Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants.

The burden of rheumatoid arthritis (RA) has gradually elevated, increasing the need for medical resource redistribution. Forecasting RA patient arrivals can be helpful in managing medical resources. However, no relevant studies have been conducted yet. This study aims to construct a long short-term memory (LSTM) model, a deep learning model recently developed for novel data processing, to forecast RA patient arrivals considering meteorological factors and air pollutants and compares this model with traditional methods. Data on RA patients, meteorological factors and air pollutants from 2015 to 2022 were collected and normalized to construct moving average (MA)- and autoregressive (AR)-based and LSTM models. After data normalization, the root mean square error (RMSE) was adopted to evaluate models' forecast ability. A total of 2422 individuals were enrolled. Not using the environmental data, the RMSEs of the MA- and AR-based models' test sets are 0.131, 0.132, and 0.117 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they are 0.110, 0.130, and 0.112 for the univariate LSTM models. Considering meteorological factors and air pollutants, the RMSEs of the MA- and AR-based model test sets were 0.142, 0.303, and 0.164 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they were 0.108, 0.119, and 0.109 for the multivariable LSTM models. Our study demonstrated that LSTM models can forecast RA patient arrivals more accurately than MA- and AR-based models for datasets of all three sizes. Considering the meteorological factors and air pollutants can further improve the forecasting ability of the LSTM models. This novel method provides valuable information for medical management, the optimization of medical resource redistribution, and the alleviation of resource shortages.

Optimized air quality management based on air quality index prediction and air pollutants identification in representative cities in China.

With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

Application of Acinetobacter indicus to promote cigarette smoke particulate matter phytoremediation: removal efficiency and plant-microbe interactions.

Particulate matter (PM) is one of the most hazardous atmospheric pollutants. Several plant species show high potential to reduce air pollutants and are widely used as green belts to provide clean outdoor spaces for human well-being. However, high PM concentrations cause physiological changes and stress in plants. In this study, 11 species of Thai native perennial plants were exposed to PM generated from tobacco smoke. Wrightia religiosa (Teijsm. & Binn.) Benth. ex Kurz, Bauhinia purpurea DC. ex Walp. and Tectona grandis L.f. reduced PM effectively (which is in the typical range of 43.95 to 52.97%) compared to other plant species. In addition, the responses of perennial plants under PM stress at the proteomic level were also evaluated. Proteomic analysis of these three plant species showed that plants respond negatively to high PM concentrations, such as reducing several photosynthetic-related proteins and increasing plant stress response proteins. To improve PM phytoremediation efficiency and reduce plant stress from PM, perennial plant-microbe interactions were investigated. W. religiosa was inoculated with Acinetobacter indicus PS1, and high biosurfactant-producing strains clearly showed a higher PM removal efficiency than non-inoculated plants (9.48, 9.5 and 12.6% for PM1.0, PM2.5 and PM10, respectively). Inoculating W. religiosa with A. indicus PS1 maintained chlorophyll a and b concentrations. Moreover, the malondialdehyde (MDA) concentration of W. religiosa inoculated with A. indicus PS1 was lower than that of non-inoculated W. religiosa. The leaf wax content (µg/cm2) and biosurfactant (µg/cm2) of W. religiosa inoculated with A. indicus PS1 were also higher than those of non-inoculated W. religiosa. This study clearly showed that inoculating plants with A. indicus PS1 can help plants remediate PM and improve their PM stress response.

Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN) in World Trade Center exposed firefighters: a case-control observational study protocol.

