Assessing 3-D variability of ultrafine particle using a Geo-AI modelling approach: A case study in Zhunan-Miaoli, Taiwan.

Previous air pollution modeling studies have predominantly emphasized horizontal distributions, overlooking the critical vertical variability of pollutant concentrations in urban environments. Therefore, the three-dimensional (3-D) behavior of air pollutants, and of ultrafine particulate matter (PM) in particular, is insufficiently characterized. This study examined the 3-D distribution of PM in the Zhunan and Toufen regions in Miaoli, Taiwan.

Impact of urban residential environment and biological agents on childhood allergic diseases: Insights from the Greater Taipei area.

Pediatric allergic diseases pose a significant global public health concern, with environmental factors playing a crucial role in their development. Investigating these determinants, particularly during early childhood, is crucial. This study, part of the Longitudinal Examination Across Prenatal and Postnatal Health in Taiwan (LEAPP-HIT) project, focused on children under 5 years old in the Greater Taipei Area.

Heat exposure and adverse birth outcomes: Modification by residential greenness in a 14-year prospective cohort study in Taiwan.

Background: Pregnant women are vulnerable to climate change, with high ambient temperature (AT) associated with adverse birth outcomes. Inconsistent study designs, limited metrics, and unclear roles of greenness as an adaptation strategy hinder region-specific climate policies. This study assessed the effects of prenatal heat exposure on birth outcomes and whether greenness moderates this association.

Long-term monitoring of particulate matter in an Asian community using research-grade low-cost sensors.

Particulate matter with an aerodynamic diameter of 2.5 µm or less (PM) poses significant health risks, necessitating comprehensive exposure assessment. Long-term community monitoring can provide representative exposure levels for environmental epidemiological studies.

Innovating Taiwan's greenhouse gas estimation: A case study of atmospheric methane using GeoAI-Based ensemble mixed spatial prediction model.

This study addresses a gap in atmospheric greenhouse gas research, focusing on methane (CH), a gas with a global warming potential 80 times greater than carbon dioxide (CO). Unlike prior studies that focus on emission sources and reduction strategies, this research emphasizes the spatiotemporal variations in atmospheric CH concentrations, providing new perspectives on global climate mitigation efforts. A novel GeoAI-based ensemble mixed spatial prediction model was developed, integrating multiple machine learning algorithms and considering various factors to accurately estimate CH concentrations across Taiwan.

Diurnal temperature range and cardiopulmonary health in Taiwan: Evaluating impacts, thresholds, and vulnerable groups.

The health impacts of the diurnal temperature range (DTR), which may be affected by climate change, have received little attention. The objectives of this study were (1) to evaluate the association of DTR and cardiopulmonary outcomes, (2) to select the proper thresholds for a DTR warning system, and (3) to identify vulnerable groups. The weather and health records in Taiwan from 2000 to 2019, with a maximum DTR of 12.

What is the spatiotemporal pattern of benzene concentration spread over susceptible area surrounding the Hartman Park community, Houston, Texas?

The Hartman Park community in Houston, Texas-USA, is in a highly polluted area which poses significant risks to its predominantly Hispanic and lower-income residents. Surrounded by dense clustering of industrial facilities compounds health and safety hazards, exacerbating environmental and social inequalities. Such conditions emphasize the urgent need for environmental measures that focus on investigating ambient air quality.

Peaks, sources, and immediate health impacts of PM and PM exposure in Indonesia and Taiwan with microsensors.

Background: Microsensors have been used for the high-resolution particulate matter (PM) monitoring.

Objectives: This study applies PM and health microsensors with the objective of assessing the peak exposure, sources, and immediate health impacts of PM and PM in two Asian countries.

Methods: Exposure assessment and health evaluation were carried out for 50 subjects in 2018 and 2019 in Bandung, Indonesia and for 55 subjects in 2019 and 2020 in Kaohsiung, Taiwan.

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model.

Nitrogen dioxide (NO) 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 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 concentrations at a high resolution of 50m.

Explainable geospatial-artificial intelligence models for the estimation of PM concentration variation during commuting rush hours in Taiwan.

PM concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 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 concentrations during morning and dusk rush hours in Taiwan.

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan.

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NO), 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.

Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model.

Background: The increase in global temperature and urban warming has led to the exacerbation of heatwaves, which negatively affect human health and cause long-term loss of work productivity. Therefore, a global assessment in temperature variation is essential.

Objective: This paper is the first of its kind to propose land-use based spatial machine learning (LBSM) models for predicting highly spatial-temporal variations of wet-bulb globe temperature (WBGT), which is a heat stress indicator used to assess thermal comfort in indoor and outdoor environments, specifically for the main island of Taiwan.

Estimating morning and evening commute period O concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology.

Elevated levels of ground-level ozone (O) 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 concentrations across the entire island of Taiwan.

A pilot heat-health warning system co-designed for a subtropical city.

Significant heat-related casualties underlie the urgency of establishing a heat-health warning system (HHWS). This paper presents an evidence-based pilot HHWS developed for Taipei City, Taiwan, through a co-design process engaging stakeholders. In the co-design process, policy concerns related to biometeorology, epidemiology and public health, and risk communication aspects were identified, with knowledge gaps being filled by subsequent findings.

Impact of physical and social living environments on pro-environmental intentions.

The living environment might play an important role in shaping the pro-environmental intentions of the people. However, there was limited research on how the living environments influenced the pro-environmental intentions of people. The objectives of this study are to evaluate the direct effects of physical and social environments on pro-environmental intentions as well as the mediating effects of environmental attitudes and life satisfaction.

Greenspace related to bipolar disorder in Taiwan: Quantitative benefits of saving DALY loss and increasing income.

Scientific evidence reported that surrounding greenspace could promote better mental health. Considering bipolar disorder as the health outcome, this study aimed to investigate the association between greenspace and bipolar disorder in Taiwan and quantified the benefits of greenspace on bipolar disorder adjusted for the international greenspace availability standard. By examining datasets across 348 townships, two quantitative measures (i.

Effects of indoor air quality and home environmental characteristics on allergic diseases among preschool children in the Greater Taipei Area.

Indoor air quality and home environmental characteristics are potential factors associated with the onset and exacerbation of allergic diseases. Our study examined the effects of these factors on allergic diseases (i.e.

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

High levels of ground level ozone (O) 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.

An ensemble mixed spatial model in estimating long-term and diurnal variations of PM in Taiwan.

Meteorology, human activities, and other emission sources drive diurnal cyclic patterns of air pollution. Previous studies mainly focused on the variation of PM concentrations during daytime rather than nighttime. In addition, assessing the spatial variations of PM in large areas is a critical issue for environmental epidemiological studies to clarify the health effects from PM exposures.

Sex-differences in the effects of indoor air pollutants and household environment on preschool child cognitive development.

Air pollution, outdoor residential environment, indoor household characteristics, and parental mental health are potential factors associated with child development. However, few studies have simultaneously analyzed the association between the aforementioned factors and preschool child (aged 2-5 years) development. This study investigated the effects of those factors on child development and their potential modifying effects.

Model development and validation of personal exposure to PM among urban elders.

Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM were conducted for an elderly population in the Taipei city of Taiwan.

Exploring the Potential Relationship Between Global Greenness and DALY Loss Due to Depressive Disorders.

Objective: Prior studies have shown that greenness can reduce the burden of depressive disorders. However, most were focused on local-scale analyses while limited evaluated globally. We aimed to investigate the association between greenness and the burden of depressive disorders using data from 183 countries worldwide.