Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Unintentional emissions of polychlorinated naphthalenes in China: Sources, composition, and historical trends.

Polychlorinated naphthalenes (PCNs) are detrimental to human health and the environment. With the commercial production of PCNs banned, unintentional releases have emerged as a significant environmental source. However, relevant information is still scarce. In this study, provincial emissions for eight PCNs homologues from 37 sources in the Chinese mainland during the period of 1960-2019 were estimated based on a source-specific and time-varying emission factor database. The results showed that the total PCNs emissions in 2019 reached 757.0 kg with Hebei ranked at the top among all the provinces and iron & steel industry as the biggest source. Low-chlorinated PCNs comprised 90% of emissions by mass, while highly chlorinated PCNs dominated in terms of toxicity, highlighting divergent priorities for mitigating emissions and safeguarding human health. The emissions showed an overall upward trend from 1960 to 2019 driven by emission increase from iron & steel industry in terms of source, and from North China and East China in terms of geographic area. Per-capita emissions followed an inverted U-shaped environmental Kuznets curve while emission intensities decreased with increasing per-capita Gross Domestic Product (GDP) following a nearly linear pattern when log-transformed.

Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

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.

Carbon emissions in China's steel industry from a life cycle perspective: Carbon footprint insights.

China is the most important steel producer in the world, and its steel industry is one of the most carbon-intensive industries in China. Consequently, research on carbon emissions from the steel industry is crucial for China to achieve carbon neutrality and meet its sustainable global development goals. We constructed a carbon dioxide (CO2) emission model for China's iron and steel industry from a life cycle perspective, conducted an empirical analysis based on data from 2019, and calculated the CO2 emissions of the industry throughout its life cycle. Key emission reduction factors were identified using sensitivity analysis. The results demonstrated that the CO2 emission intensity of the steel industry was 2.33 ton CO2/ton, and the production and manufacturing stages were the main sources of CO2 emissions, accounting for 89.84% of the total steel life-cycle emissions. Notably, fossil fuel combustion had the highest sensitivity to steel CO2 emissions, with a sensitivity coefficient of 0.68, reducing the amount of fossil fuel combustion by 20% and carbon emissions by 13.60%. The sensitivities of power structure optimization and scrap consumption were similar, while that of the transportation structure adjustment was the lowest, with a sensitivity coefficient of less than 0.1. Given the current strategic goals of peak carbon and carbon neutrality, it is in the best interest of the Chinese government to actively promote energy-saving and low-carbon technologies, increase the ratio of scrap steel to steelmaking, and build a new power system.

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.

A novel hybrid optimization model for evaluating and forecasting air quality grades.

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models.

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.

Exploring the association between atmospheric pollutants and preterm birth risk in a river valley city.

Objective: To investigate the association between exposure to atmospheric pollutants and preterm birth in a river valley-type city and its critical exposure windows. Methods: A retrospective cohort study was used to collect data from the medical records of preterm and full-term deliveries in two hospitals in urban areas of a typical river valley-type city from January 2018 to December 2019. A total of 7,288 cases were included in the study with general information such as pregnancy times, the number of cesarean sections, occupation, season of conception and regularity of the menstrual cycle. And confounding factors affecting preterm birth were inferred using the chi-square test. The effects of exposure to each pollutant, including particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO) and ozone (O3), during pregnancy on preterm birth and the main exposure windows were explored by establishing a logistic regression model with pollutants introduced as continuous variables. Results: Maternal age, pregnancy times, number of births, number of cesarean sections, season of conception, complications diseases, comorbidities diseases, hypertension disorder of pregnancy and neonatal low birth weight of the newborn were significantly different between preterm and term pregnant women. Logistic regression analysis after adjusting for the above confounders showed that the risk of preterm birth increases by 0.9, 0.6, 2.4% in T2 and by 1.0, 0.9, 2.5% in T3 for each 10 µg/m3 increase in PM2.5, PM10, NO2 concentrations, respectively. The risk of preterm birth increases by 4.3% in T2 for each 10 µg/m3 increase in SO2 concentrations. The risk of preterm birth increases by 123.5% in T2 and increases by 188.5% in T3 for each 10 mg/m3 increase in CO concentrations. Conclusion: Maternal exposure to PM2.5, PM10, NO2, CO was associated with increased risk on preterm birth in mid-pregnancy (T2) and late pregnancy (T3), SO2 exposure was associated with increased risk on preterm birth in mid-pregnancy (T2).

