细颗粒物免疫毒性研究进展

大气中细颗粒物的暴露是多种健康问题的危险因素 ,而免疫系统是细颗粒物毒性作用的靶器官之一 ,细颗粒物对非特异性免疫系统和特异性免疫系统均有一定影响。免疫系统作用有两面性 ,既对颗粒物具有一定的清除能力 ,同时也是机体受损的重要原因。免疫毒性与颗粒物产生的其他生物效应有密切关系。免疫毒性的作用机制可能通过氧化、炎症刺激和神经性炎症反应等有关 ,但具体致病机制仍不清楚 ,有必要从整体上研究颗粒物的毒性及其作用机制。本文从不同角度和不同水平对细颗粒物的免疫毒性进行综述。     

大气细颗粒物对人支气管上皮细胞氧化损伤作用的研究

目的 研究大气细颗粒物(PM2.5)对人支气管上皮细胞(16-HBE)氧化损伤的作用.方法 将16-HBE分别暴露于8、16、32、64、128 μg/ml的于广州灰霾天气采集的大气PM2.5中24、48、72 h,采用ELISA法检测细胞8-羟基脱氧鸟苷(8-OHdG)含量,并与本课题组前期检测的细胞凋亡和DNA损伤数据进行相关性分析.结果 染毒24、48、72 h后,与对照组相比,64、128 μg/ml染毒组8-OHdG含量均明显增加(P＜0.05);与染毒24 h相比,128 μg/ml染毒72 h组8-OHdG含量明显增加(P＜0.05).氧化损伤与细胞DNA损伤和凋亡之间存在正相关(P＜0.05),细胞DNA损伤与凋亡之间存在正相关(P＜0.05).结论 大气PM2.5可导致16-HBE氧化损伤,氧化损伤可能是PM2.5引起呼吸道损伤的重要作用机制之一.     

Evaluation of cytokine expression in BEAS cells exposed to fine particulate matter (PM2.5) from specialized indoor environments

Fine particles were collected in three indoor environments and an outdoor reference site. Samples were acid and aqueous extracted for metal analyses and cytokine expression study using a BEAS-2B line. Results revealed that the average PM(2.5) concentration indoors was 5.8 μg/m(3) while outside, it was 9.4 μg/m(3). The airborne metal concentrations in indoor air ranged from 0.01 ng/m(3) (Cd) to 620 ng/m(3) (Al). All metals analyzed were higher indoors when compared to outdoor (I/O ratio) indicating a contribution from the workplace. Some metals were more efficiently extracted (e.g., Ni, V, As) in the aqueous phase than others (e.g., Fe and Al). Toxicological assays showed that the aqueous extracts at 20% induced IL-6 and subsequently inhibited it at a higher concentration (50%); both IL-8 and MCP-1 were inhibited at 20 and 50%. As, Ni and V concentrations seem to be the most important metals associated with the cytokine induction/inhibition response probably due to the higher bioavailability.     

Airborne particulate matter (PM2.5 and PM10) and associated metals in urban Turkey

Airborne particulate matter (PM) and associated metals were measured in a district of an industrial city in Western Turkey. We compared PM concentrations in Bursa, Turkey (Nilufer district) with international air quality standards. Turkish legislature adopted the EC Air Quality Framework in 2008, and compliance is required in the medium term. State-of-the-art reference methods were used for all measurements. A Partisol sampler measured urban background PM_2.5 and PM₁₀ between May 2007 and April 2008, and PM_2.5 samples were later analysed for selected metals using ICP-MS. Average PM_2.5 and PM₁₀ mass concentrations over the year were 53 and 83 μg/m³, respectively. The annual mean PM_2.5:PM₁₀ ratio in Bursa was 0.64. PM_2.5 and PM₁₀ were highly correlated at the site (R?=?0.91 overall), especially in winter. In the cold seasons, the coarse and fine fractions were strongly correlated R?=?0.67 (p?<?0.1), while in the warm seasons, they were not (R?=?0.01). Sampler results correlated well with a nearby Government sampler. Current PM₁₀ and PM_2.5 levels in Bursa breach current and prospective EU air quality standards, with significant implications in public health.     