BACKGROUND: Particulate matter exposure (PM) is a cause of aerodigestive disease globally. The destruction of the World Trade Center (WTC) exposed first responders and inhabitants of New York City to WTC-PM and caused obstructive airways disease (OAD), gastroesophageal reflux disease (GERD) and Barrett's Esophagus (BE). GERD not only diminishes health-related quality of life but also gives rise to complications that extend beyond the scope of BE. GERD can incite or exacerbate allergies, sinusitis, bronchitis, and asthma. Disease features of the aerodigestive axis can overlap, often necessitating more invasive diagnostic testing and treatment modalities. This presents a need to develop novel non-invasive biomarkers of GERD, BE, airway hyperreactivity (AHR), treatment efficacy, and severity of symptoms. METHODS: Our observational case-cohort study will leverage the longitudinally phenotyped Fire Department of New York (FDNY)-WTC exposed cohort to identify Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN). Our study population consists of n = 4,192 individuals from which we have randomly selected a sub-cohort control group (n = 837). We will then recruit subgroups of i. AHR only ii. GERD only iii. BE iv. GERD/BE and AHR overlap or v. No GERD or AHR, from the sub-cohort control group. We will then phenotype and examine non-invasive biomarkers of these subgroups to identify under-diagnosis and/or treatment efficacy. The findings may further contribute to the development of future biologically plausible therapies, ultimately enhance patient care and quality of life. DISCUSSION: Although many studies have suggested interdependence between airway and digestive diseases, the causative factors and specific mechanisms remain unclear. The detection of the disease is further complicated by the invasiveness of conventional GERD diagnosis procedures and the limited availability of disease-specific biomarkers. The management of reflux is important, as it directly increases risk of cancer and negatively impacts quality of life. Therefore, it is vital to develop novel noninvasive disease markers that can effectively phenotype, facilitate early diagnosis of premalignant disease and identify potential therapeutic targets to improve patient care. TRIAL REGISTRATION: Name of Primary Registry: "Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BADBURN)". Trial Identifying Number: NCT05216133 . Date of Registration: January 31, 2022.

Multi-pollutant exposure profiles associated with breast cancer risk: A Bayesian profile regression analysis in the French E3N cohort.

BACKGROUND: Human exposure to air pollution involves complex mixtures of multiple correlated air pollutants. To date, very few studies have assessed the combined effects of exposure to multiple air pollutants on breast cancer (BC) risk. OBJECTIVES: We aimed to assess the association between combined exposures to multiple air pollutants and breast cancer risk. METHODS: The study was based on a case-control study nested within the French E3N cohort (5222 incident BC cases/5222 matched controls). For each woman, the average of the mean annual exposure to eight pollutants (benzo(a)oyrene, cadmium, dioxins, polychlorinated biphenyls (PCB153), nitrogen dioxide (NO2), ozone, particulate matter and fine particles (PMs)) was estimated from cohort inclusion in 1990 to the index date. We used the Bayesian Profile Regression (BPR) model, which groups individuals according to their exposure and risk levels, and assigns a risk to each cluster identified. The model was adjusted on a combination of matching variables and confounders to better consider the design of the nested case-control study. Odds ratios (OR) and their 95 % credible intervals (CrI) were estimated. RESULTS: Among the 21 clusters identified, the cluster characterised by low exposures to all pollutants, except ozone, was taken as reference. A consistent increase in BC risk compared to the reference cluster was observed for 3 clusters: cluster 9 (OR=1.61; CrI=1.13,2.26), cluster 16 (OR=1.59; CrI=1.10,2.30) and cluster 15 (OR=1.38; CrI=1.00,1.88) characterised by high levels of NO2, PMs and PCB153. The other clusters showed no consistent association with BC. DISCUSSION: This is the first study assessing the effect of exposure to a mixture of eight air pollutants on BC risk, using the BPR approach. Overall, results showed evidence of a positive joint effect of exposure to high levels to most pollutants, particularly high for NO2, PMs and PCB153, on the risk of BC.

Correlation of time trends of air pollutants, greenspaces and tracheal, bronchus and lung cancer incidence and mortality among the adults in United States.

Background: Tracheal, Bronchus, and Lung (TBL) cancer continues to represent the majority of cancer-related incidence and mortality in United States (U.S.). While air pollutants are considered essential risk factors, both global and national average concentrations of major harmful air pollutants have significantly decreased over the decades. Green space may have a beneficial effect on human health. Methods: We obtained data on national and state-level burden of TBL cancer, the annual average concentration of main air pollutants, and levels of green spaces in 2007, 2013, and 2019. According to generalized estimating equation (GEE), we examine the associations among incidence and mortality of TBL cancer, air pollutants, and greenspaces, represented by the Normalized Difference Vegetation Index (NDVI) in different age groups with models adjusted with meteorological, and socio-demographic. We observed additional effects of the interaction between the NDVI, Ozone, PM2.5, and other factors, which helped us to interpret and understand our results. Also, we collated states that witnessed net increments in forest coverage and conducted the same analysis separately. Results: In our analysis, the majority of associations between NDVI and air pollutants with TBL cancer remained significantly positive, particularly noticeable among individuals aged 20 to 54. However, our findings did not explore air pollution as a potential mediator between greenspace exposure and TBL cancer. While the associations of PM2.5 with TBL cancer remained positive, the other four pollutants showed positive but statistically insignificant associations. Our interaction analysis yielded that there were positive associations between NDVI and ozone, PM2.5, and tobacco use. Max NDVI acts as a protective factor along with high HDI. Additionally, PM2.5 and HDI also showed a negative association. In 18 states with more forest, NDVI acts as a protective factor along with higher health care coverage, better health status, and participation in physical activities. Conclusion: In the state-level of U.S., the effects of total greenspace with TBL cancer are mixed and could be modified by various socio-economic factors. PM2.5 has a direct correlation with TBL cancer and the effects can be influenced by underlying socioeconomic conditions.