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin.

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.

Impact of formaldehyde on ozone formation in Central China: Important role of biogenic emission in forest region.

Formaldehyde (HCHO) is an important source for driving tropospheric ozone (O3) formation. This study investigated the combined effects of anthropogenic and biogenic emission on O3 formation in the Guanzhong Basin (GZB), Central China, providing useful information into the mechanisms of O3 formation due to the interaction between anthropogenic and biogenic volatile organic compounds (VOCs). A severe O3 pollution episode in summer of 2017 was simulated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impacts of ambient HCHO on ground-level O3. Results showed secondary HCHO dominated ambient levels, peaking in the afternoon (up to 86 %), while primary emissions contributed 14 % on average. This enhanced O3 production by 7.7 % during the morning rush hour and 24.3 % in the afternoon. In addition, HCHO concentration peaked before that of O3, suggesting it plays significant role in O3 formation. Biogenic emission oxidation contributed 3.1 µg m-3 (53.1 %) of HCHO and 5.2 pptv (40.1 %) of hydroperoxyl radicals (HO2) in average urban areas, where the downwind regions of the forests had high nitrogen oxides (NOx) levels and favorable conditions for O3 production (17.3 µg m-3, 20.5 %). In forested regions, sustained isoprene oxidation led to elevated oxidized VOCs including HCHO and acetaldehyde downwind, which practiced further photolysis of O3 formation with anthropogenic NOx in urban areas. Sensitivity experiments recommend controlling industrial and traffic NOx emissions, with regional joint prevention and regulation, which are essential to reduce O3 pollution.

The impact of population influx on infectious diseases - from the mediating effect of polluted air transmission.

The global population influx during the COVID-19 pandemic poses significant challenges to public health, making the prevention and control of infectious diseases a pressing concern. This paper aims to examine the impact of population influx on the spread of infectious diseases, with a specific emphasis on the mediating role of air pollution in this process. A theoretical analysis is conducted to explore the relationship between population influx, air pollution, and infectious diseases. Additionally, we establish a series of econometric models and employ various empirical tests and analytical techniques, including mediation effect test, threshold effect test, and systematic GMM test, to evaluate our hypotheses. The results indicate that: (1) Population influx directly and indirectly impacts infectious diseases. Specifically, population influx not only directly elevates the risk of infectious diseases, but also indirectly increases the incidence rate of infectious diseases by intensifying air pollution. (2) The impact of population inflow on infectious diseases exhibits regional heterogeneity. Compared to central and western China, the eastern regions exhibit a significantly higher risk of infectious diseases, exceeding the national average. (3) External factors influence the relationship between population influx and infectious diseases differently. Personal income and medical resources both help mitigate the risk of infectious diseases due to population influx, with medical resources having a more substantial effect. Contrary to expectations, abundant educational resources have not reduced the risk, instead, they have exacerbated the risk associated with population influx. This paper provides a scientific basis for formulating effective strategies for the prevention and control of infectious diseases.

Effects of the recovery period after particulate matter pollution events on the dust retention capacity and physiological characteristics of Nerium oleander.

