Annual Average Concentration Of PM2 Research Articles

Carcinogenicity and non-carcinogenicity health risks due to PM2.5 bound trace metals at a sub urban site in Northwest Indo-Gangetic Plain.

This study investigates the PM2.5 bound metals using yearlong measurements at a regionally representative suburban site in the Northwest Indo-Gangetic Plain (NWIGP). The order of the measured annual average concentrations of PM2.5 bound metals is Fe > Zn > Ba > Sn > Pb > Cd > Ni > Mn > Cr > Li. Lithium bound to airborne PM2.5 has been reported for the first time in NWIGP. Ni (72.4 ng m-3) and Cd (36.9 ng m-3) have exceeded the acceptable limits set by NAAQS, India. Estimated the hazard quotient (HQ > 1) of Mn and hazard index (HI > 1) of measured metals exceeded the threshold limits indicating the potential non-carcinogenic health risk due to inhalation exposure of PM2.5 bound trace metals. Further, excessive lifetime cancer risk due to inhalation exposure to Cd, Ni and Cr was estimated and found to exceed the threshold limit set by the USEPA for adults and children.

The relative contribution of PM2.5 components to the obstructive ventilatory dysfunction—insights from a large ventilatory function examination of 305,022 workers in southern China

BackgroundThe new round of WHO/ILO Joint Estimates of the Work-related Burden of Disease assessment requires futher research to provide more evidence, especially on the health impact of ambient air pollution around the workplace. However, the evidence linking obstructive ventilatory dysfunction (OVD) to fine particulate matter (PM2.5) and its chemical components in workers is very limited. Evidence is even more scarce on the interactive effects between occupational factors and particle exposures. We aimed to fill these gaps based on a large ventilatory function examination of workers in southern China. MethodsWe conducted a cross-sectional study among 363,788 workers in southern China in 2020. The annual average concentration of PM2.5 and its components were evaluated around the workplace through validated spatiotemporal models. We used mixed-effect models to evaluate the risk of OVD related to PM2.5 and its components. Results were further stratified by basic characteristics and occupational factors. FindingsAmong the 305,022 workers, 119,936 were observed with OVD. We found for each interquartile range (IQR) increase in PM2.5 concentration, the risk of OVD increased by 27.8 (95 % confidence interval (CI): 26.5–29.2 %). The estimates were 10.9 % (95 %CI: 9.7–12.1 %), 15.8 % (95 %CI: 14.5–17.2 %), 2.6 % (95 %CI: 1.4–3.8 %), 17.1 % (95 %CI: 15.9–18.4 %), and 11 % (95 %CI: 9.9–12.2 %), respectively, for each IQR increment in sulfate, nitrate, ammonium salt, organic matter and black carbon. We observed greater effect estimates among females, younger workers, workers with a length of service of 24–45 months, and professional skill workers. Furthermore, it is particularly noteworthy that the noise-exposed workers, high-temperature-exposed workers, and less-dust-exposed workers were at a 5.7–68.2 % greater risk than others. InterpretationPM2.5 and its components were significantly associated with an increased risk of OVD, with stronger links among certain vulnerable subgroups.

Risk analysis of air pollutants and types of anemia: a UK Biobank prospective cohort study.

