Removal of particulate matter and dissolved organic matter from sedimentation sludge water during pre-sedimentation process: Performances and mechanisms.

Sedimentation sludge water (SSW), a prominent constituent of wastewater from drinking water treatment plants, has received limited attention in terms of its treatment and utilization likely due to the perceived difficulties associated with managing SSW sludge. This study comprehensively evaluated the water quality of SSW by comparing it to a well-documented wastewater (filter backwash water (FBW)). Furthermore, it investigated the pollutant variations in the SSW during pre-sedimentation process, probed the underlying reaction mechanism, and explored the feasibility of employing a pilot-scale coagulation-sedimentation process for SSW treatment. The levels of most water quality parameters were generally comparable between SSW and FBW. During the pre-sedimentation of SSW, significant removal of turbidity, bacterial counts, and dissolved organic matter (DOM) was observed. The characterization of DOM components, molecular weight distributions, and optical properties revealed that the macromolecular proteinaceous biopolymers and humic acids were preferentially removed. The characterization of particulates indicated that high surface energy, zeta potential, and bridging/adsorption/sedimentation/coagulation capacities in aluminum residuals of SSW, underscoring its potential as a coagulant and promoting the generation and sedimentation of inorganic-organic complexes. The coagulation-sedimentation process could effectively remove pollutants from low-turbidity SSW ([turbidity]0 < 15 NTU). These findings provide valuable insights into the water quality dynamics of SSW during the pre-sedimentation process, facilitating the development of SSW quality management and enhancing its reuse rate.

On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles.

Thermodynamic modeling is still the most widely used method to characterize aerosol acidity, a critical physicochemical property of atmospheric aerosols. However, it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamic models are employed to estimate the acidity of coarse particles. In this work, field measurements were conducted at a coastal city in northern China across three seasons, and covered wide ranges of temperature, relative humidity and NH3 concentrations. We examined the performance of different modes of ISORROPIA-II (a widely used aerosol thermodynamic model) in estimating aerosol acidity of coarse and fine particles. The M0 mode, which incorporates gas-phase data and runs the model in the forward mode, provided reasonable estimation of aerosol acidity for coarse and fine particles. Compared to M0, the M1 mode, which runs the model in the forward mode but does not include gas-phase data, may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles; M2, which runs the model in the reverse mode, results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity calculations. However, M1 significantly underestimates liquid water contents for both fine and coarse particles, while M2 provides reliable estimation of liquid water contents. In summary, our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity, and thus may help improve our understanding of acidity of coarse particles.

Combustion aerosols and suspended dust with controlled processes in Lhasa: Elemental analysis and size distribution characteristics.

To explore air contamination resulting from special biomass combustion and suspended dust in Lhasa, the present study focused on the size distribution and chemical characteristics of particulate matter (PM) emission resulting from 7 types of non-fossil pollution sources. We investigated the concentration and size distribution of trace elements from 7 pollution sources collected in Lhasa. Combining Lhasa's atmospheric particulate matter data, enrichment factors (EFs) have been calculated to examine the potential impact of those pollution sources on the atmosphere quality of Lhasa. The highest mass concentration of total elements of biomass combustion appeared at PM0.4, and the second highest concentration existed in the size fraction 0.4-1 µm; the higher proportion (12 %) of toxic metals was produced by biomass combustion. The elemental composition of suspended dust and atmospheric particulate matter was close (except for As and Cd); the highest concentration of elements was all noted in PM2.5-10 (PM3-10). Potassium was found to be one of the main biomass markers. The proportion of Cu in suspended dust is significantly lower than that of atmospheric particulate matter (0.53 % and 3.75 %), which indicates that there are other anthropogenic sources. The EFs analysis showed that the Cr, Cu, Zn, and Pb produced by biomass combustion were highly enriched (EFs > 100) in all particle sizes. The EFs of most trace elements increased with decreasing particle size, indicating the greater influence of humanfactors on smaller particles.

Emission control status and future perspectives of diesel trucks in China.

Chinese diesel trucks are the main contributors to NOx and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment.

Exposure to fine particulate matter in the New York City subway system during home-work commute.

