Public health is threatened by air pollution and high temperature, especially in urban areas and areas impacted by climate change. Well-designed urban forms have co-benefits on promoting human health and mediating atmospheric environment-related threats (e.g., high temperature and air pollution). Previous studies overlooked these mediating effects of urban form on suicide mortality. This study used partial least squares modeling and countywide data in Taiwan to identify the crucial influences and pathways of urban environment, socioeconomic status, and diseases on suicide mortality. The model considered the impact of the characteristics of urban form (i.e., urban development intensity, land mix, and urban sprawl), urban industrial status (i.e., industrial level), urban greening (i.e., green coverage), disease (i.e., important diseases morbidity of human immunodeficiency virus [HIV], cerebrovascular disease [CVD], chronic liver disease and cirrhosis [CLDC], nephritis, nephrotic syndrome and nephrosis [NNSN], malignant tumor [MT]), socioeconomic status (i.e., income level and aging population rate), and the atmospheric environment (i.e., air pollution and high temperature) on suicide mortality. Optimizing land mix and minimizing urban development intensity and urban sprawl have been found to reduce suicide mortality. The mediating effect of urban form on suicide mortality originated from air pollution and high temperature, and mediating air pollution was greater than high temperature. Furthermore, industrial level, important diseases (HIV, CVD, CLDC, NNSN, and MT) morbidity, an aging population rate, air pollution, and high temperature were associated with an increase in suicide mortality, whereas green coverage and income level were associated with a reduction in suicide rates. The findings demonstrate that appropriate urban policy and urban planning may lower suicide mortality, be useful strategies for suicide prevention, and be a foundation for building a healthy city. Moreover, this study provides clarity on the complex relationship of suicide and the urban environment while identifying crucial factors.

Seawater, sediments, and three genera of wild scleractinian corals were collected from four coral reef areas nearshore Liuqiu Island, southwestern Taiwan. Abundance, characteristics (sizes, colors, shapes, and polymer types), and enrichment of microplastics (MPs) in the corals, and their impacts on coral cover were determined. The average MPs abundances were 0.95, 0.77, and 0.36 item/g for Galaxea sp, Acropora spp, and Pocillopora sp, respectively. The MPs abundance was relatively higher on the coral surfaces than inside the skeletons, dominated by blue rayon-fibers, correspondingly observed in seawater and sediments. Large-size colorless MPs tended to be mis-ingested by Galaxea sp. (71%) compared with Pocillopora sp. (43%) and Acropora spp. (31%). The low hard coral cover (12.5%) observed at Yufu (L1) on the northeastern coastal zone nearby tourism center of Liuqiu Island where correspondingly associated with high MPs abundance in seawater (10 item/L), sediments (260 item/kg), and corals (0.60 item/g). Tourism induced sewage discharges and sailing activities significantly contributed to the MPs pollution, probably contributing to the loss of coral cover. High MPs enrichment in corals (EFMP = 25–283) shows that the marine MPs pollution can critically threaten coral reef ecosystems. Fibrous MPs present inside the coral skeleton serve as potential indicator of MPs’ impact on corals—with the dominance of textile-related rayon and polyester/PET microfibers in the coral reef zones. This study provided valuable information for coral conservation and coastal management.

Microplastics (MPs) in the ocean have been widely recognized as causing global marine environmental problems. To gain a quantitative and comprehensive understanding of oceanic MP contamination, detailed numerical Lagrangian particle tracking experiments were conducted to evaluate the regional oceanic transport and dispersal of MPs in the South China Sea (SCS) derived from three major rivers, Pearl (China), Mekong (Vietnam), and Pasig (the Philippines), which are known to discharge large amounts of plastic waste into the SCS. As previous field surveys have suggested, MP contamination spreads from the surface to the deeper ocean in the water column, we thus considered three types of MPs: (1) positively buoyant (light) MPs, (2) positively buoyant (light) MPs with random walk diffusion, and (3) full 3-D tracking of non-buoyant MPs that are passively transported by ambient currents. Transport patterns of these MPs from the three rivers clearly showed the intra-annual variability associated with seasonally varying circulations driven by the Asian monsoons in the SCS. Many MPs floating during the prevailing southwest monsoon are transported to the northwest Pacific Ocean and the East China Sea through the Luzon Strait and the Taiwan Strait to form MP hotspots. Non-buoyant MPs are broadly transported from the surface layer to depths of approximately 100 m or deeper, where in situ observations are rare. In addition, the buoyant MPs drifting on the continental shelf originating from southern China tend to be pushed toward the shore and beached by northward wind-induced currents more pronouncedly than the non-buoyant MPs. Therefore, the river-derived MPs to the SCS were found to serve as sources to adjacent basins and oceans, to be distributed not only in the upper layer but also in the abyssal ocean (non-buoyant MPs), and to be transported to the shores (buoyant MPs).