臭氧和细颗粒物暴露对大鼠心脏自主神经系统和系统炎症的影响

目的探讨臭氧(O3)和大气细颗粒物(PM2.5)暴露对大鼠心脏自主神经系统和系统炎症的影响以及二者之间是否存在关联。方法 48只Wistar大鼠随机分为8组。PM2.5低、中、高剂量单独暴露组为每只动物分别经气管滴注0.2、0.8和3.2mg的细颗粒物。O3和PM2.5联合暴露低、中、高剂量组为大鼠预先暴露于0.8ppm的臭氧4h,然后再气管滴注PM2.50.2、0.8和3.2mg。O3单独暴露组为只吸入暴露4h臭氧,对照组为气管滴注生理盐水,每周暴露2次,共3周。末次暴露24h后,测定血压,同时记录心电图。测定血清中TNF-α、IL-6和CRP。取右心室进行HE染色病理分析。结果 PM2.5单独暴露和联合暴露组中心率变异性(HRV)指标与对照组比较有显著变化。O3单独暴露仅引起低频成分(LF)显著增加。心率只有在联合暴露组有明显降低。血压在联合暴露组和高剂量PM2.5组有明显上升。TNF-α和IL-6在PM2.5单独暴露和联合暴露组均有上升趋势。CRP在PM2.5单独和联合暴露组均表现出明显的剂量-效应关系。IL-6、TNF-α和CRP与HRV之间显著相关。病理学检查发现,PM2.5暴露组有颗粒物的沉积和心肌炎症。结论 O3可增强由PM2.5暴露引起的大鼠心脏自主神经系统紊乱和炎症反应。     

Sociodemographic descriptors of personal exposure to fine particles (PM2.5) in EXPOLIS Helsinki

Demographic and socioeconomic differences between population sub-groups were analyzed, as a component of the EXPOLIS (Air Pollution Exposure Distributions Within Adult Urban Populations in Europe) Helsinki study, to explain variation in personal exposures to fine particles (PM2.5). Two-hundred one individuals were randomly selected among 25--55-year-old inhabitants of Helsinki Metropolitan area. Personal exposure samples and residential indoor, residential outdoor and workplace indoor microenvironment measurements of PM2.5 were collected between October 1996 and December 1997. Variation in PM2.5 personal exposures, between sociodemographic sub-groups, was best described by differences in occupational status, education and age. Lower occupational status, less educated and young participants had greater exposures than upper occupational status, more educated and older participants. Different workplace concentrations explained most of the socioeconomic differences, and personal day and night exposures and concentrations in home (but not workplace or outdoor concentrations) caused the PM2.5 exposure differences between age groups. Men had higher exposures and much larger exposure differences between the sociodemographic groups than women. No gender, socioeconomic or age differences were observed in home outdoor concentrations between groups. Exposure to tobacco smoke did not seem to create new differences between the sociodemographic groups; instead, it amplified the existing differences.     

A population exposure model for particulate matter: case study results for PM(2.5) in Philadelphia, PA.

A population exposure model for particulate matter (PM), called the Stochastic Human Exposure and Dose Simulation (SHEDS-PM) model, has been developed and applied in a case study of daily PM(2.5) exposures for the population living in Philadelphia, PA. SHEDS-PM is a probabilistic model that estimates the population distribution of total PM exposures by randomly sampling from various input distributions. A mass balance equation is used to calculate indoor PM concentrations for the residential microenvironment from ambient outdoor PM concentrations and physical factor data (e.g., air exchange, penetration, deposition), as well as emission strengths for indoor PM sources (e.g., smoking, cooking). PM concentrations in nonresidential microenvironments are calculated using equations developed from regression analysis of available indoor and outdoor measurement data for vehicles, offices, schools, stores, and restaurants/bars. Additional model inputs include demographic data for the population being modeled and human activity pattern data from EPA's Consolidated Human Activity Database (CHAD). Model outputs include distributions of daily total PM exposures in various microenvironments (indoors, in vehicles, outdoors), and the contribution from PM of ambient origin to daily total PM exposures in these microenvironments. SHEDS-PM has been applied to the population of Philadelphia using spatially and temporally interpolated ambient PM(2.5) measurements from 1992-1993 and 1990 US Census data for each census tract in Philadelphia. The resulting distributions showed substantial variability in daily total PM(2.5) exposures for the population of Philadelphia (median=20 microg/m(3); 90th percentile=59 microg/m(3)). Variability in human activities, and the presence of indoor-residential sources in particular, contributed to the observed variability in total PM(2.5) exposures. The uncertainty in the estimated population distribution for total PM(2.5) exposures was highest at the upper end of the distribution and revealed the importance of including estimates of input uncertainty in population exposure models. The distributions of daily microenvironmental PM(2.5) exposures (exposures due to time spent in various microenvironments) indicated that indoor-residential PM(2.5) exposures (median=13 microg/m(3)) had the greatest influence on total PM(2.5) exposures compared to the other microenvironments. The distribution of daily exposures to PM(2.5) of ambient origin was less variable across the population than the distribution of daily total PM(2.5) exposures (median=7 microg/m(3); 90th percentile=18 microg/m(3)) and similar to the distribution of ambient outdoor PM(2.5) concentrations. This result suggests that human activity patterns did not have as strong an influence on ambient PM(2.5) exposures as was observed for exposure to other PM(2.5) sources. For most of the simulated population, exposure to PM(2.5) of ambient origin contributed a significant percent of the daily total PM(2.5) exposures (median=37.5%), especially for the segment of the population without exposure to environmental tobacco smoke in the residence (median=46.4%). Development of the SHEDS-PM model using the Philadelphia PM(2.5) case study also provided useful insights into the limitations of currently available data for use in population exposure models. In addition, data needs for improving inputs to the SHEDS-PM model, reducing uncertainty and further refinement of the model structure, were identified.     

