monocyte levels compared to the comparison group. CAR, NLR, SIII, and MLR values were also significantly elevated, whereas PLR did not differ between groups. ROC analysis demonstrated that CAR showed the highest discriminative ability for suicide attempt, with high sensitivity and specificity. Conclusions: Hemogram-derived inflammatory indices, particularly CAR, were significantly associated with suicide attempts. These easily accessible and low-cost biomarkers may provide additional biological insight into suicide risk assessment. Further prospective studies are needed to confirm these findings. Full article

Aim: This study aimed to evaluate the diagnostic performance of the Delta Neutrophil Index (DNI) and Neutrophil-to-Lymphocyte Ratio (NLR) in distinguishing malignant from benign primary brain tumors during the preoperative period. Methods: This retrospective cohort study was conducted at a tertiary university hospital. A total of 140 participants were included 60 patients with malignant glial tumors, 50 patients with benign brain tumors, and 30 healthy controls without inflammatory, infectious, or hematologic disease. Preoperative complete blood count results obtained within seven days before surgery were analyzed. Results: Patients with malignant tumors were significantly older than those in the benign and control groups (p < 0.001). DNI, NLR, PLR, MLR, and SII values were all significantly elevated in the malignant group (p < 0.001, for all comparisons). ROC analysis revealed high diagnostic accuracy for DNI (AUC = 0.847) and NLR (AUC = 0.850), with optimal cut-off values of 3.50 and 3.95, respectively. In multivariable logistic regression adjusted for age, DNI > 3.5 (OR = 20.67; 95% CI: 3.35–127.64; p = 0.001), NLR > 3.95 (OR = 21.17; 95% CI: 3.28–136.50; p = 0.001), and CRP (OR = 1.52; 95% CI: 1.20–1.93; p = 0.001) remained independent predictors of malignancy. The combined model including DNI and NLR achieved the highest diagnostic accuracy (AUC = 0.937; age-adjusted AUC = 0.943), with a sensitivity of 88.3% and a specificity of 86.0% after age adjustment. Conclusions: Both DNI and NLR demonstrated significant value in differentiating malignant from benign primary brain tumors prior to surgery, with DNI emerging as the most powerful independent predictor. The combined use of DNI and NLR substantially improved diagnostic accuracy, suggesting that simple hematologic indices may serve as practical, noninvasive adjunctive tools in the preoperative assessment of brain tumor malignancy. These markers may assist in surgical prioritization, patient counseling, and clinical decision-making, particularly in resource-limited settings. Full article

Road transport across Asia is undergoing rapid motorisation and exemplifies growing road safety challenges, with rising accident rates closely linked to driver behaviour. Recent reports indicate that Vietnamese drivers often perceive risk as manageable and enforcement as inconsistent, contributing to habitual violations such as speeding, signal ignoring, and risky manoeuvres, particularly when traffic is light. Evidence shows that riders, especially young adults, feel confident controlling their vehicles and frequently disregard safety warnings. This study investigates traffic safety awareness among motorcyclists and car drivers in Hanoi, based on a questionnaire survey of 393 respondents. Principal Component Analysis (PCA) was used to group 11 attitudinal statements into key components influencing road safety perceptions, identifying five: non-compliance with traffic regulations (Component 1), aggressive driving behaviour (Component 2), traffic signal issues (Component 3), road quality and infrastructure (Component 4), and preventive measures (Component 5). Multiple Correspondence Analysis (MCA) and two-step cluster analysis (TCA) were then applied to determine user clusters by socio-demographic characteristics, producing three groups: young adults in employment riding motorcycles (Cluster 1), young adults in education riding motorcycles (Cluster 2), and mature adults in employment driving cars (Cluster 3). Finally, Multinomial Logistic Regression (MLR) was applied to assess variations in road safety perceptions across the different groups (clusters). Mature adults driving cars (Cluster 3) identified the first four components as significant, with Components 1 and 2 showing negative associations and Components 3 and 4 positive associations. Full article