Previous studies have suggested that exposure to air pollutants may be associated with specific blood indicators or anemia in certain populations. However, there is insufficient epidemiological data and prospective evidence to evaluate the relationship between environmental air pollution and specific types of anemia. We conducted a large-scale prospective cohort study based on the UK Biobank. Annual average concentrations of NO2, PM2.5, PM2.5-10, and PM10 were obtained from the ESCAPE study using the Land Use Regression (LUR) model. The association between atmospheric pollutants and different types of anemia was investigated using the Cox proportional hazards model. Furthermore, restricted cubic splines were used to explore exposure-response relationships for positive associations, followed by stratification and effect modification analyses by gender and age. After adjusting for demographic characteristics, 3-4 of the four types of air pollution were significantly associated with an increased risk of iron deficiency, vitamin B12 deficiency and folate deficiency anemia, while there was no significant association with other defined types of anemia. After full adjustment, we estimated that the hazard ratios (HRs) of iron deficiency anemia associated with each 10 μg/m3 increase in NO2, PM2.5, and PM10 were 1.04 (95%CI: 1.02, 1.07), 2.00 (95%CI: 1.71, 2.33), and 1.10 (95%CI: 1.02, 1.20) respectively. The HRs of folate deficiency anemia with each 10 μg/m3 increase in NO2, PM2.5, PM2.5-10, and PM10 were 1.25 (95%CI: 1.12, 1.40), 4.61 (95%CI: 2.03, 10.47), 2.81 (95%CI: 1.11, 7.08), and 1.99 (95%CI: 1.25, 3.15) respectively. For vitamin B12 deficiency anemia, no significant association with atmospheric pollution was found. Additionally, we estimated almost linear exposure-response curves between air pollution and anemia, and interaction analyses suggested that gender and age did not modify the association between air pollution and anemia. Our research provided reliable evidence for the association between long-term exposure to PM10, PM2.5, PM2.5-10, NO2, and several types of anemia. NO2, PM2.5, and PM10 significantly increased the risk of iron deficiency anemia and folate deficiency anemia. Additionally, we found that the smaller the PM diameter, the higher the risk, and folate deficiency anemia was more susceptible to air pollution than iron deficiency anemia. No association was observed between the four types of air pollution and hemolytic anemia, aplastic anemia, and other types of anemia. Although the mechanisms are not well understood, we emphasize the need to limit the levels of PM and NO2 in the environment to reduce the potential impact of air pollution on folate and iron deficiency anemia.

Hourly variation characteristics of PM2.5 and main components in Beijing based on wind direction

The annual average concentration of PM2.5 in Beijing consistently met the standard between 2021 and 2023. However, the pollution situation persists, particularly during autumn and winter. With the diminishing capacity for local emission reduction in Beijing, the significance of regional transport has increased. To examine the impact of various air masses on PM2.5 and its chemical constituents, a detailed analysis was conducted using wind direction data, hourly PM2.5 values, and concentrations of major components from 2015 to 2020 in Beijing. The findings reveal that the southwest transport corridor remained a critical pathway for regional pollutant influx into Beijing, although the influence of pollutants transported from the east has intensified. During October to February, when the hourly increase in NO3− surpassed 10 μg/m³ and increases in SO42−, NH4+, and OM exceeded 6 μg/m³, the likelihood of southwest and southern transport ranged from 56% to 60%, peaking at 56%–72% from December to February. From March to September, when the hourly increase in NO3− exceeded 10 μg/m³, eastward transport was probable at 63%, reaching 69% from May to July. Sharp increases in SO42−, NH4+, and OM, related to combustion, primarily occurred during autumn and winter evenings, whereas a sharp increase in NO3− was observed mainly during spring and summer mornings. This study elucidates the correlation between hourly concentration growth of PM2.5 chemical components and transport direction, offering a scientific foundation for mitigating regional air pollution in the Beijing-Tianjin-Hebei area.

Coordinated Control of Carbon Emission Reduction and Air Quality Improvement in the Industrial Sector in Hunan Province