Monitoring of long-term personal exposure to fine particulate matter (PM<Subscript>2.5</Subscript>)

Personal monitoring is of a demanding nature; thus, it is very difficult to obtain personal data for periods longer than a few days or a maximum of a few weeks. To fill this gap, we have performed a study in which personal exposure to particulate matter of aerodynamic diameter under 2.5 μm (PM_2.5) was monitored for almost 1 year. One healthy, adult, non-smoking, female student living in Prague (Czech Republic) was involved in the study. A battery-operated, fast-responding nephelometer was worn by the individual for a period of 10 months, recording PM_2.5 concentration every 5 min. A written time activity diary was used to record the experimental person's movement and the microenvironments visited. The dataset was divided into 12 different (seven indoor and four outdoor and transit) microenvironments. The overall average of the year-long measurement was 14.9 ± 52.5 μg.m⁻³ (median, 8.0 μg.m⁻³). The highest PM_2.5 average concentration was detected in restaurant microenvironments (294.4 μg.m⁻³), while the second highest concentration was recorded in an indoor microenvironment heated by wood and coal stoves (112.2 μg.m⁻³). The lowest mean aerosol concentrations were detected outdoors in a rural/natural environment (7.0 μg.m⁻³) and indoors at the monitored person's home (9.3 μg.m⁻³). During the measurement period, isolated and brief, but very high concentration excursions over 500 μg.m⁻³ or even over 1,000 μg.m⁻³ were recorded. However, they accounted for less than 0.5% of the total time of personal exposure. We conclude that continuous long-term monitoring is a good tool capable of disclosing the frequency and severity of short-term peak events of high particulate concentrations, which may be associated with adverse health effects.     

高效液相色谱-荧光检测法测定大气细颗粒物(PM2.5〖KG-*5〗)中的双酚A

目的 建立大气细颗粒物(PM2.5)中双酚A 的高效液相色谱-荧光测定法,并用于成都市大气细颗粒物(PM2.5)中BPA的测定。方法 以玻璃纤维滤纸为滤料,用中流量PM2.5 采样器以100L/min的流速连续采样24h。滤纸样品经0.1%盐酸-甲醇超声提取后经C18小柱固相萃取,用0.1%氨水-甲醇洗脱,洗脱液调pH 2~3后,C18色谱柱(250mm×4.6mm,5μm)分离,荧光法检测(λex=227nm,λem=310nm)。结果 在0.001μg/ml~0.020μg/ml范围内回归方程为y=5.39×106x+5146,相关系数大于0.997;方法检出限为3.0×10-5μg/ml,定量限为1.1×10-4μg/ml,最低检出含量为200pg/m3(取样1/8分析),加标回收率为94.9%~104.8%,RSD为2.84%~9.23%。利用本法检测了20个成都市大气细颗粒物样品,测得双酚A含量范围在<200~4654pg/m3,平均含量为1455pg/m3。结论 本法灵敏度高,准确度好,检出限低,适用于大气中细颗粒物中双酚A的检测。成都市大气细颗粒物中双酚A的含量高于日美等发达国家以及我国北京、广州、香港,但低于印度的报道值。     

