Explore Regional PM2.5 Features and Compositions Causing Health Effects in Taiwan

A comprehensive examination of temporal-seasonal variations of PM1.0 and PM2.5 in taiwan before and during the COVID-19 lockdown

COVID-19 has been a significant global concern due to its contagious nature. In May 2021, Taiwan experienced a severe outbreak, leading the government to enforce strict Pandemic Alert Level 3 restrictions in order to curtail its spread. Although previous studies in Taiwan have examined the effects of these measures on air quality, further research is required to compare different time periods and assess the health implications of reducing particulate matter during the Level 3 lockdown. Herein, we analyzed the mass concentrations, chemical compositions, seasonal variations, sources, and potential health risks of PM1.0 and PM2.5 in Central Taiwan before and during the Level 3 lockdown. As a result, coal-fired boilers (47%) and traffic emissions (53%) were identified as the predominant sources of polycyclic aromatic hydrocarbons (PAHs) in PM1.0, while in PM2.5, the dominant sources of PAHs were coal-fired boilers (28%), traffic emissions (50%), and iron and steel sinter plants (22.1%). Before the pandemic, a greater value of 20.9 ± 6.92 μg/m3 was observed for PM2.5, which decreased to 15.3 ± 2.51 μg/m3 during the pandemic due to a reduction in industrial and anthropogenic emissions. Additionally, prior to the pandemic, PM1.0 had a contribution rate of 79% to PM2.5, which changed to 89% during the pandemic. Similarly, BaPeq values in PM2.5 exhibited a comparable trend, with PM1.0 contributing 86% and 65% respectively. In both periods, the OC/EC ratios for PM1.0 and PM2.5 were above 2, due to secondary organic compounds. The incremental lifetime cancer risk (ILCR) of PAHs in PM2.5 decreased by 4.03 × 10-5 during the pandemic, with PM1.0 contributing 73% due to reduced anthropogenic activities.

Adverse effects of exposure to fine particles and ultrafine particles in the environment on different organs of organisms

Particulate pollution is a global risk factor that seriously threatens human health. Fine particles (FPs) and ultrafine particles (UFPs) have small particle diameters and large specific surface areas, which can easily adsorb metals, microorganisms and other pollutants. FPs and UFPs can enter the human body in multiple ways and can be easily and quickly absorbed by the cells, tissues and organs. In the body, the particles can induce oxidative stress, inflammatory response and apoptosis, furthermore causing great adverse effects. Epidemiological studies mainly take the population as the research object to study the distribution of diseases and health conditions in a specific population and to focus on the identification of influencing factors. However, the mechanism by which a substance harms the health of organisms is mainly demonstrated through toxicological studies. Combining epidemiological studies with toxicological studies will provide a more systematic and comprehensive understanding of the impact of PM on the health of organisms. In this review, the sources, compositions, and morphologies of FPs and UFPs are briefly introduced in the first part. The effects and action mechanisms of exposure to FPs and UFPs on the heart, lungs, brain, liver, spleen, kidneys, pancreas, gastrointestinal tract, joints and reproductive system are systematically summarized. In addition, challenges are further pointed out at the end of the paper. This work provides useful theoretical guidance and a strong experimental foundation for investigating and preventing the adverse effects of FPs and UFPs on human health.

Transcriptomics profile of human bronchial epithelial cells exposed to ambient fine particles and influenza virus (H3N2)

Fine particulate matter (PM2.5) pollution remains a major threat to public health. As the physical barrier against inhaled air pollutants, airway epithelium is a primary target for PM2.5 and influenza viruses, two major environmental insults. Recent studies have shown that PM2.5 and influenza viruses may interact to aggravate airway inflammation, an essential event in the pathogenesis of diverse pulmonary diseases. Airway epithelium plays a critical role in lung health and disorders. Thus far, the mechanisms for the interactive effect of PM2.5 and the influenza virus on gene transcription of airway epithelial cells have not been fully uncovered. In this present pilot study, the transcriptome sequencing approach was introduced to identify responsive genes following individual and co-exposure to PM2.5 and influenza A (H3N2) viruses in a human bronchial epithelial cell line (BEAS-2B). Enrichment analysis revealed the function of differentially expressed genes (DEGs). Specifically, the DEGs enriched in the xenobiotic metabolism by the cytochrome P450 pathway were linked to PM2.5 exposure. In contrast, the DEGs enriched in environmental information processing and human diseases, such as viral protein interaction with cytokines and cytokine receptors and epithelial cell signaling in bacterial infection, were significantly related to H3N2 exposure. Meanwhile, co-exposure to PM2.5 and H3N2 affected G protein-coupled receptors on the cell surface. Thus, the results from this study provides insights into PM2.5- and influenza virus-induced airway inflammation and potential mechanisms.