The New York City (NYC) subway system accommodates 5.5 million daily commuters, and the environment within the subway is known to have high concentrations of fine particulate matter (PM2.5) pollution. Naturally, subway air pollution varies among individuals according to their mobility patterns, introducing the possibility of inequality in PM2.5 exposure. This study aims to evaluate individual and community-level exposure to subway PM2.5. We simulated the intracity home-to-work trip patterns using the Longitudinal Employer-Household Dynamics (LEHD) records of 3.1 million working commuters across 34,169 census blocks in four boroughs (Manhattan, Brooklyn, Queens, and the Bronx) of NYC. We incorporated the on-platform and on-train measured PM2.5 concentration data for the entire subway system. The mean underground platform concentration in the city was 139 µg/m3 with a standard deviation of 25 µg/m3, while the on-train concentration when underground was 99 µg/m3 with a standard deviation of 21 µg/m3. Using a network model, we determined the exposure of individual commuters during their daily home-work trips. We quantified the mean per capita exposure at the census block level by considering the proportion of workers within the blocks who rely on the subway for their work commute. Results indicate statistically significant weak positive correlation between elevated subway PM2.5 exposure and economically disadvantaged and racial minority groups.

Examining air pollution exposure dynamics in disadvantaged communities through high-resolution mapping.

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO2 and PM2.5 to that of O3. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO2 and PM2.5 reductions and the advantaged neighborhoods experienced greatest rising O3 concentrations. Further, day-to-day exposure variations decreased for NO2 and O3. The disparity in NO2 exposure decreased, while it persisted for O3. In addition, PM2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.

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.

Spatial and Temporal Distribution Characteristics and Cytotoxicity of Atmospheric PM2.5 in the Main Urban Area of Lanzhou City.

PM2.5, as one of the most harmful pollutant in the atmospheric environment and population health, has received much attention. We monitored PM2.5 levels at five sampling sites in the Lanzhou City and collected PM2.5 particles from two representative sites for cytotoxicity experiment. The cytotoxicity of PM2.5 samples on A549 cells and migration ability of the cells were respectively detected by Cell Counting kit-8 (CCK-8) assay and scratch assay. We detected the levels of cellular inflammatory factors and oxidative damage-related biochemical indexes. RT-qPCR was used to detect the mRNA levels of NF-κB and epithelial-mesenchymal transition (EMT)-related genes. We found that the Lanlian Hotel station had the highest PM2.5 annual average concentration. The annual average concentration change curve of PM2.5 showed a roughly "U"-shaped distribution during the whole sampling period. The cytotoxicity experiment showed the viability of A549 cells decreased and the scratch healing rate increased in the 200 and 400 µg/mL PM2.5-treated groups. We also found 400 µg/mL PM2.5 induced changes in the mRNA levels of NF-κB and EMT-related genes, the mRNA levels of IKK-α, NIK, and NF-κB in the 400 µg/mL PM2.5 group were higher than those in the control group. The mRNA levels of E-cadherin decreased and α-SMA increased in the 400 µg/mL PM2.5 groups, and the mRNA levels of Fibronectin increased in the 400 µg/mL PM2.5 groups. Moreover, we found hydroxyl radical scavenging ability and T-AOC levels were lower, and LPO levels were higher in the 200 and 400 µg/mL PM2.5 groups, and the SOD activity of cells in the 400 µg/mL PM2.5 group decreased. And compared with the control group, the levels of TNF-α were higher in the 200 and 400 µg/mL PM2.5 groups and the levels of IL-1 were higher in the 400 µg/mL PM2.5 group. The results indicated that the cytotoxicity of atmospheric PM2.5 was related to oxidative damage, inflammatory response, NF-κB activity and EMT.

The effects of ambient particulate matter air pollution on platelets and hemostasis.

Introduction: Elevated ambient pollution exposure is potentially linked to thromboembolism. However, the mechanisms by which particulate matter (PM) interferes with the balance of hemostatic system remain unclear. This study investigates PM-mediated hemostatic changes in individuals across unique seasonal variations of ambient pollution. Methods: This prospective study was conducted between February and July 2020 during alterations in ambient pollution in Chiang Mai, Thailand. Blood tests from 30 healthy subjects were assessed at four-week intervals, four times in total. Various coagulation tests, including prothrombin time (PT), activated partial thromboplastin time (aPTT), von Willebrand factor (vWF), platelet count, and platelet functions, were evaluated. A mixed-effects model was used to analyze the impact of high PM2.5 and PM10 on hemostatic parameters. Results: Thirty male subjects with mean age of 38.9 ± 8.2 years, were included. High levels of PM2.5 and PM10 were significantly associated with PT shortening, with no such effect observed in aPTT. PM2.5 and PM10 values also positively correlated with vWF function, while vWF antigen levels remained unchanged. Soluble P-selectin showed a strong positive association with PM2.5 and PM10 levels. Platelet function analysis revealed no correlation with PM values. Conclusion: Short-term exposure to elevated PM2.5 and PM10 concentrations was linked to shortened PT and enhanced vWF function in healthy individuals. Exploring the impact of these changes on clinically relevant thrombosis is crucial. Additional studies on the pathogenesis of pollution-related thrombosis are warranted for maintaining good health.