贵阳市大气颗粒物PM10、PM2.5日污染特征

于2015年秋季(1月)、夏季(8月)对贵阳市五个区的大气颗粒物(PM10、PM2.5)进行了监测研究。结果发现,贵阳市大气颗粒物污染很轻,8月和11月PM10、PM2.5的超标率都为0;大气颗粒物PM10和PM2.5浓度季节变化大,11月PM10和PM2.5浓度明显大于8月;2个月的监测数据显示5个区PM10和PM2.5日均值浓度变化趋势基本一致,5个区中南明区的PM10和PM2.5浓度最高,花溪区的PM10和PM2.5浓度最低。PM10中PM2.5比重较大,PM2.5粒径小,对人体健康危害很大。     

Evaluation of intake fractions for different subpopulations due to primary fine particulate matter (PM2.5) emitted from domestic wood combustion and traffic in Finland

Domestic wood combustion and traffic are the two most significant primary fine particulate matter (PM_2.5) emission source categories in Finland. We estimated emission–exposure relationships for primary PM_2.5 emissions from these source categories using intake fractions (iF), which describes the fraction of an emission that is ultimately inhaled by a target population. The iFs were calculated for four different emission source subcategories in Finland in 2000: (1) domestic wood combustion in residential buildings, (2) domestic wood combustion in recreational buildings, (3) traffic exhaust and wear emissions, and (4) traffic resuspension emissions. The iFs were estimated for both total population and for subpopulations with different gender, age, and educational status. Primary PM_2.5 emissions were based on the Finnish Regional Emission Scenario model and the dispersion of particles was calculated using the Urban Dispersion Modeling system of Finnish Meteorological Institute. Both emissions and dispersion were estimated on a 1 km spatial resolution. The iFs for primary PM_2.5 emissions from (1) residential and (2) recreational buildings were 3.4 and 0.6 per million, respectively. The corresponding iF for (3) traffic exhaust and wear and (4) traffic resuspension emissions were 9.7 and 9.5 per million, respectively. The differences in population-weighted outdoor concentrations were significant between subpopulations with different educational status so that people with higher education were exposed more to traffic-related PM_2.5.     

Exposure to fine particles (PM2.5 and PM1) and black smoke in the general population: personal, indoor, and outdoor levels

Personal exposure to PM(2.5) and PM(1), together with indoor and residential outdoor levels, was measured in the general adult population (30 subjects, 23-51 years of age) of Gothenburg, Sweden. Simultaneously, urban background concentrations of PM(2.5) were monitored with an EPA WINS impactor. The 24-h samples were gravimetrically analyzed for mass concentration and black smoke (BS) using a smokestain reflectometer. Median levels of PM(2.5) were 8.4 microg/m(3) (personal), 8.6 microg/m(3) (indoor), 6.4 microg/m(3) (residential outdoor), and 5.6 microg/m(3) (urban background). Personal exposure to PM(1) was 5.4 microg/m(3), while PM(1) indoor and outdoor levels were 6.2 and 5.2 microg/m(3), respectively. In non-smokers, personal exposure to PM(2.5) was significantly higher than were residential outdoor levels. BS absorption coefficients were fairly similar for all microenvironments (0.4-0.5 10(-5) m(-1)). Personal exposure to particulate matter (PM) and BS was well correlated with indoor levels, and there was an acceptable agreement between personal exposure and urban background concentrations for PM(2.5) and BS(2.5) (r(s)=0.61 and 0.65, respectively). PM(1) made up a considerable amount (70-80%) of PM(2.5) in all microenvironments. Levels of BS were higher outdoors than indoors and higher during the fall compared with spring. The correlations between particle mass and BS for both PM(2.5) vs. BS(2.5) and PM(1) versus BS(1) were weak for all microenvironments including personal exposure. The urban background station provided a good estimate of residential outdoor levels of PM(2.5) and BS(2.5) within the city (r(s)=0.90 and 0.77, respectively). Outdoor levels were considerably affected by long-range transported air pollution, which was not found for personal exposure or indoor levels. The within-individual (day-to-day) variability dominated for personal exposure to both PM(2.5) and BS(2.5) in non-smokers.     

Short-term impact of particulate matter (PM(2.5)) on respiratory mortality in Madrid

Objectives: This paper sought to quantify the particulate matter (PM_2.5) pollutant's impact on short-term daily respiratory-cause mortality in the city of Madrid.

Methods: As our dependent variable, we took daily mortality registered in Madrid from 1 January 2003 to 31 December 2005, attributed to all diseases of the respiratory system as classified under heads J00–J99 of the ICD 10 and broken down as follows: J12–J18, pneumonia; J40–J44, chronic diseases of the respiratory system except asthma; J45–J46, asthma; and J96, respiratory failure.