The precise identification of pollution sources constitutes a cornerstone for effective water environment management in mountainous watersheds. This study employed principal component analysis–absolute principal component scores–multiple linear regression (PCA-APCS-MLR) receptor modeling to analyze monthly water quality indicators across the Longxi River Basin. Results revealed comparable water quality between the main stream and its tributaries, with no statistically significant differences identified. Water quality exhibited a distinct spatial pattern, with superior conditions in the upstream and downstream segments compared to the middle reaches. Water quality parameters exhibited significant seasonal variations. During the wet period, the degradation of water quality was primarily driven by diffuse agricultural sources, contributing 42.9%, followed by watershed background levels and surface runoff. In the dry season, rural domestic wastewater (39.3%) was the leading pollution source. For Permanganate index (COD_Mn) exceedance, basin background and agricultural non-point sources in the wet season were the main contributors (46.8% and 44.7%, respectively). For ammonium nitrogen (NH₃-N), wet season agricultural non-point sources (44.4%) and dry season rural domestic pollution (71.8%) were key contributors. Agricultural non-point sources were the dominant pollution source for total nitrogen (TN) in the wet season (84.2%). Effective water quality improvement in the Longxi River Basin hinges on targeted strategies—to mitigate diffuse agricultural sources through optimized fertilization, and to enhance the collection and treatment of rural domestic sewage. This study not only enhances the understanding of pollution source distribution and quantification in mountainous watersheds, but also serves as a vital reference for formulating targeted water environment management strategies. Full article

Groundwater, a critical resource for regional water security and public health, faces escalating threats from heavy metal contamination—a pressing environmental challenge worldwide. This study focuses on the central-south Hunan region of China, a mineral-rich, densely populated area characterized predominantly by non-point-source pollution, aiming to systematically unravel the spatial patterns, source contributions, and associated health risks of heavy metals in local groundwater. Based on 717 spring and well water samples collected in 2024, we determined pH and seven heavy metals (As, Cd, Pb, Zn, Fe, Mn, and Tl). By integrating hydrogeological zoning, lithology, topography, and river networks, the study area was divided into 11 assessment units, clearly revealing the spatial heterogeneity of heavy metals. The results demonstrate that exceedances of Cd, Pb, and Zn were sporadic and point-source-influenced, whereas As, Fe, Mn, and Tl showed regional exceedance patterns (e.g., Mn exceeded the standard in 9.76% of samples), identifying them as priority control elements. The spatial distribution of heavy metals was governed the synergistic effects of lithology, water–rock interactions, and hydrological structure, showing a distinct “acidic in the northeast, alkaline in the southwest” pH gradient. Combined application of the APCS-MLR and PMF models resolved five principal pollution sources: an acid-reducing-environment-driven release source (contributing 76.1% of Fe and 58.3% of Pb); a geogenic–anthropogenic composite source (contributing 81.0% of Tl and 62.4% of Cd); a human-perturbation-triggered natural Mn release source (contributing 94.8% of Mn); an agricultural-activity-related input source (contributing 60.1% of Zn); and a primary geological source (contributing 89.9% of As). Monte Carlo simulation-based health risk assessment indicated that the average hazard index (HI) and total carcinogenic risk (TCR) for all heavy metals were below acceptable thresholds, suggesting generally manageable risk. However, As was the dominant contributor to both non-carcinogenic and carcinogenic risks, with its carcinogenic risk exceeding the threshold in up to 3.84% of the simulated adult exposures under extreme scenarios. Sensitivity analysis identified exposure duration (ED) as the most influential parameter governing risk outcomes. In conclusion, we recommend implementing spatially differentiated management strategies: prioritizing As control in red-bed and granite–metamorphic zones; enhancing Tl monitoring in the northern and northeastern granite-rich areas, particularly downstream of the Mishui River; and regulating land use in brick-factory-dense riparian zones to mitigate disturbance-induced Mn release—for instance, through the enforcement of setback requirements and targeted groundwater monitoring programs. This study provides a scientific foundation for the sustainable management and safety assurance of groundwater resources in regions with similar geological and anthropogenic settings. Full article