"Occupational exposure to charcoal smoke and dust, a major risk factor for COPD: a multiregional cross-sectional study in the Democratic Republic of Congo".

BACKGROUND: Occupational exposure to charcoal smoke and dust is a threat to workers respiratory system. RESEARCH QUESTION: What is the prevalence of chronic obstructive pulmonary disease (COPD) in charcoal workers as compared to farmers in rural areas of Democratic Republic of Congo (DRC)? STUDY DESIGN: This cross-sectional, comparative, and multi-site study was performed in the charcoal-producing provinces of South-Western DRC. METHODS: We randomly included charcoal workers and compared them to farmers (18 to 70 years old). Air quality indexes, anthropometric, physical activity, sociodemographic characteristics, and related medical events data were recorded. A Lung function questionnaire was used to assess respiratory symptoms (RS) and spirometry was performed. COPD was defined as the presence of RS for more than 3 months with a FEV1/FVC ratio below the lower limit of normal. The prevalence of COPD was calculated, and logistic regression was used to identify COPD-associated factors. RESULTS: We included 485 subjects between August 2020 and July 2021. Charcoal Producers (CP, n = 229) were compared with farmers (n = 118), and charcoal saleswomen (CS, n = 72) were compared to vegetable saleswomen (VS, n = 66). Respective groups were similar in age, job seniority, height, and weight. The air was more polluted at charcoal workplaces. The prevalence of COPD was higher in CP than in farmers (39.7% vs 14.4%; P < 0.0001), and in CS compared to VS (40.3% vs 13.6%; P < 0.0001). Being a charcoal worker was independently associated with COPD in the CP and farmers group: adjusted OR, 3.54 (95% CI, 1.94-6.46), and, in the saleswomen group: 7.85 (95% CI, 2.85-21.5), where it was also independently associated with young age: 0.85 (0.80-0.93) and monthly income: 0.88 (0.83-0.96). INTERPRETATION: In rural areas of DRC, producing or selling charcoal is associated with a higher risk of COPD.

Predictions of PM2.5 using air pollutants and meteorological factors with COVID-19 cases in Malaysia and Indonesia: a comparative study using feature selection and robust regression.

The study examines the relationship between air quality, meteorological factors, and COVID-19 cases in Cheras, Kuala Lumpur, and Kelapa Gading, North Jakarta. Analyzing data from 2020 and 2021, the research found notable correlations: COVID-19 cases in Cheras were positively associated with relative humidity (RH) and carbon monoxide (CO) but negatively with ozone (O3) and RH in different years. In Kelapa Gading, COVID-19 cases were positively correlated with pollutants like sulfur dioxide (SO2) and CO, while ambient temperature (AT) showed a negative correlation. The enforcement of social restrictions notably reduced air pollution, affecting COVID-19 spread. Predictive models for PM2.5 levels using robust regression techniques showed strong performance in Kuala Lumpur (R² > 0.9) but exhibited overfitting tendencies in Jakarta, suggesting the need for a longer study period for more accurate results.

Dataset of air pollutants (PM2.5, PM10, CO) concentrations in the export processing area of Dhaka, Bangladesh.