Climate warming and air pollution are the main environmental problems in China. This study used China's Carbon Accounting Database, energy economic model, and air quality model to analyze the potential carbon emission peaking path and synergistic air quality improvement gain in the industrial sector in Hunan Province. Based on China's Carbon Accounting Database and the local industry/energy statistical yearbooks in Hunan, the total CO2 emissions in Hunan Province in 2019 were 310.6 Mt, of which the industrial sector accounted for over 70% of the emissions, mainly from the production and supply of electricity, steam, and heat; the production of non-metallic minerals; and the smelting and pressing of ferrous metals. Three potential industrial carbon emission peaking scenarios were analyzed using the LEAP energy economic model, including the business-as-usual scenario (peaking by 2030), moderate emission reduction scenario (peaking by 2028), and aggressive emission reduction scenario (peaking by 2025), by employing different economic growth rates, energy technology progress, and energy structures of the industrial sector. Furthermore, by combining the anthropogenic air pollutant emission inventory and the regional air quality model WRF-Chem, we analyzed the air quality improvement associated with various carbon emission peak paths. The results showed that the annual mean concentrations of major air pollutants had decreased in the three scenarios, especially in the Chang-Zhu-Tan Region. The aggressive emission reduction scenario was the most effective scenario, followed by the moderate emission reduction scenario and the business-as-usual scenario. Manufacturing was the sector with the most significant synergistic effect of pollution and carbon reduction. When carbon emission peaks were achieved, the annual average concentrations of PM2.5 and PM10 in Hunan Province could be synergistically reduced by 0.6-1.8 μg·m-3 and 1.8-8.9 μg·m-3, respectively. Our findings offer important insights into carbon emission peaking and can provide useful information for potential mitigation actions.

Long-term exposure to ambient PM2.5 and its components on menarche timing among Chinese adolescents: evidence from a representative nationwide cohort

BackgroundAmbient air pollutants have been suggested to affect pubertal development. Nevertheless, current studies indicate inconsistent effects of these pollutants, causing precocious or delayed puberty onset. This study aimed to explore the associations between long-term exposure to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) along with its components and menarche timing among Chinese girls.MethodSelf-reported age at menarche was collected among 855 girls from China Health and Nutrition Survey 2004 to 2015. The pre-menarche annual average concentrations of PM2.5 and its components were calculated on the basis of a long-term (2000–2014) high-resolution PM2.5 components dataset. Generalized linear models (GLM) and logistic regression models were used to analyze the associations of exposure to a single pollutant (PM2.5, sulfate, nitrate, ammonium, black carbon and organic matter) with age at menarche and early menarche (< 12 years), respectively. Weighted quantile sum methods were applied to examine the impacts of joint exposure on menarche timing.ResultsIn the adjusted GLM, per 1 µg/m3 increase of annual average concentrations of nitrate and ammonium decreased age at menarche by 0.098 years and 0.127 years, respectively (all P < 0.05). Every 1 µg/m3 increase of annual average concentrations of PM2.5 (OR: 1.04, 95% CI: 1.00-1.08), sulfate (OR: 1.23, 95% CI: 1.01–1.50), nitrate (OR: 1.23, 95% CI: 1.06–1.43) and ammonium (OR: 1.32, 95% CI: 1.06–1.66) were significantly positively associated with early menarche. Higher level of joint exposure to PM2.5 and its components was associated with 11% higher odds of early menarche (P = 0.04). Additionally, the estimated weight of sulfate was the largest among the mixed pollutants.ConclusionsLong-term exposure to PM2.5 and its components could increase the risk of early menarche among Chinese girls. Moreover, sulfate might be the most critical components responsible for this relationship. Our study provides foundation for targeted prevention of PM2.5 components.

Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques

Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the “optimal” estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the “optimal” estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM2.5, PM10, NO2, and SO2 in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O3 slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area.

PM10 increases mortality risk in rheumatoid arthritis-associated interstitial lung disease

ObjectivesThe effect of air pollution on the prognosis of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) remains poorly understood. We aimed to evaluate the effect of long-term exposure to particulate matter...

Chemical characteristics, morphology and source apportionment of PM10 over National Capital Region (NCR) of India.