The Association between Exposure to Fine Particulate Air Pollution and the Trajectory of Internalizing and Externalizing Behaviors during Late Childhood and Early Adolescence: Evidence from the Adolescent Brain Cognitive Development (ABCD) Study.

BACKGROUND: Exposure to high levels of fine particulate matter (PM) with aerodynamic diameter ≤2.5µm (PM2.5) via air pollution may be a risk factor for psychiatric disorders during adulthood. Yet few studies have examined associations between exposure and the trajectory of symptoms across late childhood and early adolescence. OBJECTIVE: The current study evaluated whether PM2.5 exposure at 9-11 y of age affects both concurrent symptoms as well as the longitudinal trajectory of internalizing and externalizing behaviors across the following 3 y. This issue was examined using multiple measures of exposure and separate measures of symptoms of internalizing disorders (e.g., depression, anxiety) and externalizing disorders (e.g., conduct disorder), respectively. METHODS: In a sample of more than 10,000 youth from the Adolescent Brain Cognitive Development (ABCD) Study, we used a dataset of historical PM2.5 levels and growth curve modeling to evaluate associations of PM2.5 exposure with internalizing and externalizing symptom trajectories, as assessed by the Child Behavioral Check List. Three distinct measures of PM2.5 exposure were investigated: annual average concentration during 2016, number of days in 2016 above the US Environmental Protection Agency (US EPA) 24-h PM2.5 standards, and maximum 24-h concentration during 2016. RESULTS: At baseline, higher number of days with PM2.5 levels above US EPA standards was associated with higher parent-reported internalizing symptoms in the same year. This association remained significant up to a year following exposure and after controlling for PM2.5 annual average, maximum 24-h level, and informant psychopathology. There was also evidence of an association between PM2.5 annual average and externalizing symptom levels at baseline in females only. DISCUSSION: Results suggested PM2.5 exposure during childhood is associated with higher symptoms of internalizing and externalizing disorders at the time of exposure and 1 y later. In addition, effects of PM2.5 exposure on youth internalizing symptoms may be most impacted by the number of days of exposure above US EPA standards in comparison with annual average and maximum daily exposure. https://doi.org/10.1289/EHP13427.

Relation between air quality and asthma in high-altitude places, La Paz, Bolivia (3,600 m a.s.l.) / Relación entre la calidad del aire y el asma en habitantes de grandes altitudes, La Paz, Bolivia (3.600 m.s.n.m.).

Introduction. Asthma is a chronic disease affecting millions of people around the world. Air quality is a major factor in triggering asthma symptoms. Objective. To analyze air quality and asthma in high-altitude residents of La Paz, Bolivia. Materials and methods. In this analytical, descriptive, and retrospective study, we collected data from patients diagnosed with asthma at the Instituto Nacional del Tórax and the Instituto Boliviano de Biología de Altura. In addition, air quality monitoring of particulate matter was carried out at the stations of the Red de Monitoreo de la Calidad del Aire. Results. Women represented 56.9% of cases at the Instituto Nacional del Tórax and the Instituto Boliviano de Biología de Altura. In both institutions, the average age was 47 years and patients were overweight or obese. Increases in PM2.5 were recorded in autumn, winter and spring from 2014, 2016 to 2019 and 2015 in all four seasons. PM10 showed increases in autumn and winter from 2014 to 2020 within the established limits. We observed a positive and significant association between PM2,5 concentration and the spirometry parameters of forced vital capacity, peak expiratory flow, and "reversibility percentage" or "bronchodilator response percentage". The association of PM10 and forced vital capacity, forced expiratory volume in the first second, and peak expiratory flow, was also statistically significant. Conclusion. Asthma cases occur on average at 47 years of age in overweight or obese people. We observed a positive association between particles PM2,5 and PM10 with spirometric parameters, stronger with particulate matter PM2,5.