This study presents a valuable dataset on air quality in the densely populated Dhaka Export Processing Zone (DEPZ) of Bangladesh. It included a dataset of Particulate Matter (PM2.5, PM10) and CO concentrations with Air Quality Index (AQI) values. PM data was collected 24h, and CO data was collected 8h monthly from 2019 to 2023 using respirable dust sampler APS-113NL for PM2.5, APS-113BL for PM10, and LUTRON AQ9901SD Air Quality Monitor Data Logger used to measure CO concentration data. Data sampling locations are selected based on population density, and employment data for DEPZ is also included, highlighting a potential rise in population density. This article also forecasted pollutant concentrations, AQI values, and health hazards associated with air pollutants using the Auto Regressive Moving Average (ARIMA) model. The performance of the ARIMA model was also measured using root mean squared error (RMSE) and mean absolute error (MAE). However, this can be used to raise awareness among the public about the health hazards associated with air pollution and encourage them to take measures to reduce their exposure to air pollutants. In addition, this data can be instrumental for researchers and policymakers to assess air pollution risks, develop control strategies, and improve air quality in the DEPZ.

Causal association between air pollution and allergic rhinitis, asthma: a Mendelian randomization study.

Backgrounds: Observational studies suggest that air pollutants, including particulate matter and nitrogen compounds, could elevate asthma and allergic rhinitis health risks. Nevertheless, the exact nature of the causal relationship between air pollution and asthma and allergic rhinitis remains unknown. This study utilizes the Mendelian randomization (MR) technique to explore the potential causal links between air pollution components (PM2.5, PM2.5-10, PM10, NO2, and nitrogen dioxide) and the incidence of allergic rhinitis and asthma. Methods: A MR study utilized summary statistics from GWAS that are publicly accessible. The inverse variance weighting (IVW) approach served as the foundational analysis technique. To ensure robustness, supplementary methodologies such as the weighted median, MR-Egger regression, simple mode, and weighted model were also applied. Heterogeneity was evaluated using Cochran's Q test, and the presence of pleiotropy was determined through MR-Egger regression. The MR-PRESSO test was employed for outlier detection, and the analysis's sensitivity was scrutinized via a leave-one-out strategy. Results: The IVW technique showed a strong correlation between PM10 and asthma (OR = 0.625, 95% CI = 0.396-0.988, p = 0.044). No significant associations were found between asthma and other air pollutants such as PM2.5, PM2.5-10, NO2, or nitrogen dioxide. Similarly, allergic rhinitis showed no causal relationships with any studied air pollution metrics. Pleiotropy was absent in the findings. Sensitivity analyses, employing the leave-one-out method, confirmed the stability of these results, unaffected by individual single nucleotide polymorphisms (SNPs). Conclusion: This Mendelian randomization study establishes a causal link between PM10 exposure and asthma, suggesting that interventions to reduce air pollution may decelerate the adverse progression of asthma.

Respiratory conditions and health symptoms associated with air pollution amongst children aged six years and below in Melusi Informal Settlement, Tshwane Metropolitan Municipality, South Africa: a cross-sectional study.

BACKGROUND: Respiratory conditions and health symptoms associated with air pollution in children are a major public health concern, as their immune systems and lungs are not yet fully developed. This study aimed to assess self-reported respiratory conditions and health symptoms associated with air pollution sources amongst children aged six years and below in Melusi informal settlement, Tshwane Metropolitan Municipality, South Africa. METHODS: With a quantitative cross-sectional study design, parents/caregivers of children aged six years and below (n = 300) from eight Early Childhood Development Centres were invited to participate in the study. This study employed complete sampling, and data was collected using the modified International Study of Asthma and Allergies in Children. The chi-square and multiple logistic regression models were used to analyze data, with p < 0.05 in the adjusted odds ratios considered as being statistically significant. RESULTS: Three models were run to examine the predictors of wheezing in the past 12 months, dry cough, and itchy-watery eyes. The model for asthma was excluded, as only seven participants reported having asthma. Wheeze in the past 12 months was associated with participants living in the area for more than three years (OR 2.96 95%CI: 1.011-8.674). Furthermore, having a dog in the house in the past 12 months was associated with wheeze in the past 12 months (OR 5.98 95%CI: 2.107-16.967). There was an association between duration of stay in a residence and dry cough prevalence (OR 5.63 95%CI: 2.175-14.584). Trucks always or frequently passing near homes was associated with itchy-watery eyes (OR 3.27 95%CI: 1.358-7.889). 59% (59%) of participants perceived the indoor air quality in their homes to be good, while 6% perceived it as poor. In contrast, 36% of participants perceived the outdoor air quality to be good, and 19.7% perceived it as poor. CONCLUSION: The association between perceived air pollution exposure, self-reported respiratory conditions, and health symptoms amongst children is complex. Further research is required to better understand the multifaceted nature of air pollution and its impact on the health of children.