Plants are known for their significant dust retention capacity and are widely used to alleviate atmospheric pollution. Urban green plants are exposed to periodic particulate matter pollution stress, and the time intervals between periods of pollution exposure are often inconsistent. The impact of stress memory and pollution intervals on plant dust retention capacity and physiological characteristics during periodic stress is not yet clear. In this study, the common urban landscaping species Nerium oleander L. was selected as the test plant, and stable isotope (15NH4Cl) tracing technology and aerosol generators were used to simulate periodic PM2.5 pollution. This study included two particulate pollution periods (each lasting 14 days) and one recovery period with three different durations (7, 14, and 21 days). The results indicated that periodic particulate matter pollution-induced stress decreased the dust retention capacity of N. oleander leaf surfaces, but particle adsorption to the wax layer was more stable. As the duration of the recovery period increased, leaf particle absorption, which accounted for the greatest proportion of total dust retention, increased, indicating that leaves are the primary organ for dust retention in Nerium oleander L. Root absorption also increased with increasing recovery periods. Prior pollution stress increased oleander physiological and morphological responses, and the plant's air pollution tolerance significantly improved after a recovery period of >14 days.

Anthropogenic debris as nest material in three swift species: New insights into the interactions of atmospheric pollution with wildlife.

Plastic pollution has become a global concern, affecting many species around the world. While well-documented for marine ecosystems, the impact of plastic pollution on terrestrial ecosystems is comparatively limited. In fact, only recently have some studies begun to explore the occurrence, pathways, and impacts of plastic in the atmosphere and on terrestrial species. Here, we assess the presence of synthetic material in nests of three swift species breeding in the Western Palearctic: the common swift (Apus apus), the pallid swift (Apus pallidus), and the alpine swift (Tachymarptis melba). Using data from 487 nests spanning 25 colonies and seven European countries, we show that 36.5 % of the examined nests contained anthropogenic materials, mainly plastic debris. Notably, Pallid swifts' nests, with 85 % of the total nests examined with plastic, rank among birds with the highest plastic content in nests. We also demonstrate that the probability of finding plastic in the nest increased substantially with the human footprint of the landscape. Last, we recorded four cases of swifts entangled in their own nest, a low proportion compared to other species studied previously. Our study provides compelling evidence that plastic pollution may also be considered a concern for other terrestrial species, particularly for birds with highly aerial lifestyles, such as other swifts. The correlation with the human footprint suggests that areas with higher human activity contribute more significantly. Moreover, the entanglement cases, although low, indicate a threat to bird health and welfare. To our knowledge, our study is the first to report a direct interaction between floating plastic debris in the atmosphere and any species. Understanding this interaction is key, not only due to the lack of research on the topic, but also because it highlights that plastic pollution is a multifaceted environmental issue affecting various ecosystem categories, and the broader implications of atmospheric plastic circulation on wildlife and ecosystems health.

Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models.

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.

Semi-diurnal distribution of polycyclic aromatic hydrocarbons bound to PM2.5 and PM0.1 during pollution episode in the urban area of Hanoi.

Every year, Hanoi suffers from several episodes (periods with daily concentration of PM2.5 higher than 50 µg m-3 during at least two consecutive days). These episodes are of health concern because of the high concentration of PM2.5 and/or PM0.1 and the presence of PM-bound toxic components, such as, PAHs. In this study, the concentrations of PAHs bound to PM2.5 and PM0.1 in night-time and day-time samples during episode and non-episode periods in December 2021 were determined. The concentrations of PAHs bound to PM2.5 were found to increase significantly from day-time samples of 3.24 ± 0.83 ng m-3 to night-time samples of 10.8 ± 4.45 ng m-3 in episode periods. However, PAHs bound to PM0.1 increased slightly from day-time samples of 0.58 ± 0.12 ng m-3 to night-time samples of 0.89 ± 0.30 ng m-3 in episode periods. Diagnostic ratios of PAHs indicate that biomass/coal combustion and vehicular emission are the primary sources of PAHs. The incremental lifetime cancer risk was estimated to vary from 8.7E-09 to 2.5E-08 for children and 6.7E-08 to 2.2E-07 for adults, respectively. Accordingly, loss of life expectancy was estimated at 0.11 min and 0.82 min for children and adults, respectively. These findings imply that the carcinogenic impact induced by PAHs via inhalation is negligible during the episode period.