The influence of PM2.5 exposure duration and concentration on outpatient visits of urban hospital in a typical heavy industrial city

To explain the duration and dose effects of pollutant exposure on public health and provide scientific data for air pollution prevention and control and disease prevention by examining the influence of PM2.5 concentration and exposure duration on daily outpatient visits among patients with cardiovascular, cerebrovascular, and respiratory diseases in a typical heavy industrial city in China. Daily outpatient data on cardiovascular, cerebrovascular, and respiratory diseases and regional PM2.5 exposure duration and concentration were collected from a provincial hospital in Taiyuan, China, from 2016 to 2021. The correlations of numeric variables were analyzed using the Pearson correlation method. A generalized additive model (GAMs) was also established to investigate the effects of PM2.5 concentration and exposure duration on outpatient visits. Correlation analysis showed that the outpatient visits in Taiyuan was significantly correlated with the PM2.5 concentration and exposure duration. The longer the exposure time of PM2.5 pollution, the stronger the correlation of PM2.5 with outpatient visits showed. Cardiovascular outpatient visits were extremely significant related with medium to long-term exposure of PM2.5 (exposure with more than 30 days) (p < 0.001). In addition, outpatient visits of cerebrovascular and respiratory disease were extremely significant correlated with PM2.5 (exposures within 0-360 days) (p < 0.001). The results of GAMs showed the linear or the nonlinear relationships between outpatient visits and exposure of PM2.5. Among the linear relationships, when average concentration of PM2.5 (exposure within less than 15 days) increased by 1 mg/m3, the cardiovascular outpatient visits increased most dramatically (by about 440 people). For nonlinear relationships, when the average PM2.5 concentration (exposure with over 30 days or more) increased by 1 mg/m3, the most dramatic increase occurred in cardiovascular outpatient visits (with a maximum increase of 7000), followed by cerebrovascular outpatient visits (with a maximum increase of 1200), and respiratory outpatient visits (with a maximum increase of 250). The GAMs also revealed a dose effect in the relationship between outpatient visits and PM2.5 exposure. In moderately polluted air (based on air quality standards of China, GB3095-2012), when the average concentration of PM2.5 increased by 1 mg/m3, the cardiovascular outpatient visits increased the most (by 1200 people), followed by cerebrovascular outpatient visits (by 200 people) and respiratory outpatient visits (by 20 people). We concluded that outpatient visits in cardiovascular, cerebrovascular, and respiratory disease are closely correlated with the concentration and exposure duration of air pollution. There is a linear relationship between short-term air pollution exposure (exposure within less than 15 days) and outpatient visits. As PM2.5 concentration increases, cardiovascular outpatient visits increase gradually, with its growth trend exceeding that of cerebrovascular and respiratory disease. There is a nonlinear relationship between medium and long-term air pollution exposure (exposure with more than 30 days) and outpatient visits, with cardiovascular and cerebrovascular outpatient visits showed a nonlinear but overall upward trend when the atmosphere is moderately polluted.

Quantifying the potential effects of air pollution reduction on population health and health expenditure in Taiwan

Air pollution, particularly ambient fine particulate matter (PM2.5) pollution, poses a significant risk to public health, underscoring the importance of comprehending the long-term impact on health burden and expenditure at national and subnational levels. Therefore, this study aims to quantify the disease burden and healthcare expenditure associated with PM2.5 exposure in Taiwan and assess the potential benefits of reducing pollution levels. Using a comparative risk assessment framework that integrates an auto-aggressive integrated moving average model, we evaluated the avoidable burden of cardiopulmonary diseases (including ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and diabetes mellitus) and related healthcare expenditure under different air quality target scenarios, including status quo and target scenarios of 15, 10, and 5 μg/m3 reduction in PM2.5 concentration. Our findings indicate that reducing PM2.5 exposure has the potential to significantly alleviate the burden of multiple diseases. Comparing the estimated attributable disease burden and healthcare expenditure between reference and target scenarios from 2022 to 2050, the avoidable disability-adjusted life years were 0.61, 1.83, and 3.19 million for the 15, 10, and 5 μg/m3 target scenarios, respectively. Correspondingly, avoidable healthcare expenditure ranged from US$ 0.63 to 3.67 billion. We also highlighted the unequal allocation of resources and the need for policy interventions to address health disparities due to air pollution. Notably, in the 5 μg/m3 target scenario, Kaohsiung City stands to benefit the most, with 527,368 disability-adjusted life years avoided and US$ 0.53 billion saved from 2022 to 2050. Our findings suggest that adopting stricter emission targets can effectively reduce the health burden and associated healthcare expenditure in Taiwan. Overall, this study provides policymakers in Taiwan with valuable insights for mitigating the negative effects of air pollution by establishing a comprehensive framework for evaluating the co-benefits of air pollution reduction on healthcare expenditure and disease burden.

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

OBJECTIVE

To investigate potential interactive effects of fine particulate matter (PM2.5) and ozone (O3) on daily mortality at global level.

DESIGN

Two stage time series analysis.

SETTING

372 cities across 19 countries and regions.

POPULATION

Daily counts of deaths from all causes, cardiovascular disease, and respiratory disease.