Non-linear association between air pollutants and secondary sensitive skin in acne patients.

BACKGROUND: There is a growing number of patients suffering from sensitive skin secondary to acne, but its prevalence and influencing factors are not yet well-understood. OBJECTIVE: The aim of this study is to investigate the nonlinear relationship between air pollutants and secondary sensitive skin in acne patients. METHODS: A cross-sectional study comprising 4325 acne outpatients in China was carried out between September 2021 and December 2022, employing a simple random sampling approach. Air pollutants data was derived from the nearest air quality monitoring station corresponding to the subjects' residential locations. Furthermore, socio-economic characteristics, biological attributes, and lifestyle data of patients were acquired via questionnaire surveys. The data were subsequently analyzed utilizing the XGBoost machine learning model. RESULTS: A nonlinear relationship has been observed between secondary sensitive skin in acne patients and various factors, including particulate matter (PM2.5), inhalable particulate matter (PM10), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), the severity of depression, different levels of exercise intensity, acne grading, frequency of sunscreen application, gender, and age. CONCLUSION: The occurrence of secondary sensitive skin in acne patients be mitigated through the implementation of measures such as the control of air pollutant emissions, regulation of negative emotions, and improvement of personal lifestyle.

The Impact of the Reduction in Environmental Pollution during COVID-19 Lockdown on Healthy Individuals.

The lockdown imposed to combat the COVID-19 pandemic produced a historic fall in air pollution in cities like Barcelona. This exceptional situation offered a unique context in which to examine the effects of air pollutants on human health. The present study aims to determine and compare the oxidative stress biomarkers Th1/Th2 and inflammatory-related cytokines in healthy individuals first during lockdown and then six months after the easing of the restrictions on mobility. A prospective study of a representative sample of 58 healthy, non-smoking adults was carried out. During lockdown and six months post-easing of restrictions, blood samples were drawn to measure the percentage of eosinophils, levels of Th1/Th2 and inflammatory-related cytokines assessed by a multiplex assay (BioRad Laboratories S.A., Marnes-la-Coquette, France), and levels of 8-isoprostane, glutathione peroxidase activity, and myeloperoxidase (Cayman Chemical Co., Ann Arbor, MI, USA), to assess their value as biomarkers of oxidative stress. Six months after easing mobility restrictions, increases in the levels of 8-isoprostane (p < 0.0001), IL-1ß (p = 0.0013), IL-1ra (p = 0.0110), IL-4 (p < 0.0001), IL-13 (p < 0.0001), G-CSF (p = 0.0007), and CCL3 (p < 0.0001) were recorded, along with reductions in glutathione peroxidase (p < 0.0001), IFN-γ (p = 0.0145), TNFα (p < 0.0001), IP-10 (p < 0.0001), IL-2 (p < 0.0001), IL-7 (p < 0.0001), basic FGF (p < 0.0001), CCL4 (p < 0.0001), and CCL5 (p < 0.0001). No significant differences were observed in the rest of the biomarkers analyzed. The reduction in environmental pollution during the COVID-19 lockdown significantly lowered the levels of oxidative stress, systemic inflammation, and Th2-related cytokines in healthy people.

A comprehensive emission inventory of air pollutants from municipal solid waste incineration in China's megacity, Beijing based on the field measurements.

