Fine particulate matter and cardiorespiratory health in China: A systematic review and meta-analysis of epidemiological studies

This review aimed to systematically summarize the epidemiological literature on the cardiorespiratory effects of PM_2.5 published during the 13^th Five-Year Plan period (2016–2020) in China. Original articles published between January 1, 2016 and June 30, 2021 were searched in PubMed, Web of Science, the China National Knowledge Internet Database and Wanfang Database. Random- or fixed-effects models were used to pool effect estimates where appropriate. Of 8558 records identified, 145 met the full eligibility criteria. A 10 µg/m³ increase in short-term PM_2.5 exposure was significantly associated with increases of 0.70%, 0.86%, 0.38% and 0.96% in cardiovascular mortality, respiratory mortality, cardiovascular morbidity, and respiratory morbidity, respectively. The specific diseases with significant associations included stroke, ischemic heart disease, heart failure, arrhythmia, chronic obstructive pulmonary disease, pneumonia and allergic rhinitis. The pooled estimates per 10 µg/m³ increase in long-term PM_2.5 exposure were 15.1%, 11.9% and 21.0% increases in cardiovascular, stroke and lung cancer mortality, and 17.4%, 11.0% and 4.88% increases in cardiovascular, hypertension and lung cancer incidence respectively. Adverse changes in blood pressure, heart rate variability, systemic inflammation, blood lipids, lung function and airway inflammation were observed for either short-term or long-term PM_2.5 exposure, or both. Collectively, we summarized representative exposure-response relationships between short- and long-term PM_2.5 exposure and a wide range of cardiorespiratory outcomes applicable to China. The magnitudes of estimates were generally smaller in short-term associations and comparable in long-term associations compared with those in developed countries. Our findings are helpful for future standard revisions and policy formulation. There are still some notable gaps that merit further investigation in China.     

桂林市流动源对大气细颗粒物的贡献研究

在桂林市2013年机动车保有量数据的基础上,参考清华大学开发的中国多尺度大气污染排放清单模型(MEIC)中的排放因子,估算流动源对大气细颗粒物的贡献。结果表明:桂林市区和桂林3大区域的流动源PM_(2.5)排放量分别为71.96和118.87t;按使用的燃料来分,柴油燃料对桂林市区和桂林3大区域PM_(2.5)贡献率较大,分别为94.07%和90.44%;按机动车类型来分,桂林市区和桂林3大区域的流动源PM_(2.5)主要贡献车型均为重型载货车、大型载客车、中型载货车,3大车型PM_(2.5)贡献率之和均超过80%。     

WATCH

Abstract

The characteristics of fine particulate pollution (PM₁₀ and PM_2.5) were measured at urban and suburban sites in Jinan during the 2008–2009 heating and non-heating seasons. The results showed that PM₁₀ and PM_2.5 pollution was quite serious, and PM mass concentration was higher during the heating season than the non-heating season. PM was the highest in the chemical factory and lowest in the development zone. The mass concentrations of PM₁₀ and PM_2.5 were linearly related, and the mass concentration ratio of PM_2.5/PM₁₀ was up to 0.59 in urban areas. PM pollution in Jinan was related to local meteorological factors: PM_2.5 mass concentration and humidity were positively correlated, and PM_2.5 mass concentration was negatively correlated with both click on the temperature and wind speed, although wind speed varied more.     

蚌埠市夏季大气细颗粒物在线源解析

为了解蚌埠市环境空气中PM_(2.5)的来源,于2017年8月18日—9月18日,在百货大楼和高新区站点,利用单颗粒物气溶胶飞行时间质谱仪开展PM_(2.5)在线源解析。结果表明,百货大楼点位ρ(PM_(2.5))高于高新区点位,轻度污染比例(4.2%)明显高于高新区点位(0.8%),出现了中度污染(0.3%);SPAMS的PM_(2.5)质谱图显示百货大楼点位PM_(2.5)中K~+、Na~+特征明显,高新区点位HSO_4~-、NO_3~-、NO_2~-等无机信号较为明显;2个点位NO_3~-、NO_2~-、NH_4~+离子颗粒数占总颗粒数的百分比明显较高,且高新区点位NO_3~-、HSO_4~-离子数占比要明显高于百货大楼点位,燃料燃烧、工业工艺源、农田氮肥施用是其主要的人为污染源;2个点位PM_(2.5)成分主要为元素碳,分别占比42.4%,40.6%;污染时段,ρ(PM_(2.5))快速上升,除受本地机动车尾气源和燃煤源累积影响外,百货大楼点位扬尘源排放增加,高新区点位扬尘源和工业工艺排放源增加;2个点位机动车尾气源均为首要污染源,分别占比29.5%和30.9%,其次为燃煤源(24.3%和24.7%),扬尘源占比分别为22.9%和20.8%。     