MAIN OUTCOME MEASURE

Daily mortality data during 1994-2020. Stratified analyses by co-pollutant exposures and synergy index (>1 denotes the combined effect of pollutants is greater than individual effects) were applied to explore the interaction between PM2.5 and O3 in association with mortality.

RESULTS

During the study period across the 372 cities, 19.3 million deaths were attributable to all causes, 5.3 million to cardiovascular disease, and 1.9 million to respiratory disease. The risk of total mortality for a 10 μg/m3 increment in PM2.5 (lag 0-1 days) ranged from 0.47% (95% confidence interval 0.26% to 0.67%) to 1.25% (1.02% to 1.48%) from the lowest to highest fourths of O3 concentration; and for a 10 μg/m3 increase in O3 ranged from 0.04% (-0.09% to 0.16%) to 0.29% (0.18% to 0.39%) from the lowest to highest fourths of PM2.5 concentration, with significant differences between strata (P for interaction <0.001). A significant synergistic interaction was also identified between PM2.5 and O3 for total mortality, with a synergy index of 1.93 (95% confidence interval 1.47 to 3.34). Subgroup analyses showed that interactions between PM2.5 and O3 on all three mortality endpoints were more prominent in high latitude regions and during cold seasons.

CONCLUSION

The findings of this study suggest a synergistic effect of PM2.5 and O3 on total, cardiovascular, and respiratory mortality, indicating the benefit of coordinated control strategies for both pollutants.

Influential factors of tuberculosis in mainland China based on MGWR model

Tuberculosis (TB), as a respiratory infectious disease, has damaged public health globally for decades, and mainland China has always been an area with high incidence of TB. Since the outbreak of COVID-19, it has seriously occupied medical resources and affected medical treatment of TB patients. Therefore, the authenticity and reliability of TB data during this period have also been questioned by many researchers. In response to this situation, this paper excludes the data from 2019 to the present, and collects the data of TB incidence in mainland China and the data of 11 influencing factors from 2014 to 2018. Using spatial autocorrelation methods and multiscale geographically weighted regression (MGWR) model to study the temporal and spatial distribution of TB incidence in mainland China and the influence of selected influencing factors on TB incidence. The experimental results show that the distribution of TB patients in mainland China shows spatial aggregation and spatial heterogeneity during this period. And the R2 and the adjusted R2 of MGWR model are 0.932 and 0.910, which are significantly better than OLS model (0.466, 0.429) and GWR model (0.836, 0.797). The fitting accuracy indicators MAE, MSE and MAPE of MGWR model reached 5.802075, 110.865107 and 0.088215 respectively, which also show that the overall fitting effect is significantly better than OLS model (19.987574, 869.181549, 0.314281) and GWR model (10.508819, 267.176741, 0.169292). Therefore, this model is based on real and reliable TB data, which provides decision-making references for the prevention and control of TB in mainland China and other countries.

Association between traffic-related air pollution and osteoporotic fracture hospitalizations in inland and coastal areas: evidences from the central areas of two cities in Shandong Province, China

Our result showed that short-term exposure to traffic-related air pollutants (TRAPs) might increase the risk of hospitalizations for osteoporotic fractures. It was suggested that government should formulate emission reduction policies to protect the health of citizens.

INTRODUCTION

As the main source of urban air pollution in China, exhaust emissions of motor vehicles have been linked to adverse health outcomes, but evidence of the relationship between short-term exposure to TRAPs and osteoporotic fractures is still relatively rare.

METHODS

In this study, a total of 5044 inpatients from an inland city (Jinan) and a coastal city (Qingdao), two cities with developed transportation in Shandong Province, were included. A generalized additive model (GAM) was used to investigate the association between TRAPs and hospitalizations for osteoporotic fractures. The stratified analyses were performed by gender and age.

RESULTS

Positive associations between TRAPs and osteoporotic fracture hospitalizations were observed. We found that short-term exposure to TRAPs was associated with increased numbers of hospitalizations for osteoporotic fractures. PM2.5 and PM10 were statistically significant associated with hospitalizations for osteoporotic fractures at both single-day and multiday lag structures only in Qingdao, with the strongest associations at lag06 and lag07 [RR=1.0446(95%CI: 1.0018,1.0891) for PM2.5, RR=1.0328(95%CI: 1.0084,1.0578) for PM10]. For NO2 and CO, we found significant associations at lag4 in the single lag structure in Jinan [RR=1.0354 (95%CI: 1.0071, 1.0646) for NO2, RR=1.0014 (95%CI: 1.0002, 1.0025) for CO], while only CO at lag4 was significantly associated with hospitalizations for osteoporotic fractures in Qingdao [1.0038 (1.0012, 1.0063)]. Stratified analyses indicated that the associations were stronger in females and older individuals (65 + years).

CONCLUSION

This study implied that short-term exposure to TRAPs pollution was associated with an increased risk of hospitalizations for osteoporotic fractures. Female patients and patients aged 65 + years appeared to be more vulnerable to TRAPs, suggesting that poor air quality is a modifiable risk factor for osteoporotic fractures.