The rising of municipal solid waste incineration (MSWI), constituting 5 % of NOx emissions in Beijing, poses a significant challenge to improving air quality. This study establishes a comprehensive historical inventory of air pollutants (APs) emitted from MSWI plants between 2004 and 2023. The inventory was developed using both the continuous emissions monitoring systems (CEMS)-based method and the EF (emission factors) -based method, incorporating detailed plant-level activity data and localized EF derived from field measurements. These include data from CEMS and manual monitoring. Analysis of CEMS data reveals high compliance rates with emission limits for MSW in Beijing, with 99.9 %, 99.5 %, 99.8 %, 98.7 %, and 99.5 % of units meeting standards for PM, SO2, NOx, CO and HCl, respectively. This suggests effective implementation of emission standards in Beijing, although further strengthening of policies, particularly for CO emissions, is warranted. Overall, total AP emissions have increased annually largely attributed to measures implemented for DeSOx, DeNOx, and DePM since 1998. Most MSWI facilities are located in suburban areas rather than urban cores. Emissions of SO2, HCl, CO, Hg, Cd + Ti, other metals, dioxins, VOCs, and NH3 exhibit a spatially homogeneous distribution at the district level, while PM and NOx emissions demonstrate heterogeneity. Scenario analysis underscores the importance of continuous improvement and upgrading of advanced air pollution control devices. This study contributes a methodological framework for estimating emissions, reducing uncertainties, and informing policy-making to mitigate APs emissions in megacities. It serves as a valuable reference for similar cities grappling with air quality challenges.

Exploring potential causal links between air pollutants and congenital malformations: A two-sample Mendelian Randomization study.

Observational studies have suggested an association between air pollutants and congenital malformations; however, conclusions are inconsistent and the causal associations have not been elucidated. In this study, based on publicly available genetic data, a two-sample Mendelian randomization (MR) was applied to explore the associations between particulate matter 2.5 (PM2.5), NOX, NO2 levels and 11 congenital malformations. Inverse variance weighted (IVW), MR-Egger and weighted median were used as analytical methods, with IVW being the main method. A series of sensitivity analyses were used to verify the robustness of the results. For significant associations, multivariable MR (MVMR) was utilized to explore possible mediating effects. The IVW results showed that PM2.5 was associated with congenital malformations of digestive system (OR = 7.72, 95 %CI = 2.33-25.54, P = 8.11E-4) and multiple systems (OR = 8.63, 95 %CI = 1.02-73.43, P = 0.048) risks; NOX was associated with circulatory system (OR = 4.65, 95 %CI = 1.15-18.86, P = 0.031) and cardiac septal defects (OR = 14.09, 95 %CI = 1.62-122.59, P = 0.017) risks; NO2 was correlated with digestive system (OR = 27.12, 95 %CI = 1.81-407.07, P = 0.017) and cardiac septal defects (OR = 22.57, 95 %CI = 2.50-203.45, P = 0.005) risks. Further MVMR analyses suggest that there may be interactions in the effects of these air pollutants on congenital malformations. In conclusion, this study demonstrated a causal association between air pollution and congenital malformations from a genetic perspective.

Airborne Influenza Virus in Daycare Centers.

In this study, we investigated the concentration of airborne influenza virus in daycare centers and influencing factors, such as common cold prevalence, air pollutants, and meteorological factors. A total of 209 air samples were collected from daycare centers in Kaohsiung and the influenza virus was analyzed using real-time quantitative polymerase chain reaction. Air pollutants and metrological factors were measured using real-time monitoring equipment. Winter had the highest positive rates of airborne influenza virus and the highest prevalence of the common cold, followed by summer and autumn. The concentration of CO was significantly positively correlated with airborne influenza virus. Daycare center A, with natural ventilation and air condition systems, had a higher concentration of airborne influenza A virus, airborne fungi, and airborne bacteria, as well as a higher prevalence of the common cold, than daycare center B, with a mechanical ventilation system and air purifiers, while the concentrations of CO2, CO, and UFPs in daycare center A were lower than those in daycare center B. We successfully detected airborne influenza virus in daycare centers, demonstrating that aerosol sampling for influenza can provide novel epidemiological insights and inform the management of influenza in daycare centers.

Perception matters: How air pollution influences life satisfaction in China.

Academic studies on environmental pollution have convincingly acknowledged the salient relevance of ambient pollutant emissions on individual life satisfaction. However, an understanding of how the different dimensions of air pollution influence public self-assessment of their living condition is required. This research investigates whether objective pollutant emissions and subjective evaluation influence individual life satisfaction. The findings were based on data from the China Environment Yearbook and China Social Survey in 2019. The multi-level linear regression model found that air pollutants emissions, including particulate matter (PM) and sulfur dioxide (SO2), failed to explain the variations in public life satisfaction because of the lag effect of public perception. A significant nexus between perceived air pollution and public life satisfaction was observed at a significance level of 0.01. Specially, as the perceived air pollution by the public increased by one-point, life satisfaction decreased by 0.22 on a scale of 1-10, on average. Heterogeneous analysis based on income further suggested the salient negative effect of PM emissions on life satisfaction only occurred in the high-income group. The findings were robust after various methodological analyses. This study has theoretical implications for understanding the effects of air pollution on public subjective perception and provides guidance for how the government can manage the relationship between environmental governance and life satisfaction.