Results: The relative risk (RR) for daily overall respiratory mortality was RR 1.0281 (1.0043–1.0520), with a proportional attributable risk (PAR) of 2.74%. This effect occurred in lag 1; respiratory failure, RR 1.0816 (1.0119–1.1512) and PAR 7.54% at lag 5; and pneumonia, RR 1.0438 (1.0001–1.0875) and PAR 4.19% at lag 6.

Conclusions: Our results reflect the association that exists between PM_2.5 concentrations and daily respiratory-cause mortality.     

Occupational exposure to particulate matter and endotoxin for California dairy workers

Occupational exposure of dairy workers to particulate matter (PM) and endotoxin has been considered by some to be of potential concern. This paper reports personal exposure concentrations of PM (μg/m³) and endotoxin (EU/m³) for 226 workers from 13 California dairies. Arithmetic mean personal concentrations for PM_2.5, inhalable PM and endotoxin were 48 μg/m³ (N = 222), 987 μg/m³ (N = 225) and 453 EU/m³ (N = 225), respectively. Using mixed effects models, time spent re-bedding of freestall barns versus any other job conducted on a dairy led to the highest exposure for PM_2.5, inhalable PM, and endotoxin. Personal exposure concentrations were found to be greater than those reported for ambient area based concentrations at the same dairies. A pseudo R-square approach revealed that one area based measure combined with time spent performing tasks explained a significant portion of variation in personal exposure concentrations.     

Fine particulate matter (PM2.5) association with peripheral artery disease admissions in northeastern United States

Background: Current evidence, on the association of PM_2.5 and peripheral artery disease (PAD) is very sparse. Methods: We use novel PM_2.5 prediction models to investigate associations between chronic and acute PM_2.5 exposures and hospital PAD admissions across the northeast USA. Poisson regression analysis was preformed where daily admission counts in each zip code are regressed against both chronic and acute PM_2.5 exposure, temperature, socio-economic characteristics and time to control for seasonal patterns. Results: Positive significant associations were observed between both chronic and acute exposure to PM_2.5 and PAD hospitalizations. Every 10-μg/m³ increase in acute PM_2.5 exposure was associated with a 0.26 % increase in admissions (CI = 0.08 – 0.45 %) and every 10-μg/m³ increase in chronic PM _2.5 exposure was associated with a 4.4 % increase in admissions (CI = 3.50 – 5.35 %). Conclusions: The study supports the hypothesis that acute and chronic exposure to PM_2.5 can increase the risk of PAD.     

Fine particulate matter (PM2.5) air pollution and selected causes of postneonatal infant mortality in California

Studies suggest that airborne particulate matter (PM) may be associated with postneonatal infant mortality, particularly with respiratory causes and sudden infant death syndrome (SIDS). To further explore this issue, we examined the relationship between long-term exposure to fine PM air pollution and postneonatal infant mortality in California. We linked monitoring data for PM相似文献   

Reduction of wheat photosynthesis by fine particulate (PM2.5) pollution over the North China Plain

The negative impact of rapid urbanization in developing countries has led to a deterioration of urban and regional air quality. Much attention has been given to the impact of fine particulate pollution on urban public health. However, very little attention has been given to its impact on the regional ecosystem such as the agricultural ecosystem. Thus, we evaluate the direct impact of air pollution on the reduction of wheat photosynthesis by fine particulate matter (PM_2.5) pollution in the world’s most heavily polluted area, the North China Plain, using remote sensing observations and ground measurements. We found the following to be true: (1) Heavy PM_2.5 pollution could significantly reduce wheat photosynthesis and cause an expositional relationship between the PM_2.5 concentration and wheat photosynthesis (R² = 0.9824, P < 0.05); (2) Heavy PM_2.5 pollution makes up 2% for the reduction in wheat photosynthesis at all wheat-plant farmlands in the North China Plain, approximately covering an area of 354,400 km²; (3) Increasing heavy PM_2.5 pollution significantly reduced wheat photosynthesis by 87% in wheat-planted farmland during 1999–2011. We hope the results presented here could draw attention to the effect of PM_2.5 pollution on the agricultural ecosystem and encourage further studies to evaluate the feedback of atmospheric pollution on the agricultural ecosystem using remote sensing.

Abbreviation: Northern China Plain (NCP); normalized difference vegetation index (NDVI); The Moderate Resolution Imaging Spectroradiometer (MODIS); fine particulate matter (PM_2.5)