Introducción. El asma es una enfermedad crónica que afecta a millones de personas en todo el mundo. La calidad del aire es uno de los factores clave que puede desencadenar los síntomas del asma. Objetivo. Analizar la calidad del aire y su relación con el asma en habitantes de grandes altitudes en La Paz (Bolivia). Materiales y métodos. Se desarrolló un estudio analítico, descriptivo y retrospectivo. Se recolectaron datos de pacientes con diagnóstico de asma en el Instituto Nacional del Tórax y en el Instituto Boliviano de Biología de Altura. Además, se monitoreó la calidad del aire y su material particulado en las estaciones de la "Red de monitoreo de la calidad del aire". Resultados. El 56,9 % de los casos fueron mujeres del Instituto Nacional del Tórax y el 45,7 % del Instituto Boliviano de Biología de Altura. En ambas instituciones, la media de edad fue de 47 años y los pacientes presentaban sobrepeso u obesidad. Se registraron incrementos de material particulado fino (PM2,5) en otoño, invierno y primavera, en 2014, 2016-2019 y en las cuatro estaciones del 2015. El material particulado inhalable grueso (PM10) se incrementó en otoño e invierno del 2014 al 2020, dentro de los límites establecidos. Se observó una asociación positiva y significativa entre la concentración de material particulado PM2,5 y los parámetros espirométricos de capacidad vital forzada, flujo espiratorio máximo y el porcentaje de reversión. La relación de partículas PM10 y los parámetros espirométricos de capacidad vital forzada, volumen espiratorio máximo en el primer segundo y flujo espiratorio máximo, también fue estadísticamente significativa. Conclusión. Los casos de asma se presentaron en promedio a los 47 años y en personas con sobrepeso u obesidad. Se observó una asociación positiva entre el material particulado, PM2,5 y PM10, con los parámetros espirométricos, la cual fue más marcada con las partículas PM2,5.

Short-term associations of PM10 attributed to biomass burning with respiratory and cardiovascular hospital admissions in Peninsular Malaysia.

BACKGROUND: Biomass burning (BB) is a major source of air pollution and particulate matter (PM) in Southeast Asia. However, the health effects of PM smaller than 10 µm (PM10) originating from BB may differ from those of other sources. This study aimed to estimate the short-term association of PM10 from BB with respiratory and cardiovascular hospital admissions in Peninsular Malaysia, a region often exposed to BB events. METHODS: We obtained and analyzed daily data on hospital admissions, PM10 levels and BB days from five districts from 2005 to 2015. We identified BB days by evaluating the BB hotspots and backward wind trajectories. We estimated PM10 attributable to BB from the excess of the moving average of PM10 during days without BB hotspots. We fitted time-series quasi-Poisson regression models for each district and pooled them using meta-analyses. We adjusted for potential confounders and examined the lagged effects up to 3 days, and potential effect modification by age and sex. RESULTS: We analyzed 210 960 respiratory and 178 952 cardiovascular admissions. Almost 50% of days were identified as BB days, with a mean PM10 level of 53.1 µg/m3 during BB days and 40.1 µg/m3 during normal days. A 10 µg/m3 increment in PM10 from BB was associated with a 0.44% (95% CI: 0.06, 0.82%) increase in respiratory admissions at lag 0-1, with a stronger association in adults aged 15-64 years and females. We did not see any significant associations for cardiovascular admissions. CONCLUSIONS: Our findings suggest that short-term exposure to PM10 from BB increased the risk of respiratory hospitalizations in Peninsular Malaysia.

Estimating the short-term effect of PM2.5 on the mortality of cardiovascular diseases based on instrumental variables.

BACKGROUND: PM2.5 can induce and aggravate the occurrence and development of cardiovascular diseases (CVDs). The objective of our study is to estimate the causal effect of PM2.5 on mortality rates associated with CVDs using the instrumental variables (IVs) method. METHODS: We extracted daily meteorological, PM2.5 and CVDs death data from 2016 to 2020 in Binzhou. Subsequently, we employed the general additive model (GAM), two-stage predictor substitution (2SPS), and control function (CFN) to analyze the association between PM2.5 and daily CVDs mortality. RESULTS: The 2SPS estimated the association between PM2.5 and daily CVDs mortality as 1.14% (95% CI: 1.04%, 1.14%) for every 10 µg/m3 increase in PM2.5. Meanwhile, the CFN estimated this association to be 1.05% (95% CI: 1.02%, 1.10%). The GAM estimated it as 0.85% (95% CI: 0.77%, 1.05%). PM2.5 also exhibited a statistically significant effect on the mortality rate of patients with ischaemic heart disease, myocardial infarction, or cerebrovascular accidents (P < 0.05). However, no significant association was observed between PM2.5 and hypertension. CONCLUSION: PM2.5 was significantly associated with daily CVDs deaths (excluding hypertension). The estimates from the IVs method were slightly higher than those from the GAM. Previous studies based on GAM may have underestimated the impact of PM2.5 on CVDs.