A new strategy for risk assessment of PM2.5-bound elements by considering the influence of wind regimes

For regulatory purposes, air pollution has been reduced to management of air quality control regions (AQCR), by inventorying pollution sources and identifying the receptors significantly affected. However, beyond being source-dependent, particulate matter can be physically and chemically altered by factors and elements of climate during transport, as they act as local environmental constraints, indirectly modulating the adverse effects of particles on the environment and human health. This case study, at an industrial site in a Brazilian coastal city - Joinville, combines different methodologies to integrate atmospheric dynamics in a strategic risk assessment approach whereby the influence of different wind regimes on environmental and health risks of exposure to PM_2.5-bound elements, are analysed. Although Joinville AQCR has been prone to stagnation/recirculation events, distinctly different horizontal wind circulation patterns indicate two airsheds within the region. The two sampling sites mirrored these two conditions and as a result we report different PM_2.5 mass concentrations, chemical profiles, geo-accumulation, and ecological and human health risks. In addition, feedback mechanisms between the airsheds seem to aggravate the air quality and its effects even under good ventilation conditions. Recognizably, the risks associated with Co, Pb, Cu, Ni, Mn, and Zn loadings were extremely high for the environment as well as being the main contributors to elevated non-carcinogenic risks. Meanwhile, higher carcinogenic risks occurred during stagnation/recirculation conditions, with Cr as the major threat. These results highlight the importance of integrating local airshed characteristics into the risk assessment of PM_2.5-bound elements since they can aggravate air pollution leading to different risks at a granular scale. This new approach to risk assessment can be employed in any city's longer-term development plan since it provides public authorities with a strategic perspective on incorporating environmental constraints into urban growth planning and development zoning regulations.

Impacts of short-term air pollution exposure on appendicitis admissions: Evidence from one of the most polluted cities in mainland China

Background

Emerging evidence indicates that air pollutants contribute to the development and progression of gastrointestinal diseases. However, there is scarce evidence of an association with appendicitis in mainland China.

Methods

In this study, Linfen city, one of the most polluted cities in mainland China, was selected as the study site to explore whether air pollutants could affect appendicitis admissions and to identify susceptible populations. Daily data on appendicitis admissions and three principal air pollutants, including inhalable particulate matter (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2) were collected in Linfen, China. The impacts of air pollutants on appendicitis were studied by using a generalized additive model (GAM) combined with the quasi-Poisson function. Stratified analyses were also performed by sex, age, and season.

Results

We observed a positive association between air pollution and appendicitis admissions. For a 10 μg/m3 increase in pollutants at lag01, the corresponding relative risks (RRs) and 95% confidence intervals (95% CIs) were 1.0179 (1.0129–1.0230) for PM10, 1.0236 (1.0184–1.0288) for SO2, and 1.0979 (1.0704–1.1262) for NO2. Males and people aged 21–39 years were more susceptible to air pollutants. Regarding seasons, the effects seemed to be stronger during the cold season, but there was no statistically significant difference between the seasonal groups.

Conclusions

Our findings indicated that short-term air pollution exposure was significantly correlated with appendicitis admissions, and active air pollution interventions should be implemented to reduce appendicitis hospitalizations, especially for males and people aged 21–39 years.

Association between PM2.5-bound metals and pediatric respiratory health in Guangzhou: An ecological study investigating source, health risk, and effect

Background

The adverse effects of 2.5-μm particulate matter (PM2.5) exposure on public health have become an increasing concern worldwide. However, epidemiological findings on the effects of PM2.5-bound metals on children's respiratory health are limited and inconsistent because PM2.5 is a complicated mixture.

Objectives

Given the vulnerability of children's respiratory system, aim to pediatric respiratory health, this study evaluated the potential sources, health risks, and acute health effects of ambient PM2.5-bound metals among children in Guangzhou, China from January 2017 to December 2019.

Methods

Potential sources of PM2.5-bound metals were detected using positive matrix factorization (PMF). A health risk assessment was conducted to investigate the inhalation risk of PM2.5-bound metals in children. The associations between PM2.5-bound metals and pediatric respiratory outpatient visits were examined with a quasi-Poisson generalized additive model (GAM).

Results

During 2017–2019, the daily mean concentrations of PM2.5 was 53.39 μg/m3, and the daily mean concentrations of PM2.5-bound metals range 0.03 ng/m3 [thorium (Th) and beryllium (Be)] from to 396.40 ng/m3 [iron (Fe)]. PM2.5-bound metals were mainly contributed by motor vehicles and street dust. PM2.5-bound arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr)(VI), nickel (Ni), and lead (Pb) were found to pose a carcinogenic risk (CR). A quasi-Poisson GAM was constructed that showed there were significant associations between PM2.5 concentrations and pediatric outpatient visits for respiratory diseases. PM2.5 was significantly associated with pediatric outpatient visits for respiratory diseases. Moreover, with a 10 μg/m3 increase in Ni, Cr(VI), Ni, and As concentrations, the corresponding pediatric outpatient visits for respiratory diseases increased by 2.89% (95% CI: 2.28–3.50%), acute upper respiratory infections (AURIs) increased by 2.74% (2.13–3.35%), influenza and pneumonia (FLU&amp;PN) increased by 23.36% (20.09–26.72%), and acute lower respiratory infections (ALRIs) increased by 16.86% (15.16–18.60%), respectively.