Do social economic status modify the association between air pollution and depressive or anxiety symptoms? A big sample cross-sectional study from the rural areas of Central China.

BACKGROUND: Fewer studies have examined the relationship between air pollution and depressive or anxiety symptoms in rural residents. Social economic status (SES), as an important indicator of the current state of socioeconomic development, we know little about how it modifies the relationship between air pollution and symptoms of depression or anxiety. METHODS: The patient health questionnaire (PHQ-2) and generalized anxiety scale (GAD-2) were used to learn about the prevalence of depressive and anxiety symptoms, the social economic status of the participants was categorized into two levels: lower and higher, and a binary logistic regression model was used to analyze the relationship between air pollution and residents' symptoms of depression or anxiety. RESULTS: A total of 10,670 adults were enrolled in this study, of which a total of 1292 participants suffered from depressive symptoms and 860 suffered from anxiety symptoms. Short-term exposure to PM2.5 and O3, singly or in combination, may be associated with the onset of depression symptoms, and there was a significant interaction between SES and exposure to PM2.5 or O3. Residents of areas with higher SES may have a lower risk of suffering from anxiety symptoms after O3 exposure compared to participants living in lower SES. LIMITATIONS: The study was a cross-sectional study, which may have lowered the quality level of the evidence. CONCLUSIONS: Short-term PM2.5 and O3 exposure may be associated with an increased prevalence risk of depressive symptoms. Higher levels of SES may reduce the adverse effects of air pollution on depressive or anxiety symptoms.

The mediating role of air pollutants in the association between education and lung function among the elderly, the HAPIEE study.

BACKGROUND: Chronic exposure to air pollutants harms human health, and at a geographical level, concentrations of air pollutants are often associated with socioeconomic disadvantage. OBJECTIVES: The aim of this study was to investigate the effects of educational attainment and air pollution on lung function in older adults, and whether air pollution may mediate the effect of education. METHODS: The study included 6381 individuals (mean age 58.24 ± 7.14 years) who participated in the Czech HAPPIE (Health, Alcohol, and Psychosocial Factors in Eastern Europe) study. Participants' residential addresses were linked to air pollution data, including mean exposures to PM10 (particulate matter of aerodynamic diameter below 10 µm) and NO2 (nitrogen dioxide). We used path analysis to link educational attainment and air pollutants to a standardized measure of the Forced Expiratory Volume in the first second (FEV1). RESULTS: Higher levels of participants' education were associated with lower exposures to PM10 and NO2. Individuals with tertiary education had higher standardized FEV1 than individuals with primary education (88 % vs 95 %). Path analysis revealed a direct positive effect of education on FEV1, while about 12 % of the relationship between education and lung function was mediated by PM10 and NO2. CONCLUSIONS: Education (typically completed at young ages) appeared to have a protective effect on lung function later in life, and a small part of this effect was mediated by air pollution.

Exposure to Air Pollution, Genetic Susceptibility, and Psoriasis Risk in the UK.