Fine particulate matter (PM₂.₅) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM₂.₅ measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM₂.₅ estimates in a study area. In this study, we used AOD estimates of surface PM₂.₅ together with meteorological and land use variables to estimate monthly PM₂.₅ concentrations at a spatial resolution of 3 km² over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R² of 0.98 with a root mean square error (RMSE) of 1.40 μg/m3. The leave-one-out cross-validation (LOOCV) R² with seasonal stratification was 0.82, and the RMSE was 3.85 μg/m3, whereas the R² and RMSE obtained by using the pure random forest approach produced R² and RMSE values of 0.74 and 4.60 μg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM₂.₅ estimates over the entire island, which has complex terrain in terms of land use and topography.

The green turtle (Chelonia mydas) is listed as a globally endangered species and is vulnerable to anthropogenic threats, including environmental pollution. This study investigated the antimicrobial resistance of Gram-negative bacteria isolated from wild green turtles admitted to a sea turtle rehabilitation center in Taiwan. For this investigation, cloacal and nasal swab samples were collected from 28 green turtles between 2018 and 2020, from which a total of 47 Gram-negative bacterial isolates were identified. Among these, Vibrio spp. were the most dominant isolate (31.91%), and 89.36% of the 47 isolates showed resistance to at least one of 18 antimicrobial agents tested. Isolates resistant to one (6.38%), two (8.51%), and multiple (74.47%) antimicrobials were observed. The antimicrobial agents to which isolates showed the greatest resistance were penicillin (74.47%), followed by spiramycin, amoxicillin, and cephalexin. The antimicrobial-resistance profiles identified in this study provide useful information for the clinical treatment of sea turtles in rehabilitation facilities. The results of our study also imply that wild green turtles may be exposed to polluting effluents containing antimicrobials when the turtles traverse migratory corridors or forage in feeding habitats. To benefit sea turtle conservation, future research should focus on (1) how to prevent pollution from antimicrobials in major green turtle activity areas and (2) identifying sources of antimicrobial-resistant bacterial strains in coastal waters of Taiwan.

Although many researchers have identified the potential psychological benefits offered by greenness, the association between green space structures and mental disorders is not well understood. The purpose of this study was to identify associations between green space structures and the incidence of bipolar disorder. To this end, we investigated 1,907,776 individuals collected from Taiwan’s National Health Insurance Research Database. After a follow-up investigation from 2005 to 2016, among those with no history of bipolar disorder, 20,548 individuals were further found to be diagnosed with bipolar disorder. A geographic information system and landscape index were used to quantify three indices of green space structures: mean patch area (area and edge), mean fractal dimension index (shape), and mean proximity index (proximity). Additionally, greenness indices, the normalized difference vegetation index, and the enhanced vegetation index were used to confirm the association between greenness and incidence of bipolar disorder. These five indices were used to represent the individual’s exposure according to the township of the hospital that they most frequently visited with symptoms of the common cold. Spearman’s correlation analysis was performed to select variables by considering their collinearity. Subsequently, the frailty model for each index was used to examine the specific associations between those respective indices and the incidence of bipolar disorder by adjusting for related risk factors, such as socioeconomic status, metabolic syndrome, and air pollution. A negative association was identified between the mean patch area and the mean proximity index, and the incidence of bipolar disorder. In contrast, a positive association was found between the mean fractal dimension index and the incidence of bipolar disorder. We observed similar results in sensitivity testing and subgroup analysis. Exposure to green spaces with a larger area, greater proximity, lower complexity, and greener area may reduce the risk of bipolar disorder.