Conclusions

Our findings showed that PM2.5 and PM2.5-bound As, Cd, Co, Cr(VI), Ni, and Pb had adverse effects on pediatric respiratory health during the study period. New strategies are required to decrease the production of PM2.5 and PM2.5-bound metals by motor vehicles and to reduce levels of street dust to reduce children's exposure to these pollutants and thereby increase child health.

Associations between fine particulate matter and colorectal cancer: a systematic review and meta-analysis

Colorectal cancer (CRC) is the second deadliest cancer worldwide. The impact of fine particulate matter (PM2.5) on many diseases is a global concern, yet its association with CRC is unclear. This study aimed to assess the effect of PM2.5 exposure on CRC. We searched PubMed, Web of Science, and Google Scholar databases for population-based articles published before September 2022, providing risk estimates with 95% confidence intervals (CI). Among 85,743 articles, we identified 10 eligible studies across multiple countries and regions in North America and Asia. We calculated the overall risk, incidence and mortality and performed subgroup analyses according to countries and regions. The results revealed an association between PM2.5 and increased risk of CRC (total risk, 1.19 [95% CI 1.12-1.28]; incidence, OR=1.18 [95% CI 1.09-1.28]; mortality, OR=1.21 [95% CI 1.09-1.35]). The elevated risks of CRC associated with PM2.5 were different across countries and regions, at 1.34 [95% CI 1.20-1.49], 1.00 [95% CI 1.00-1.00], 1.08 [95% CI 1.06-1.10], 1.18 [95% CI 1.07-1.29], 1.01 [95% CI 0.79-1.30], in the United States, China, Taiwan, Thailand, and Hong Kong, respectively. Incidence and mortality risks were higher in North America than those in Asia. In particular, the incidence and mortality were highest in the United States (1.61 [95% CI 1.38-1.89] and 1.29 [95% CI 1.17-1.42], respectively) than those in other countries. This study is the first comprehensive meta-analysis to find a strong association between PM2.5 exposure and increased CRC risk.

Surveillance of long-term environmental elements and PM2.5 health risk assessment in Yangtze River Delta, China, from 2016 to 2020

PM2.5 metal pollution significantly harms human health. The air quality in Wuxi is poor, especially in winter, and long-term monitoring of PM2.5 elements comprising has not been performed previously. In the present study, 420 PM2.5 samples were collected from January 2016 to December 2020. Eleven elements, including Al, Mn, Ni, Cr, As, Cd, Sb, Hg, Pb, Se, and Tl, were analyzed by inductively coupled plasma mass spectrometry. The mean PM2.5 level was 56.1 ± 31.0 μg/m3, with a tendency of yearly decreasing and a significant seasonal distribution variation. The concentration of 11 elements in the PM2.5 samples was 0.38 ± 0.33 μg/m3. Al was the highest element with a range of 37.5-2148 ng/m3. Meanwhile, the spatial distribution differences were compared by literatures review. Based on the Crystal Ball model, health risks were assessed dynamically using Monte Carlo uncertainty analysis. After 10,000 simulations, the mean value of the hazard index for nine elements was 0.743, and Mn contributed the most to the hazard index among elements, with a correlation of 0.3464. The average carcinogenic risk was 1.01 × 10-5, which indicated that the non-carcinogenic and carcinogenic risks were within the acceptable range. However, considerable attention should be paid to the potential health risks associated with long-term Al, Mn, and As exposure. This study provides detailed data on local atmospheric pollution characteristics, helps identify potential risk elements, and contributes to the development of effective regional air quality management.

Effects of antibiotics and metals on lung and intestinal microbiome dysbiosis after sub-chronic lower-level exposure of air pollution in ageing rats