The present study frames the physico-chemical characteristics and the source apportionment of PM10 over National Capital Region(NCR) of India using the receptor model's Positive Matrix Factorization (PMF) and Principal Momponent Mnalysis/Absolute Principal Component Score-Multilinear Regression (PCA/APCS-MLR). The annual average mass concentration of PM10 over the urban siteof Faridabad, IGDTUW-Delhi and CSIR-NPL of NCR-Delhi were observed to be 195 ± 121, 275 ± 141 and 209 ± 81µgm-3, respectively. Carbonaceous species (organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC)), elemental constituents (Al, Ti, Na, Mg, Cr, Mn, Fe, Cu, Zn, Br, Ba, Mo Pb) and water-soluble ionic components (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) of PM10were entrenched to the receptor models to comprehend the possible sourcesof PM10. The PMF assorted sources over Faridabad were soil dust (SD 15%), industrial emission (IE 14%), vehicular emission (VE 19%), secondary aerosol (SA 23%) and sodium magnesium salt (SMS 17%). For IGDTUW-Delhi, the sources were SD (16%), VE (19%), SMS (18%), IE (11%), SA (27%) and VE + IE (9%). Emission sources like SD (24%), IE (8%), SMS (20%), VE + IE (12%), VE (15%) and SA + BB (21%) were extracted over CSIR-NPL, New Delhi, which are quite obvious towards the sites. PCA/APCS-MLR quantified the similar sources with varied percentage contribution. Additionally, catalogue the Conditional Bivariate Probability Function (CBPF) for directionality of the local source regions and morphology as spherical, flocculent and irregular were imaged using a Field Emission-Scanning Electron Microscope (FE-SEM).

Particulate matter – a cancerous threat to our health?

Introduction: Particulate matter (PM) as a part of outdoor air pollutants are classified as human carcinogens. They are formed majorly as a result of combustion process by industry, power plants and engines. PM can be divided by the size of their particles into PM2.5 and PM10, where PM2.5 are small enough to penetrate into the alveoli sacs in the lung reaching the bloodstream, whereas PM10 affect mostly oral cavity, nose and the throat.&#x0D; Aim of the study: This study aims to investigate the impact of PM2.5 and PM10 particle concentrations in the EU NUTS 2 subregions on the death rates due to the most common malignant neoplasms.&#x0D; Results: There is a positive moderate correlation (r = 0.421; p &lt; 0.001) between the annual mean concentration of PM2.5 and deaths due to malignant tumors. The strongest correlation was observed for malignant neoplasms of larynx, which is a positive correlation with a strong effect (r = 0.641; p &lt; 0.001); and malignant neoplasm of bladder (r = 0.523; p &lt; 0.001). For PM10, there is a moderately weak positive correlation (r = 0,195; p = 0.008) between the annual average concentration of PM10 and deaths due to malignant tumors. The strongest correlation was observed for malignant neoplasms of larynx, which is a positive correlation with a strong effect (r = 0,551; p &lt; 0.001).&#x0D; Conclusion: The effect of PM impact on the malignant neoplasms is strong to moderate. The most affected neoplasm site are the ones the PMs intake occurs, being in the respiratory system. However other sites, where PMs can accumulate can be impacted as well. Further studies about the population with the highest risk due to the PMs exposure may be beneficial as other non-air quality-connected predictors may be found.

Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences

The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m3, 10.2 µg/m3, and 7.3%, respectively.This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study’s findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.

Associations of long-term exposure to PM2.5 constituents with serum uric acid and hyperuricemia in Chinese adults

ObjectiveLittle is known about the magnitude of the relation of ambient fine particulate matter (PM2.5) constituents with hyperuricemia and serum uric acid (SUA) levels. Therefore, we aimed to assess the associations and to identify the most hazardous constituent.MethodsThis study included 72,840 participants from the China Multi-Ethnic Cohort. Annual average concentrations of PM2.5 mass and its major 7 constituents were matched to individuals by residential address. SUA levels exceeding 7.0 mg/dL (417 μmol/L) for men and 6.0 mg/dL (357 μmol/L) for women were considered to be hyperuricemia. Multiple logistic and linear regressions were performed on the association of single exposure to PM2.5 constituents with hyperuricemia and SUA, separately. The weighted quantile sum method was applied to examine the joint effect of PM2.5 constituents on hyperuricemia/SUA.ResultsSignificant positive associations were discovered between PM2.5 constituents and SUA/hyperuricemia. For example, the odds ratio (95% confidence interval) of hyperuricemia for per standard deviation increase of PM2.5 mass, black carbon, organic matter, ammonium, and nitrate concentrations were 1.22 (1.12–1.32), 1.17 (1.08–1.27), 1.20 (1.10–1.31), 1.21 (1.11–1.31), and 1.28 (1.18–1.40), respectively. The joint exposure to PM2.5 constituents was significantly positively correlated with hyperuricemia (1.09, 1.05–1.14) and SUA (1.05, 1.03–1.06). And the weight of nitrate was the largest (0.668 for hyperuricemia, 0.586 for SUA).ConclusionsOur findings suggest that long-term exposure to PM2.5 constituents is associated with increased SUA levels and a higher risk of hyperuricemia. In particular, nitrate seems to be the main contributor. This study may help prevent hyperuricemia by promoting the introduction of precise preventive measures.Graphical