Importance: Psoriasis is a common autoinflammatory disease influenced by complex interactions between environmental and genetic factors. The influence of long-term air pollution exposure on psoriasis remains underexplored. Objective: To examine the association between long-term exposure to air pollution and psoriasis and the interaction between air pollution and genetic susceptibility for incident psoriasis. Design, Setting, and Participants: This prospective cohort study used data from the UK Biobank. The analysis sample included individuals who were psoriasis free at baseline and had available data on air pollution exposure. Genetic analyses were restricted to White participants. Data were analyzed between November 1 and December 10, 2023. Exposures: Exposure to nitrogen dioxide (NO2), nitrogen oxides (NOx), fine particulate matter with a diameter less than 2.5 µm (PM2.5), and particulate matter with a diameter less than 10 µm (PM10) and genetic susceptibility for psoriasis. Main Outcomes and Measures: To ascertain the association of long-term exposure to NO2, NOx, PM2.5, and PM10 with the risk of psoriasis, a Cox proportional hazards model with time-varying air pollution exposure was used. Cox models were also used to explore the potential interplay between air pollutant exposure and genetic susceptibility for the risk of psoriasis incidence. Results: A total of 474 055 individuals were included, with a mean (SD) age of 56.54 (8.09) years and 257 686 (54.36%) female participants. There were 9186 participants (1.94%) identified as Asian or Asian British, 7542 (1.59%) as Black or Black British, and 446 637 (94.22%) as White European. During a median (IQR) follow-up of 11.91 (11.21-12.59) years, 4031 incident psoriasis events were recorded. There was a positive association between the risk of psoriasis and air pollutant exposure. For every IQR increase in PM2.5, PM10, NO2, and NOx, the hazard ratios (HRs) were 1.41 (95% CI, 1.35-1.46), 1.47 (95% CI, 1.41-1.52), 1.28 (95% CI, 1.23-1.33), and 1.19 (95% CI, 1.14-1.24), respectively. When comparing individuals in the lowest exposure quartile (Q1) with those in the highest exposure quartile (Q4), the multivariate-adjusted HRs were 2.01 (95% CI, 1.83-2.20) for PM2.5, 2.21 (95% CI, 2.02-2.43) for PM10, 1.64 (95% CI, 1.49-1.80) for NO2, and 1.34 (95% CI, 1.22-1.47) for NOx. Moreover, significant interactions between air pollution and genetic predisposition for incident psoriasis were observed. In the subset of 446 637 White individuals, the findings indicated a substantial risk of psoriasis development in participants exposed to the highest quartile of air pollution levels concomitant with high genetic risk compared with those in the lowest quartile of air pollution levels with low genetic risk (PM2.5: HR, 4.11; 95% CI, 3.46-4.90; PM10: HR, 4.29; 95% CI, 3.61-5.08; NO2: HR, 2.95; 95% CI, 2.49-3.50; NOx: HR, 2.44; 95% CI, 2.08-2.87). Conclusions and Relevance: In this prospective cohort study of the association between air pollution and psoriasis, long-term exposure to air pollution was associated with increased psoriasis risk. There was an interaction between air pollution and genetic susceptibility on psoriasis risk.

Assessing the spatiotemporal evolution and socioeconomic determinants of PM2.5-related premature deaths in China from 2000 to 2021.

China's swift socioeconomic development has led to extremely severe ambient PM2.5 levels, the associated negative health outcomes of which include premature death. However, a comprehensive explanation of the socioeconomic mechanism contributing to PM2.5-related premature deaths has not yet to be fully elucidated through long-term spatial panel data. Here, we employed a global exposure mortality model (GEMM) and the system generalized method of moments (Sys-GMM) to examine the primary determinants contributing to premature deaths in Chinese provinces from 2000 to 2021. We found that in the research period, premature deaths in China increased by 46 %, reaching 1.87 million, a figure that decreased somewhat after the COVID-19 outbreak. 62 thousand premature deaths were avoided in 2020 and 2021 compared to 2019, primarily due to the decline in PM2.5 concentrations. Premature deaths have increased across all provinces, particularly in North China, and a discernible spatial agglomeration effect was observed, highlighting effects on nearby provinces. The findings also underscored the significance of determinants such as urbanization, import and export trade, and energy consumption in exacerbating premature deaths, while energy intensity exerted a mitigating influence. Importantly, a U-shaped relationship between premature deaths and economic development was unveiled for the first time, implying the need for vigilance regarding potential health impact deterioration and the implementation of countermeasures as the per capita GDP increases in China. Our findings deserve attention from policymakers as they shed fresh insights into atmospheric control and Health China action.