We investigated the effects of antibiotics, drugs, and metals on lung and intestinal microbiomes after sub-chronic exposure of low-level air pollution in ageing rats. Male 1.5-year-old Fischer 344 ageing rats were exposed to low-level traffic-related air pollution via whole-body exposure system for 3 months with/without high-efficiency particulate air (HEPA) filtration (gaseous vs. particulate matter with aerodynamic diameter of ≤2.5 µm (PM_2.5) pollution). Lung functions, antibiotics, drugs, and metals in lungs were examined and linked to lung and fecal microbiome analyses by high-throughput sequencing analysis of 16 s ribosomal (r)DNA. Rats were exposed to 8.7 μg/m³ PM_2.5, 10.1 ppb NO₂, 1.6 ppb SO₂, and 23.9 ppb O₃ in average during the study period. Air pollution exposure decreased forced vital capacity (FVC), peak expiratory flow (PEF), forced expiratory volume in 20 ms (FEV₂₀), and FEF at 25∼75% of FVC (FEF_25-75). Air pollution exposure increased antibiotics and drugs (benzotriazole, methamphetamine, methyl-1 H-benzotriazole, ketamine, ampicillin, ciprofloxacin, pentoxifylline, erythromycin, clarithromycin, ceftriaxone, penicillin G, and penicillin V) and altered metals (V, Cr, Cu, Zn, and Ba) levels in lungs. Fusobacteria and Verrucomicrobia at phylum level were increased in lung microbiome by air pollution, whereas increased alpha diversity, Bacteroidetes and Proteobacteria and decreased Firmicutes at phylum level were occurred in intestinal microbiome. Lung function decline was correlated with increasing antibiotics, drugs, and metals in lungs as well as lung and intestinal microbiome dysbiosis. The antibiotics, drugs, and Cr, Co, Ca, and Cu levels in lung were correlated with lung and intestinal microbiome dysbiosis. The lung microbiome was correlated with intestinal microbiome at several phylum and family levels after air pollution exposure. Our results revealed that antibiotics, drugs, and metals in the lung caused lung and intestinal microbiome dysbiosis in ageing rats exposed to air pollution, which may lead to lung function decline.

Pentraxin 3 mediates early inflammatory response and EMT process in human tubule epithelial cells induced by PM2.5

Pentraxin 3 (PTX3) is a multifunctional molecule that mainly expressed in response to proinflammatory stimuli under physiological and pathological conditions. It is produced in tubule epithelial cells that is involved in the innate immune response and inflammatory reactions in the kidney. However, its role in fine particulate matter (PM2.5)-induced renal injury associated with inflammation remains to be investigated. As a result of PM2.5 exposure, the levels of interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α levels were increased in HK-2 cells. Notably, the mesenchymal phenotypes with migratory abilities of HK-2 cells were found following PM2.5 exposure. The elevated expressions of PTX3 mRNA and protein in response to PM2.5 were tested by RT-PCR and Western blotting respectively. Further determinate the role of PTX3 by siRNA showed lack of PTX3 could increase IL-6 production and promote epithelial-mesenchymal transition (EMT) process, as evidenced by decreased expressions of E-cadherin, and increased expressions of N-cadherin and α-SMA in HK-2 cells following PM2.5 exposure. Our study indicates that PTX3 mediates early inflammatory response and EMT in PM2.5-exposed HK-2 cells, suggesting a counter-regulatory role of PTX3 in the early course of tubule cell injury induced by PM2.5.

Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data

Reliable long-horizon PM_2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM_2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM_2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM_2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM_2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM_2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

Allergy from perspective of environmental pollution effects: from an aspect of atopic dermatitis, immune system, and atmospheric hazards-a narrative review of current evidences

Environmental pollution has become more diversified in recent years as technologies for urbanization is increasingly more advanced. Several environmental factors such as air and water pollutants have been linked to allergic symptoms. For instance, because of industrialization for city development in many countries, polluted soil or tiny particles in the air could result in an even more hazardous environment for people to reside. Aside from the aspects of environmental issues, other newly emerging factors such as the electromagnetic field (EMF) also require further investigation. Here, in this narrative review, we focused on allergens from atmospheric and water pollution, hygiene improvement, changes in food trend, and residential environmental pollution. Current evidences regarding the association between various pollutants and the potential clinical diseases could be induced. For people with high skin exposure to air pollutants such as PM 2.5, PM 10, or sulfur dioxide, potential onset of dermatological allergic events should be alerted. The mechanisms involved in allergic diseases are being discussed and summarized. Interactions between immunological mechanisms and clinical implications could potentially provide clearer view to the association between allergic status and pollutants. Moreover, understanding the mechanistic role of allergens can raise awareness to global environment and public health.

Aerosol Impacts on Water Relations of Camphor (Cinnamomum camphora)

Major parts of anthropogenic and natural aerosols are hygroscopic and deliquesce at high humidity, particularly when depositing to leaf surfaces close to transpiring stomata. Deliquescence and subsequent salt creep may establish thin, extraordinary pathways into the stomata, which foster stomatal uptake of nutrients and water but may also cause stomatal liquid water loss by wicking. Such additional water loss is not accompanied by a wider stomatal aperture with a larger CO₂ influx and hypothetically reduces water use efficiency (WUE). Here, the possible direct impacts of aerosols on physical and physiological parameters of camphor (Cinnamomum camphora) were studied (i) in a greenhouse experiment using aerosol exclusion and (ii) in a field study in Taiwan, comparing trees at two sites with different aerosol regimes. Scanning electron microscopy (SEM) images showed that leaves grown under aerosol exclusion in filtered air (FA) were lacking the amorphous, flat areas that were abundant on leaves grown in ambient air (AA), suggesting salt crusts formed from deliquescent aerosols. Increasing vapor pressure deficit (VPD) resulted in half the Ball-Berry slope and double WUE for AA compared to FA leaves. This apparent contradiction to the wicking hypothesis may be due to the independent, overcompensating effect of stomatal closure in response to VPD, which affects AA more than FA stomata. Compared to leaves in a more polluted region in the Taiwanese Southwest, NaCl aerosols dominated the leaf surface conditions on mature camphor trees in Eastern Taiwan, while the considerably lower contact angles and the 2.5 times higher minimum epidermal conductances might have come from organic surfactants. Interpretations of SEM images from leaf surface microstructures should consider amorphous areas as possible indicators of aerosol deposition and other hygroscopic material. The amount and type of the material determine the resulting impacts on plant water relations, together with the surrounding atmosphere and ecophysiological traits.