The levels and characteristics of atmospheric metals vary in time and location, can result in various health impacts, which increases the challenge of air quality management. We aimed to investigate PM₂.₅-bound metals in multiple locations and propose a methodology for comparing metal elements across study regions and prioritizing source contributions through integrated health risk assessments. PM₂.₅-bound metals were collected in the urban, suburban, rural, and industrial regions of Taiwan between 2016 and 2018. We incorporated the positive matrix factorization (PMF) with health risk assessments (considering estimates of the margin of exposure (MOE) and excess cancer risk (ECR)) to prioritize sources for control. We found that the concentrations of Fe, Zn, V, Cu, and Mn (industry-related metals) were higher at the industrial site (Kaohsiung) and Ba, Cr, Ni, Mo, and Co (traffic-related metals) were higher at the urban site (Taipei). The rural site (Hualian) had good air quality, with low PM₂.₅ and metal concentrations. Most metal concentrations were higher during the cold season for all study sites, except for the rural. Ambient concentrations of Mn, Cr, and Pb obtained from all study sites presents a higher health risk of concern. In Kaohsiung, south Taiwan, PM₂.₅-bound metals from the iron ore and steel factory is suggested as the first target for control based on the calculated health risks (MOE < 1 and ECR > 10⁻⁶). Overall, we proposed an integrated strategy for initiating the source management prioritization of PM₂.₅-bound metals, which can aid an effort for policymaking.

This study examined the long-term trends in chemical components in PM₂.₅ (particulate matter with aerodynamic diameter ≤2.5 μm) samples collected at Lulin Atmospheric Background Station (LABS) located on the summit of Mt. Lulin (2862 m above mean sea level) in Taiwan in the western North Pacific during 2003–2018. High ambient concentrations of PM₂.₅ and its chemical components were observed during March and April every year. This enhancement was primarily associated with the long-range transport of biomass burning (BB) smoke emissions from Indochina, as revealed from cluster analysis of backward air mass trajectories. The decreasing trends in ambient concentrations of organic carbon (−0.67% yr⁻¹; p = 0.01), elemental carbon (−0.48% yr⁻¹; p = 0.18), and non–sea-salt (nss) K⁺ (−0.71% yr⁻¹; p = 0.04) during 2003–2018 indicated a declining effect of transported BB aerosol over the western North Pacific. These findings were supported by the decreasing trend in levoglucosan (−0.26% yr⁻¹; p = 0.20) during the period affected by the long-range transport of BB aerosol. However, NO₃⁻ displayed an increasing trend (0.71% yr⁻¹; p = 0.003) with considerable enhancement resulting from the air masses transported from the Asian continent. Given that the decreasing trends were for the majority of the chemical components, the columnar aerosol optical depth (AOD) also demonstrated a decreasing trend (−1.04% yr⁻¹; p = 0.0001) during 2006–2018. Overall decreasing trends in ambient (carbonaceous aerosol and nss-K⁺) as well as columnar (e.g., AOD) aerosol loadings at the LABS may influence the regional climate, which warrants further investigations. This study provides an improved understanding of the long-term trends in PM₂.₅ chemical components over the western North Pacific, and the results would be highly useful in model simulations for evaluating the effects of BB transport on an area.

A widespread monitoring network of Airbox microsensors was implemented since 2016 to provide high-resolution spatial distributions of ground-level PM₂.₅ data in Taiwan. We developed models for estimating ground-level PM₂.₅ concentrations for all the 3 km × 3 km grids in Taiwan by combining the data from air quality monitoring stations and the Airbox sensors. The PM₂.₅ data from the Airbox sensors (AB-PM₂.₅) was used to predict daily mean PM₂.₅ levels at the grids in 2017 using a semiparametric additive model. The estimated PM₂.₅ level at the grids was further applied as a predictor variable in the models to predict the monthly mean concentration of PM₂.₅ at all the grids in the previous year. The modeling–predicting procedures were repeated backward for the years from 2016 to 2006. The model results revealed that the model R² increased from 0.40 to 0.87 when the AB-PM₂.₅ data were included as a nonlinear component in the model, indicating that AB-PM₂.₅ is a significant predictor of ground-level PM₂.₅ concentration. The cross-validation (CV) results demonstrated that the root of mean squared prediction errors of the estimated monthly mean PM₂.₅ concentrations were smaller than 5 μg/m³ and the R² of the CV models of 0.79–0.88 during 2006–2017. We concluded that Airbox sensors can be used with monitoring data to more accurately estimate long-term exposure to PM₂.₅ for cohorts of small areas in health impact assessment studies.