Milling force is a parameter affecting wood processing quality, tool life, and energy consumption, and its variation is influenced by the multi-factor coupling of cutting parameters and tool geometric factors. This study systematically investigates milling forces during the processing of pine wood (Pinus sylvestris var. mongholica Litv.) using a hybrid modeling approach combining principal component analysis (PCA) and multiple linear regression (MLR). Firstly, PCA was employed to reduce the dimensionality of the tool rake angle (γ), helix angle (λ), cutting depth (h), feed per tooth (U_z), and triaxial milling forces (F_x, F_y, F_z); this eliminated the multicollinearity among variables and extracted the integrated features. Subsequently, an MLR model was constructed using the principal components as independent variables to quantitatively evaluate the contribution of each factor to milling forces. The results support the conclusion that PCA successfully extracted the first four principal components (cumulative variance contribution rate: 92.78%), with PC1 (49.16%) characterizing the comprehensive milling force effect and PC2 (15.03%) primarily reflecting the characteristics of the tool geometric parameters. The established MLR model demonstrated a high significance (R²: F_x = 0.915, F_y = 0.907, F_z = 0.852). The cutting depth exerted a significant positive driving effect on the triaxial milling forces via PC1 (each 1 mm increase in depth increased the PC1 score by 0.64 units, resulting in increases of 27.2%, 26.6%, and 21.8% for F_x, F_y, and F_z, respectively). The helix angle significantly suppressed F_y through PC2 (β = −0.090, p < 0.001), whereas the rake angle exhibited a weak negative effect on F_x via PC3 (β = −0.015). Parameter optimization identified the combination γ = 25°, λ = 30°, h = 0.5 mm, and U_z = 0.1 mm∙z⁻¹ as optimal, which reduced the triaxial milling forces by 62.3% compared to the experimental maximum. This study provides a theoretical foundation and novel parameter optimization strategy for the efficient, low-damage processing of wood materials. Full article

Eutrophication increasingly threatens tropical freshwater systems, where nutrient enrichment drives harmful algal blooms and rapid water-quality decline. This study presents a validated satellite-based approach for retrieving total nitrogen (TN) and total phosphorus (TP) in Thai inland waters using Sentinel-2 imagery. A stratified sampling campaign collected 264 water samples from 50 lentic water bodies during April–May 2024. Results showed that key environmental predictors were identified through correlation analysis and integrated into multiple linear regression (MLR) and polynomial regression (PO) algorithms, with performance evaluated using 10-fold cross-validation. PO consistently outperformed MLR for both nutrients. For TN, PO achieved higher calibration accuracy (R² = 0.63; RMSE = 4.74 mg/L) than MLR (R² = 0.47; RMSE = 5.73 mg/L) and maintained strong validation performance (R² = 0.76). For TP, PO likewise yielded superior cross-validation accuracy (R² = 0.69; RMSE = 0.07 mg/L; IoA = 0.89) compared with MLR (R² = 0.38; RMSE = 0.10 mg/L; IoA = 0.70). Spatial distributions of TN and TP derived from the imagery reinforced these findings. The PO-derived TN maps effectively captured nutrient hotspots associated with agricultural runoff, whereas the PO-based TP maps produced more stable and consistent spatial patterns. The findings demonstrate that Sentinel-2 can reliably retrieve TN and TP in tropical waters and offer a scalable pathway for improving nutrient surveillance and supporting science-based freshwater management in regions experiencing accelerating eutrophication. Full article

This study evaluates the performance of two real-time fluorescence-based bioaerosol sensors, the WIBS-NEO and IBAC-2, operating in urban Dublin, Ireland, and assesses the influence of different meteorological and pollution parameters on their outputs. This was done by comparing particle sensor data to meteorological variables and air quality metrics. Over the 41-day campaign, Urticaceae pollen and Cladosporium spores were the dominant bioaerosols recorded, comprising 78% and 66% of total pollen and fungal spore concentrations, respectively. Correlation analyses revealed several significant variables: fluorescent BC-type particles (>8 μm) detected by WIBS-NEO strongly correlated with pollen concentrations (r = 0.84 after excluding high-wind days). For fungal spores, PM₁₀ and grass minimum temperature were the most significant parameters related to variability. Anthropogenic pollutants, particularly NO_X and combustion-related aerosols, were found to correlate with fluorescence signals, especially for smaller particles (<2 μm), underscoring urban detection challenges. Wind trajectory analysis identified the likely source of Urticaceae pollen as northerly green spaces (e.g., Phoenix Park), while Cladosporium spores showed multidirectional transport. Multiple linear regression (MLR) analysis achieved strong correlation (${\mathrm{R}}^2$ = 0.82 for pollen, 0.78 for fungal spores), highlighting the value of incorporating multiple environmental variables to investigate the complex relationships between urban environmental conditions and bioaerosol sensor outputs. Both instruments exhibited operational limitations under the study conditions. The WIBS-NEO outperformed the IBAC-2 in biological discrimination due to its multi-channel single particle fluorescence capabilities. However, operational limitations emerged during higher wind speeds, comparable to moderate breezes (>16.6 km/h), which affected sampling comparability when compared with traditional methods. This study investigates how meteorological conditions and air quality influence bioaerosol detection in an urban environment. The use of MLR techniques to examine the complex relationships between environmental variables and fluorescent sensor outputs may help inform future bioaerosol modelling efforts. Full article