Air pollutants, seasonal influenza, and acute otitis media in children: a population-based analysis using 22-year hospitalization data.

BACKGROUND: Acute otitis media (AOM) is a prevalent childhood acute illness, with 13.6 million pediatric office visits annually, often stemming from upper respiratory tract infections (URI) and affected by environmental factors like air pollution and cold seasons. METHODS: Herein, we made use of territory-wide hospitalization data to investigate the relationships between meteorological factors, air pollutants, influenza infection, and AOM for children observed from 1998 to 2019 in Hong Kong. Quasi-Poisson generalized additive model, combined with a distributed-lag non-linear model, was employed to examine the relationship between weekly AOM admissions in children and weekly influenza-like illness-positive (ILI +) rates, as well as air pollutants (i.e., oxidant gases, sulfur dioxide, and fine particulate matter), while accounting for meteorological variations. RESULTS: There were 21,224 hospital admissions due to AOM for children aged ≤ 15 years throughout a 22-year period. The cumulative adjusted relative risks (ARR) of AOM were 1.15 (95% CI, 1.04-1.28) and 1.07 (95% CI, 0.97-1.18) at the 95th percentile concentration of oxidant gases (65.9 ppm) and fine particulate matter (62.2 µg/m3) respectively, with reference set to their medians of concentration. The ARRs exhibited a monotone increasing trend for all-type and type-specific ILI + rates. Setting the reference to zero, the cumulative ARRs of AOM rose to 1.42 (95% CI, 1.29-1.56) at the 95th percentile of ILI + Total rate, and to 1.07 (95% CI, 1.01-1.14), 1.19 (95% CI, 1.11-1.27), and 1.22 (95% CI, 1.13-1.32) for ILI + A/H1N1, A/H3N2, and B, respectively. CONCLUSIONS: Our findings suggested that policy on air pollution control and influenza vaccination for children need to be implemented, which might have significant implications for preventing AOM in children.

The association between the scarlet fever and meteorological factors, air pollutants and their interactions in children in northwest China.

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 µm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.

Enhancing the health benefits of air quality improvement: a comparative study across diverse scenarios.

In many studies, linear methods were used to calculate health benefits of air quality improvement, but the relationship between air pollutants and diseases may be complex and nonlinear. In addition, previous studies using reference number as average number of diseases may overestimate the health benefits. Therefore, the nonlinear model estimation and resetting of the reference number were very important. Hospital admission data for coronary heart disease (CHD), meteorological data, and air pollutant data of Zibo City from 2015 to 2019 were collected. The generalized additive model (GAM) was used to explore the association between air pollutants and hospital admission for CHD, and to evaluate the effects on health benefits under different reference number settings. A total of 21,105 hospitalized cases for CHD were reported in Zibo during the study period. The results of the GAM showed there was a log-linear exposure-response relationship between O3 and hospital admissions for CHD, with RR (relative risk) of 1.0143 (95% CI: 1.0047 ~ 1.0239). There were log-nonlinear exposure-response relationships between PM10, PM2.5, SO2, and hospital admissions for CHD. With the increase of pollutants concentrations, the risk for hospital admission showed a trend of increasing first and then decreasing. Compared with the average hospital admissions as the reference number, health benefits calculated by hospital admissions predicted by the GAM model yielded lower. Using the World Health Organization air quality guidelines as reference, attributable fractions of O3, PM10, and PM2.5 were 1.97% (95% CI: 0.63 ~ 3.40%), 11.82% (95% CI: 8.60 ~ 15.24%), and 11.82% (95% CI: 8.79 ~ 15.04%), respectively. When quantifying health benefits brought by improving air quality, corresponding calculation methods should first be determined according to the exposure-response relationships between air pollutants and outcomes. Then, applying the average hospital admissions as reference number may overestimate health benefits resulting from improved air quality.