Analysis of Air Pollutants and Their Potential Sources in Eastern Xinjiang, Northwestern Inland China, from 2018 to 2022

Air pollution in the developed regions of eastern China has been intensively investigated in the past decade. However, there is a relative dearth of air pollution studies on the northwest of inland China (e.g., Xinjiang). In this work, hourly measurement data of six criteria air pollutants (PM2.5, PM10, CO, NO2, O3, and SO2) for the past five years (2018–2022) from Hami and Turpan cities of eastern Xinjiang were analyzed to reveal air pollution characteristics and the distribution of potential sources. Hami and Turpan had the highest AQI values in winter due to increased coal combustion for domestic heating and unfavorable meteorological conditions. The slight elevations of AQI values in spring were caused by frequent dust storms. PM10 was the most frequent main pollutant in both Hami (63.1%) and Turpan (74.1%), followed by PM2.5 and O3. Except for O3, PM2.5, PM10, SO2, NO2, and CO exhibited a generally decreasing pattern in annual average values. But the annual average concentrations of PM10 in Hami (83.5 μg·m−3) and Turpan (139 μg·m−3) in 2022 were still higher than those in eastern China. Diurnal and monthly variations of the six criteria pollutants were influenced by a combination of emission sources and meteorological conditions. The air masses in eastern Xinjiang mainly originated from the west and north and were affected by both inter-regional and intra-regional transport. Analysis of the distribution of potential sources showed that local emissions strongly impacted particulate matter pollution in winter, while regional transport played a dominant role in other seasons. O3 showed a broad distribution of potential sources across all four seasons. Considering that the trend that O3 pollution increased year by year, eastern Xinjiang might face a similar pollution situation as eastern China, i.e., the combined pollution of particulate matter and O3.

Long- and short-term health benefits attributable to PM2.5 constituents reductions from 2013 to 2021: A spatiotemporal analysis in China

Long- and short-term exposure to constituents of fine particulate matter (PM2.5) substantially affects human health. However, assessments of the health and economic benefits of reducing PM2.5 constituents are scarce. This study estimates the number of premature deaths from all-cause, cardiovascular (CVD), and respiratory diseases avoided due to reductions in daily and annual average concentrations of PM2.5 constituents. The Environmental Benefits Mapping and Analysis Program was used for two scenarios: we used yearly concentrations of PM2.5 constituents from 2013 to 2020 as the baseline concentration surface (Scenario I), and 2021 as the baseline year (Scenario II). With reductions in daily and annual average concentrations of PM2.5 constituents, 309,099 (95 % confidence interval [CI]: 37,265–571,485) and 195,297 (95 % CI: 178,192–211,914) premature deaths were avoided in Scenario I, respectively; meanwhile, 347,296 (95 % CI: 79,258–604,758) and 201,567 (95 % CI: 185,038–217,530) premature deaths were avoided in Scenario II, respectively. Moreover, economic benefits associated with the prevention of premature deaths were estimated using the willingness to pay (WTP) and modified human capital (AHC) methods. The total estimated economic benefits amounted to 563.32 billion RMB (WTP) and 322.03 billion RMB (AHC) in Scenario I. In Scenario II, the associated economic benefits were 751.48 billion RMB (WTP) and 427.56 billion RMB (AHC), accounting for 0.657 and 0.374 % of China's gross domestic product in 2021, respectively. Additionally, we analyzed the sensitivity of CVD-related premature deaths to the concentrations of PM2.5 constituents, and found that CVD-related premature deaths were more sensitive to black carbon.