Relationship between exposure to fine particulate matter and cardiovascular risk factors and the modifying effect of socioeconomic status: a cross-sectional study in Beijing, China.

Accumulating research suggested that long-term exposure to fine particulate matter (PM2.5) is related to cardiovascular disease (CVD). However, evidence regarding the relationship between PM2.5 and CVD risk factors remains inconsistent. We hypothesized that this association may be partially modified by socioeconomic status (SES). To investigate the relationships and to test the modifying effect of SES, we included baseline data for 21,018 adults from September 2017 to May 2018. PM2.5 concentrations were determined by employing an amalgamation of linear measurements obtained from monitoring stations located near the participants' residential and workplace addresses. We assessed SES across several domains, including income, education, and occupation levels, as well as through a composite SES index. The results indicated that for every 10 µg/m3 increase in PM2.5 exposure, the risk of hypercholesterolemia, hyperbetalipoproteinemia, diabetes, and hyperhomocysteinemia (HHcy) increased by 7.7% [Odds ratio (OR) = 1.077, 95% Confidence Interval (CI) = 1.011, 1.146], 19.6% (OR = 1.196, 95% CI = 1.091, 1.312), 4.2% (OR = 1.042, 95% CI = 1.002, 1.084), and 17.1% (OR = 1.171, 95% CI = 1.133, 1.209), respectively. Compared to the high SES group, those with low SES are more prone to hypercholesterolemia, hyperbetalipoproteinemia, diabetes, and HHcy. Notably, the disparities in SES appear significant in the relationship between PM2.5 exposure and hypercholesterolemia as well as hyperbetalipoproteinemia. But for diabetes and HHcy, the modification effect of SES on PM2.5 shows an inconsistent pattern. In conclusion, the results confirm the association between PM2.5 and cardiovascular risk factors and low SES significantly amplified the adverse PM2.5 effect on dyslipidemia. It is crucial to emphasize a need to improve the socioeconomic inequality among adults in Beijing and contribute to the understanding of the urgency in protecting the health of vulnerable groups.

Wearable Sensor Node for Safety Improvement in Workplaces: Technology Assessment in a Simulated Environment.

Personal protective equipment (PPE) has been universally recognized for its role in protecting workers from injuries and illnesses. Smart PPE integrates Internet of Things (IoT) technologies to enable continuous monitoring of workers and their surrounding environment, preventing undesirable events, facilitating rapid emergency response, and informing rescuers of potential hazards. This work presents a smart PPE system with a sensor node architecture designed to monitor workers and their surroundings. The sensor node is equipped with various sensors and communication capabilities, enabling the monitoring of specific gases (VOC, CO2, CO, O2), particulate matter (PM), temperature, humidity, positional information, audio signals, and body gestures. The system utilizes artificial intelligence algorithms to recognize patterns in worker activity that could lead to risky situations. Gas tests were conducted in a special chamber, positioning capabilities were tested indoors and outdoors, and the remaining sensors were tested in a simulated laboratory environment. This paper presents the sensor node architecture and the results of tests on target risky scenarios. The sensor node performed well in all situations, correctly signaling all cases that could lead to risky situations.

Association Between Particulate Matter Exposure and Preterm Birth in Women With Abnormal Preconception Thyrotropin Levels: Large Cohort Study.