Association between traffic-related air pollution and anxiety hospitalizations in a coastal Chinese city: are there potentially susceptible groups?

Motor vehicle exhaust emissions have become the main source of urban air pollution in China, but few studies have explored the association of short-term exposure to traffic-related air pollutants (TRAPs) with anxiety disorders. Thus, we used an overdispersed, generalized additive model (GAM) to investigate the association between TRAPs and hospital admissions (HAs) for anxiety in Qingdao, a coastal Chinese city with high vehicle ownership. In addition, stratified analyses were performed by gender, age, season and hospitalization frequency (first admission and readmission). A positive association between TRAPs and HAs for anxiety was observed. Both inhalable particulate matter (PM₁₀) and nitrogen dioxide (NO₂) showed significant effects at lag 3 in the single-day lag structure, and each 10 μg/m³ increase in the concentrations was significantly associated with increases of 0.88% [95% confidence interval (CI): 0.04%, 1.72%] for PM₁₀ and 2.74% (0.45%, 5.08%) for NO₂ on anxiety hospitalizations. For fine particulate matter (PM_2.5) and carbon monoxide (CO), the strongest effects were found at lag05 and lag04 [2.67% (0.77%, 4.62%) and 0.19% (0.04%, 0.34%), respectively] in the multiday lag structure. The estimates of PM_2.5 were relatively robust after adjusting for other pollutants in the two-pollutant model. Stratified analyses indicated that the associations were stronger in females and younger individuals (<45 in age) than in males and elderly individuals (≥45 in age). Furthermore, the effects of PM_2.5 and CO were most obvious during the cold season. Regarding hospitalization frequency, only PM_2.5 was found to have a significant effect in the first-admission group. The results showed that short-term exposure to TRAPs, especially to PM_2.5, was significantly associated with the increased risk of daily HAs for anxiety, which can help clinicians and policymakers better understand the effects of TRAPs to implement targeted interventions.

Real-time image-based air quality estimation by deep learning neural networks

Air quality profoundly impacts public health and environmental equity. Efficient and inexpensive air quality monitoring instruments could be greatly beneficial for human health and air pollution control. This study proposes an image-based deep learning model (CNN-RC) that integrates a convolutional neural network (CNN) and a regression classifier (RC) to estimate air quality at areas of interest through feature extraction from photos and feature classification into air quality levels. The models were trained and tested on datasets with different combinations of the current image, the baseline image, and HSV (hue, saturation, value) statistics for increasing model reliability and estimation accuracy. A total of 3549 hourly air quality datasets (including photos, PM2.5, PM10, and the air quality index (AQI)) collected at the Linyuan air quality monitoring station of Kaohsiung City in Taiwan constituted the case study. The main breakthrough of this study is to timely produce an accurate image-based estimation of several pollutants simultaneously by using only one single deep learning model. The test results show that estimation accuracy in terms of R² for PM_2.5, PM_10, and AQI based on daytime (nighttime) images reaches 76% (83%), 84% (84%), and 76% (74%), respectively, which demonstrates the great capability of our method. The proposed model offers a promising solution for rapid and reliable multi-pollutant estimation and classification based solely on captured images. This readily scalable measurement approach could address major gaps between air quality data acquired from expensive instruments worldwide.

Exposure assessment of PM2.5 using smart spatial interpolation on regulatory air quality stations with clustering of densely-deployed microsensors

Accurate mapping of air pollutants is essential for epidemiological studies and environmental risk assessments. Concentrations measured by air quality monitoring stations (AQMS) have primarily been used to assess the exposure of PM_2.5. However, the low coverage and amount of monitoring stations affect the errors of spatial interpolation or geostatistical estimates. In contrast to other integrated approaches developed for improved air pollution estimates, this study utilizes data from low-cost microsensors densely deployed in Taiwan to improve the popular spatial interpolation approach called inverse distance weighting (IDW). A large dataset from thousands of low-cost sensors could improve spatial interpolation by describing the distribution of PM_2.5 in detail. Therefore, this study presents a clustering-based method to assess the distribution of PM_2.5. Then, a smarter IDW is performed based on correlated observations from the selected air quality stations. The publicly available data chosen for this investigation pertained to Taiwan, which has deployed 74 monitoring stations and more than 11,000 low-cost sensors since December 2020. The results of leave-one-out cross-validation indicate that there are fewer PM_2.5 estimation errors in the developed approach than in estimations that use kriging across almost all of the months and sampled dates of 2019 and 2020, particularly those with higher PM_2.5 spatial heterogeneities. Spatial heterogeneities could result in more significant estimation errors in mainstream approaches. The root mean square error of the monthly average estimate for PM_2.5 ranged from 1.17 to 3.86 μg/m³. We also found that the clustering of one month characterizing the pattern of PM_2.5 distribution could perform well in spatial interpolations based on historical data from monitoring stations. According to the information on the openaq platform, low-cost sensors are in demand in cities and areas. This trend might pave the way for the application of the proposed approach in other areas for superior exposure assessments.

