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Nitrogen dioxide (NO 2 ) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO 2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO 2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO 2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R 2 values of 0.92 and 0.93, respectively. The study revealed that mean NO 2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO 2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO 2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

PM 2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM 2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM 2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM 2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM 2.5 values, SO 2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM 2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NO x ), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NO x across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NO x with high explanatory powers (Adj-R 2 ) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NO x , with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NO x concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Elevated levels of ground-level ozone (O 3 ) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O 3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O 3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R 2 of 0.91. Internal and external validation procedures and verification of O 3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O 3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O 3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) has become a global epidemic, and air pollution has been identified as a potential risk factor. This study aims to investigate the non-linear relationship between ambient air pollution and MASLD prevalence., Method: In this cross-sectional study, participants undergoing health checkups were assessed for three-year average air pollution exposure. MASLD diagnosis required hepatic steatosis with at least 1 out of 5 cardiometabolic criteria. A stepwise approach combining data visualization and regression modeling was used to determine the most appropriate link function between each of the six air pollutants and MASLD. A covariate-adjusted six-pollutant model was constructed accordingly., Results: A total of 131,592 participants were included, with 40.6% met the criteria of MASLD. "Threshold link function," "interaction link function," and "restricted cubic spline (RCS) link functions" best-fitted associations between MASLD and PM 2.5 , PM 10 /CO, and O 3 /SO 2 /NO 2 , respectively. In the six-pollutant model, significant positive associations were observed when pollutant concentrations were over: 34.64 µg/m 3 for PM 2.5 , 57.93 µg/m 3 for PM 10 , 56 µg/m 3 for O 3 , below 643.6 µg/m 3 for CO, and within 33 and 48 µg/m 3 for NO 2 . The six-pollutant model using these best-fitted link functions demonstrated superior model fitting compared to exposure-categorized model or linear link function model assuming proportionality of odds., Conclusion: Non-linear associations were found between air pollutants and MASLD prevalence. PM 2.5 , PM 10 , O 3 , CO, and NO 2 exhibited positive associations with MASLD in specific concentration ranges, highlighting the need to consider non-linear relationships in assessing the impact of air pollution on MASLD.

High levels of ground level ozone (O 3 ) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O 3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R 2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Ambient fine particulate matter (PM 2.5 ) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM 2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM 2.5 . Daily average PM 2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM 2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM 2.5 exposures., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Estimating short-term exposure to PM 2.5 has been achieved for population health studies using the land use regression with machine learning (LUR_ML) and microenvironmental exposure (ME) models. However, there is a lack of clarity regarding the performance of these models in predicting PM 2.5 exposure for individuals residing in diverse environments, and the factors influencing the variations in accuracy between these models. This study performed the LUR_ML and ME models to estimate daily exposure concentrations of PM 2.5 for elders residing in urban, suburban, rural, and industrial regions in Taiwan. The accuracy of the model predictions was assessed by comparing them with personal PM 2.5 monitoring for both overall and regional assessments. The LUR_ML model demonstrated reasonably moderate agreement (R2 = 0.516) overall with personal exposure to PM 2.5 , while the ME models exhibited relatively higher predictions (R2 = 0.535–0.575) and lower biases. The agreement of PM 2.5 predictions varies across regions, particularly in areas with higher exposure contrast. The ME model 1, utilizing region-specific microenvironmental measurements rather than generic data, highlights the potential for accurate prediction of personal PM 2.5 exposure. This study contributed to the understanding of variations in prediction accuracy across different regions and support the need for improved exposure models of air pollution. [Display omitted] • The land use regression with machine learning moderately agreed (R2 = 0.516) with PM 2.5 exposure. • Microenvironmental exposure models had a higher prediction (R2 = 0.535–0.575) and lower bias. • Exposure predictions of daily PM 2.5 concentrations vary by regions. • Indoor exposure is a stronger predictor of personal PM 2.5 exposure than outdoor exposure. • Higher PM 2.5 exposure agreement is likely due to greater exposure contrast in the population. [ABSTRACT FROM AUTHOR]

It is generally established that PCDD/Fs is harmful to human health and therefore extensive field research is necessary. This study is the first to use a novel geospatial-artificial intelligence (Geo-AI) based ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and geographic predictor variables selected using SHapley Additive exPlanations (SHAP) values to predict spatial-temporal fluctuations in PCDD/Fs concentrations across the entire island of Taiwan. Daily PCDD/F I-TEQ levels from 2006 to 2016 were used for model construction, while external data was used for validating model dependability. We utilized Geo-AI, incorporating kriging, five machine learning, and ensemble methods (combinations of the aforementioned five models) to develop EMSMs. The EMSMs were used to estimate long-term spatiotemporal variations in PCDD/F I-TEQ levels, considering in-situ measurements, meteorological factors, geospatial predictors, social and seasonal influences over a 10-year period. The findings demonstrated that the EMSM was superior to all other models, with an increase in explanatory power reaching 87 %. The results of spatial-temporal resolution show that the temporal fluctuation of PCDD/F concentrations can be a result of weather circumstances, while geographical variance can be the result of urbanization and industrialization. These results provide accurate estimates that support pollution control measures and epidemiological studies. [Display omitted] • A novel Geo-AI with 10-year data was used to predict daily PCDD/Fs across Taiwan. • The Shapley Additive exPlanations (SHAP) was used for predictor selection. • The long-term PCDD/Fs variations across Taiwan main island were examined. • The ensemble mixed spatial model can refine air pollution modelling techniques. • The EMSMs demonstrated strong performance (R2 87 %) and robustness in validations. [ABSTRACT FROM AUTHOR]