Ambient air pollution exposure, plasma metabolomic markers, and risk of type 2 diabetes: A prospective cohort study

BackgroundBoth air pollution (AP) and impaired lipid metabolism contribute to type 2 diabetes (T2D). However, little is known about the detailed associations of AP to lipidomic markers and the specific lipid metabolomic profile that mediates the impact of AP on incident T2D. We aimed to examine the associations between long-term AP exposure, plasma metabolomic markers, and incident T2D, and subsequently determine the lipid metabolomic profile that mediates the relationship between AP and T2D. MethodsThis prospective study included 82,548 participants from the UK Biobank without a history of T2D at baseline. Baseline plasma samples were analyzed using the nuclear magnetic resonance (NMR) metabolomic platform, which measured 168 metabolomic markers, including lipids, lipoprotein subclasses, and other circulating metabolites. Land Use Regression models were utilized to estimate annual average concentrations of PM2.5 and NO2. The associations among AP, metabolomic markers, and T2D were investigated using multivariable linear regressions and Cox proportional hazards models. Mediation analyses were performed to assess the role of each metabolomic marker in the AP-T2D relationship. Furthermore, principal component (PC) analysis was conducted on 168 metabolomic markers to extract metabolic patterns. These patterns were utilized to determine their associations with AP and T2D, as well as their mediating role in the AP-T2D relationship. ResultsWe found that long-term AP exposure was associated with some lipid metabolites, including ApoA1, HDL concentration, HDL size, and lipid components within HDL, especially in very large, large, and medium HDL, as well as some other lipids, fatty acids, amino acids, glucose, and glycoprotein acetyls. In pairwise mediation analysis, these metabolites exhibited significant mediation effects in the AP-T2D relationship. We identified six PCS representing distinct metabolic patterns. Long-term exposure to PM2.5 and NO2 showed significantly negative associations with PC2 (characterized by high levels of ApoA1, larger HDL, other lipids, and low levels of larger VLDL). PC2 mediated 12.3% and 10.3% of the associations of PM2.5 and NO2 with incident T2D, respectively. ConclusionsThis study revealed the associations of AP with various lipid metabolites. A lipid metabolomic profile characterized by ApoA1 and larger HDL may mediate the association between AP and incident T2D.

Air Pollution and Allergic Rhinitis: Findings from a Prospective Cohort Study.

To investigate the association of long-term exposure to ambient air pollution with the risk of allergic rhinitis (AR), we performed a longitudinal analysis of 379,488 participants (47.4% women) free of AR at baseline in the UK Biobank. The annual average concentrations of PM2.5, PMcoarse, PM10, NO2, and NOx were estimated by land use regression models. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). A weighted polygenic risk score was constructed. During a median follow-up period of 12.5 years, 3095 AR cases were identified. We observed significant associations between the risk of AR and PM2.5 (HR: 1.51, 95% CI: 1.27-1.79, per 5 μg/m3), PMcoarse (HR: 1.28, 95% CI: 1.06-1.55, per 5 μg/m3), PM10 (HR: 1.45, 95% CI: 1.20-1.74, per 10 μg/m3), NO2 (HR: 1.14, 95% CI: 1.09-1.19, per 10 μg/m3), and NOx (HR: 1.10, 95% CI: 1.05-1.15, per 20 μg/m3). Moreover, participants with high air pollution combined with high genetic risk showed the highest risk of AR, although no multiplicative or additive interaction was observed. In conclusion, long-term exposure to air pollutants was associated with an elevated risk of AR, particularly in high-genetic-risk populations, emphasizing the urgent need to improve air quality.