The lag effect of exposure to PM2.5 on esophageal cancer in urban-rural areas across China

Exposure to PM_2.5 pollution is a significant health concern and increases risks for cancers in China. However, the studies regarding the effect of PM_2.5 and esophageal cancer incidence (ECI) among urban-rural areas are limited. In this study, we examined the sex- and area-specific association between exposure to PM_2.5 and ECI, as well as explored the corresponding lag effects on ECI using a geographical weighted Poisson regression. We found significantly positive effect on ECI for males and females in different models, with the greatest increase of 1.44% (95% CI: 1.30%, 1.59%) and 2.42% (95% CI: 2.17%, 2.66%) in per 10 ug/m³ increase of PM_2.5 for males and females at single year lag7 and lag4 after all covariates controlled, respectively. We also found that the long-term effect of PM_2.5 on ECI was relatively stable at all moving average year lags. Moreover, rural areas had higher ECI risks for males (0.17%) and females (0.64%) with longer lag period than urban areas. In addition, higher risks for both sexes appeared in north, northwestern, and east China. The findings indicated that long-term exposure to PM_2.5 was significantly associated with increased risks for ECI, which reinforce a comprehensive understanding for ECI related to PM_2.5.

Association between short-term ambient air pollution and outpatient visits of anxiety: A hospital-based study in northwestern China

Anxiety, a common and devastating mental disorder, has raised widespread interests. The impacts of air pollution on physical health are well known, whereas few studies have explored the association of atmospheric pollution, especially short-term air pollution exposure, with the risk of anxiety disorders. In addition, there are increasing concerns in emerging evidence supporting a possible etiological link. Therefore, our aim was to evaluate the relationship between short-term exposure to atmospheric pollutants and anxiety outpatient visits in Xi'an, a city of northwestern China and a metropolis with relatively heavy air pollution. We collected the data of both daily outpatient visits and daily air pollution (SO₂, NO₂, and PM₁₀) between January 1, 2010 and January 31, 2016 (2222 days). To clarify the association between short-term ambient atmospheric pollution exposure and anxiety outpatient visits, an over-dispersed Poisson generalized additive model was applied by adjusting the day of the week and weather conditions (including temperature, humidity, sunlight hours, and rainfalls). Positive association between gaseous air pollutants (SO₂ and NO₂) and anxiety daily outpatient visits was observed. Moreover, the largest estimated values of both SO₂ and NO₂ were evidence at lag 03 (4-day moving average lag), with 10 μg/m³ increase corresponded to the increase of outpatient anxiety visits at 4.11% (95% CI: 2.15%, 6.06%) for SO₂ and 3.97% (95% CI: 1.90%, 6.06%) for NO₂. However, there was no differences in susceptibility to air pollutants between different genders as well as different ages. Taken together, short-term exposure to ambient air pollutants, especially gaseous air pollutants (NO₂ and SO₂), can be related to higher risk of anxiety outpatient visits.

A Feature Extraction and Classification Method to Forecast the PM2.5 Variation Trend Using Candlestick and Visual Geometry Group Model

Currently, the continuous change prediction of PM2.5 concentration is an air pollution research hotspot. Combining physical methods and deep learning models to divide the pollution process of PM2.5 into effective multiple types is necessary to achieve a reliable prediction of the PM2.5 value. Therefore, a candlestick chart sample generator was designed to generate the candlestick chart from the online PM2.5 continuous monitoring data of the Guilin monitoring station site. After these generated candlestick charts were analyzed through the Gaussian diffusion model, it was found that the characteristics of the physical transmission process of PM2.5 pollutants can be reflected. Based on a set three-day period, using the time linear convolution method, 2188 sets of candlestick chart data were obtained from the 2013–2018 PM2.5 concentration data. There existed 16 categories generated by unsupervised classification that met the established classification judgment standards. After the statistical analysis, it was found that the accuracy rate of the change trend of these classifications reached 99.68% during the next period. Using the candlestick chart data as the training dataset, the Visual Geometry Group (VGG) model, an improved convolutional neural network model, was used for the classification. The experimental results showed that the overall accuracy (OA) value of the candlestick chart combination classification was 96.19%, and the Kappa coefficient was 0.960. IN the VGG model, the overall accuracy was improved by 1.93%, on average, compared with the support vector machines (SVM), LeNet, and AlexNet models. According to the experimental results, using the VGG classification method to classify continuous pollution data in the form of candlestick charts can more comprehensively retain the characteristics of the physical pollution process and provide a classification basis for accurately predicting PM2.5 values. At the same time, the statistical feasibility of this method has been proved.