Background: Prior research has linked exposure to particulate matter with an aerodynamic diameter of ≤2.5 µm (PM2.5) with preterm birth (PTB). However, the modulating effect of preconception thyroid stimulating hormone (TSH) levels on the relationship between PM2.5 exposure and PTB has not been investigated. Objective: This study aimed to assess whether preconception TSH levels modulate the impact of PM2.5 exposure on PTB. Methods: This cohort study was conducted in Guangdong, China, as a part of the National Free Pre-Pregnancy Checkups Project. PM2.5 exposure was estimated by using the inverse distance weighting method. To investigate the moderating effects of TSH levels on trimester-specific PM2.5 exposure and PTB, we used the Cox proportional hazards model. Additionally, to identify the susceptible exposure windows for weekly specific PM2.5 exposure and PTB, we built distributed lag models incorporating Cox proportional hazards models. Results: A total of 633,516 women who delivered between January 1, 2014, to December 31, 2019, were included. In total, 34,081 (5.4%) of them had abnormal preconception TSH levels. During the entire pregnancy, each 10-µg/m3 increase in PM2.5 was linked to elevated risks of PTB (hazard ratio [HR] 1.559, 95% CI 1.390-1.748), early PTB (HR 1.559, 95% CI 1.227-1.980), and late PTB (HR 1.571, 95% CI 1.379-1.791) among women with abnormal TSH levels. For women with normal preconception TSH levels, PM2.5 exposure during the entire pregnancy was positively associated with the risk of PTB (HR 1.345, 95% CI 1.307-1.385), early PTB (HR 1.203, 95% CI 1.126-1.285), and late PTB (HR 1.386, 95% CI 1.342-1432). The critical susceptible exposure windows were the 3rd-13th and 28th-35th gestational weeks for women with abnormal preconception TSH levels, compared to the 1st-13th and 21st-35th gestational weeks for those with normal preconception TSH levels. Conclusions: PM2.5 exposure was linked with a higher PTB risk, particularly in women with abnormal preconception TSH levels. PM2.5 exposure appears to have a greater effect on pregnant women who are in the early or late stages of pregnancy.

PM2.5 Exposure Inhibits Transepithelial Anion Short-circuit Current by Downregulating P2Y2 Receptor/CFTR Pathway.

Fine particulate matter (PM2.5) can damage airway epithelial barriers. The anion transport system plays a crucial role in airway epithelial barriers. However, the detrimental effect and mechanism of PM2.5 on the anion transport system are still unclear. In this study, airway epithelial cells and ovalbumin (OVA)-induced asthmatic mice were used. In transwell model, the adenosine triphosphate (ATP)-induced transepithelial anion short-circuit current (Isc) and airway surface liquid (ASL) significantly decreased after PM2.5 exposure. In addition, PM2.5 exposure decreased the expression levels of P2Y2R, CFTR and cytoplasmic free-calcium, but ATP can increase the expressions of these proteins. PM2.5 exposure increased the levels of Th2-related cytokines of bronchoalveolar lavage fluid, lung inflammation, collagen deposition and hyperplasisa of goblet cells. Interestingly, the administration of ATP showed an inhibitory effect on lung inflammation induced by PM2.5. Together, our study reveals that PM2.5 impairs the ATP-induced transepithelial anion Isc through downregulating P2Y2R/CFTR pathway, and this process may participate in aggravating airway hyperresponsiveness and airway inflammation. These findings may provide important insights on PM2.5-mediated airway epithelial injury.

Adverse effects of meteorological factors and air pollutants on dry eye disease: a hospital-based retrospective cohort study.

Although previous studies have suggested that meteorological factors and air pollutants can cause dry eye disease (DED), few clinical cohort studies have determined the individual and combined effects of these factors on DED. We investigated the effects of meteorological factors (humidity and temperature) and air pollutants [particles with a diameter ≤ 2.5 µ m (PM2.5), ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO)] on DED. A retrospective cohort study was conducted on 53 DED patients. DED was evaluated by Symptom Assessment in Dry Eye (SANDE), tear secretion, tear film break-up time (TBUT), ocular staining score (OSS), and tear osmolarity. To explore the individual, non-linear, and joint associations between meteorological factors, air pollutants, and DED parameters, we used generalized linear mixed model (GLMM) and Bayesian kernel machine regression (BKMR). After adjusting for all covariates, lower relative humidity or temperature was associated with a higher SANDE (p < 0.05). Higher PM2.5, O3, and NO2 levels were associated with higher SANDE and tear osmolarity (p < 0.05). Higher O3 levels were associated with lower tear secretion and TBUT, whereas higher NO2 levels were associated with higher OSS (p < 0.05). BKMR analyses indicated that a mixture of meteorological factors and air pollutants was significantly associated with increased SANDE, OSS, tear osmolarity, and decreased tear secretion.

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.