Mediating effect of body fat percentage in the association between ambient particulate matter exposure and hypertension: a subset analysis of China hypertension survey

BackgroundHypertension caused by air pollution exposure is a growing concern in China. The association between air pollutant exposure and hypertension has been found to be potentiated by obesity, however, little is known about the processes mediating this association. This study investigated the association between fine particulate matter (aerodynamic equivalent diameter ≤ 2.5 microns, PM2.5) exposure and the prevalence of hypertension in a representative population in southern China and tested whether obesity mediated this association.MethodsA total of 14,308 adults from 48 communities/villages in southern China were selected from January 2015 to December 2015 using a stratified multistage random sampling method. Hourly PM2.5 measurements were collected from the China National Environmental Monitoring Centre. Restricted cubic splines were used to analyze the nonlinear dose-response relationship between PM2.5 exposure and hypertension risk. The mediating effect mechanism of obesity on PM2.5-associated hypertension was tested in a causal inference framework following the approach proposed by Imai and Keele.ResultsA total of 20.7% (2966/14,308) of participants in the present study were diagnosed with hypertension. Nonlinear exposure-response analysis revealed that exposure to an annual mean PM2.5 concentration above 41.8 µg/m3 was associated with increased hypertension risk at an incremental gradient. 9.1% of the hypertension burden could be attributed to exposure to elevated annual average concentrations of PM2.5. It is noteworthy that an increased body fat percentage positively mediated 59.3% of the association between PM2.5 exposure and hypertension risk, whereas body mass index mediated 34.3% of this association.ConclusionsThis study suggests that a significant portion of the estimated effect of exposure to PM2.5 on the risk of hypertension appears to be attributed to its effect on alterations in body composition and the development of obesity. These findings could inform intersectoral actions in future studies to protect populations with excessive fine particle exposure from developing hypertension.

Job Exposure Matrix, a Solution for Retrospective Assessment of Particle Exposure in a Subway Network and Their Long-Term Effects.

Health effects after long-term exposure to subway particulate matter (PM) remain unknown due to the lack of individual PM exposure data. This study aimed to apply the job exposure matrix (JEM) approach to retrospectively assess occupational exposure to PM in the Parisian subway. Job, the line and sector of the transport network, as well as calendar period were four JEM dimensions. For each combination of these dimensions, we generated statistical models to estimate the annual average PM10 concentration using data from an exhaustive inventory of the PM measurement campaigns conducted between 2004 and 2020 in the Parisian subway and historical data from the Parisian air pollution monitoring network. The resulting JEM and its exposure estimates were critically examined by experts using the uncertainty analysis framework. The resulting JEM allows for the assignment of the estimated annual PM10 concentration to three types of professionals working in the subway: locomotive operators, station agents, and security guards. The estimates' precision and validity depend on the amount and quality of PM10 measurement data used in the job-, line-, and sector-specific models. Models using large amounts of personal exposure measurement data produced rather robust exposure estimates compared to models with lacunary data (i.e., in security guards). The analysis of uncertainty around the exposure estimates allows for the identification of the sources of uncertainty and parameters to be addressed in the future in order to refine and/or improve the JEM. The JEM approach seems relevant for the retrospective exposure assessment of subway workers. When applied to available data on PM10, it allows for the estimation of this exposure in locomotive operators and station agents with an acceptable validity. Conversely, for security guards, the current estimates have insufficient validity to recommend their use in an epidemiological study. Therefore, the current JEM should be considered as a valid prototype, which shall be further improved using more robust measurements for some jobs. This JEM can also be further refined by considering additional exposure determinants.

Ambient PM2.5 and its components associated with 10-year atherosclerotic cardiovascular disease risk in Chinese adults

BackgroundExposure to particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) may increase the risk of 10-year atherosclerotic cardiovascular disease (ASCVD) risk. While PM2.5 is comprised of various components, the evidence on the correlation of its components with 10-year ASCVD risk and which component contributes most remains limited. MethodsData were derived from the baseline assessments of China Multi-Ethnic Cohort (CMEC). In total, 69,722 individuals aged 35–74 years were included into this study. The annual average concentration of PM2.5 and its components (black carbon, ammonium, nitrate, sulfate, organic matter, soil particles, and sea salt) were estimated by satellite remote sensing and chemical transport models. The ASCVD risk of individuals was calculated by the equations from the China-PAR Project (prediction for ASCVD risk in China). The relationship between single exposure to PM2.5 and its components and predicted 10-year ASCVD risk was assessed using the logistic regression model. The effect of joint exposure was estimated, and the most significant contributor was identified using the weighted quantile sum approach. ResultsTotally 69,722 participants were included, of which 95.8 % and 4.2 % had low and high 10-year ASCVD risk, respectively. Per standard deviation increases in the 3-year average concentration of PM2.5 mass (odds ratio [OR] 1.23, 95 % confidence interval [CI]: 1.12–1.35), black carbon (1.21, 1.11–1.33), ammonium (1.21, 1.10–1.32), nitrate (1.25, 1.14–1.38), organic matter (1.29, 1.18–1.42), sulfate (1.17, 1.07–1.28), and soil particles (1.15, 1.04–1.26) were related to high 10-year ASCVD risk. The overall effect (1.19, 1.11–1.28) of the PM2.5 components was positively associated with 10-year ASCVD risk, and organic matter had the most contribution to this relationship. Female participants were more significantly impacted by PM2.5, black carbon, ammonium, nitrate, organic matter, sulfate, and soil particles compared to others. ConclusionLong-term exposure to PM2.5 mass, black carbon, ammonium, nitrate, organic matter, sulfate, and soil particles were positively associated with high 10-year ASCVD risk, while sea salt exhibited a protective effect. Moreover, the organic matter might take primary responsibility for the relationship between PM2.5 and 10-year ASCVD risk. Females were more susceptible to the adverse effect.

Analysis of PM2.5 Synergistic Governance Path from a Socio-Economic Perspective: A Case Study of Guangdong Province

Analyzing the influencing factors of PM2.5 concentration, scenario simulations, and countermeasure research to address the problem of PM2.5 pollution in Guangdong Province is of great significance for governments at all levels for formulating relevant policies. In this study, the ChinaHighPM2.5 dataset and economic and social statistics for Guangdong Province from 2010 to 2019 were selected, and a PM2.5 pollution management compliance path formulation method based on the multi-scenario simulation was proposed by combining the differences in city types and PM2.5 concentration prediction. Based on the prediction model of PM2.5 concentration constructed by the Ridge and SVM models and facing the PM2.5 pollution control target in 2025, the urban PM2.5 pollution control scenario considering the characteristics of urban development was constructed. According to the scenario simulation results of the PM2.5 prediction model, the PM2.5 pollution control path suitable for Guangdong Province during the 14th Five-Year Plan period was explored. The coupling coordination model was used to explore the spatial and temporal pattern evolution of PM2.5 pollution collaborative governance in various prefecture-level cities under the standard path, and the policy recommendations for PM2.5 pollution control during the 14th Five-Year Plan period are proposed. The results showed the following: ① in the case of small samples, the model can provide effective simulation predictions for the study of urban pollutant management compliance pathways. ② Under the scenario of PM2.5 management meeting the standard, in 2025, the annual average mass concentration of PM2.5 in all prefecture-level cities in Guangdong Province will be lower than 22 μg/m3, and the annual average concentration of PM2.5 in the whole province will drop from 25.91 μg/m3 to 21.04 μg/m3, which will fulfil the goal of reducing the annual average concentration of PM2.5 in the whole province to below 22 μg/m3, as set out in the 14th Five-Year Plan for the Ecological Environmental Protection of Guangdong Province. ③ Under the path of PM2.5 control and attainment, the regional coordination relationship among prefecture-level cities in Guangdong Province is gradually optimized, the number of intermediate-level coordinated cities will increase, and the overall spatial distribution pattern will be low in the middle and high in the surrounding area. Based on the characteristics of the four city types, it is recommended that a staggered development strategy be implemented to achieve synergy between economic development and environmental quality. Urban type I should focus on restructuring freight transportation to reduce urban pollutant emissions. City type II should focus on urban transportation and greening. For city type III, the focus should be on optimizing the industrial structure, adjusting the freight structure, and increasing the greening rate of the city. For city type IV, industrial upgrading, energy efficiency, freight structure, and management of industrial pollutant emissions should be strengthened.