高分辨率卫星遥感数据在白衣庵滑坡调查研究中的应用

利用Quickbird-2卫星遥感数据对白依庵滑坡进行了调查研究。首先选取321和432波段进行彩色合成,并且与pan波段进行了分辨率融合,对融合后的图像进行正射校正作为遥感解译底图。从遥感影像上提取了白依庵滑坡的各要素,进行了稳定性评价和危害性分析,并提出了防治措施建议。根据研究结果可知,在现阶段利用Quickbird-2高分辨率卫星遥感数据进行滑坡研究具有无比的优越性、实用性和经济性。     

电生理技术在大气PM2.5污染健康风险管控中的应用

严峻的大气颗粒物污染导致的能见度下降和城镇灰色景观可能会阻碍居民压力恢复路径,损害居民身心健康。为研究大气PM_(2.5)污染对人体压力恢复的影响,基于压力恢复理论设计了一项生理心理学实验。随机招募127名被试(女性63名,男性64名),并随机分为6组(编号为G10、G30、G50、G100、G160和G260),每组被试在压力恢复阶段分别对应观看ρ(PM_(2.5))平均值分别为10,30,50,100,160和260μg/m~3的实景照片。结合压力自评量表和皮肤电、心电等电生理技术测量了被试观看不同环境ρ(PM_(2.5))实景照片时的压力恢复比例。结果显示,G10、G30、G50组被试的皮肤电水平、心率和心率变异性恢复比例约为40%～50%,50%～70%和60%～80%,G100、G160、G260组被试的皮肤电水平、心率和心率变异性恢复比例约为20%～40%,10%～40%和30%～50%,即观看ρ(PM_(2.5))低于50μg/m~3实景照片的被试在3 min内的压力恢复比例比观看ρ(PM_(2.5))高于100μg/m~3的实景照片的被试高20%以上。基于皮肤电的逐10 s压力恢复比例分析显示,2min内的清洁空气照片(G10、G30、G50组)暴露能使压力恢复至初始水平的60%左右,而暴露时间的增加可能会使被试产生疲劳和厌倦,从而导致压力恢复比例下降。指出,电生理技术是对自评量表测量的有力补充,能够客观、有效地测量压力水平变化,丰富了开展环境健康风险评估的手段,可为大气污染心理健康风险管控提供参考和借鉴。     

呼伦贝尔草原优良牧草及土壤中有机氯农药残留量调查研究

本文根据内蒙古地区草原牧草及土壤中有害物质现状调查技术规范，对著名的畜牧业基地──呼伦贝尔草原优良牧草及土壤中有机氯农药（六六六、ＤＤＴ）残留量进行了调查研究。结果表明，这两种有机氯农药在呼伦贝尔草原牧草及土壤中的残留量低于内蒙古地区的平均水平，更低于国家粮食作物及土壤卫生标准。但其化学性质稳定、脂溶性强，应停止使用。     

Sources of organic aerosol: Positive matrix factorization of molecular marker data and comparison of results from different source apportionment models

This paper presents results from positive matrix factorization (PMF) of organic molecular marker data to investigate the sources of organic carbon (OC) in Pittsburgh, Pennsylvania. PMF analysis of 21 different combinations of input species found essentially the same seven factors with distinctive source-class-specific groupings of molecular markers. To link factors with source classes we directly compare PMF factor profiles with actual source profiles. Six of the PMF factors appear related to primary emissions from sources such as motor vehicles, biomass combustion, and food cooking. Each primary factor contributed between 5% and 10% of the annual-average OC with the exception of the cooking related factor which contributed 20% of the OC. However, the contribution of the cooking factor was sensitive to the specific combinations of input species. PMF could not differentiate between gasoline and diesel emissions, but the aggregate contribution of primary emissions from these two source classes is estimated to be less than 10% of the annual-average OC. One factor appears related to secondary organic aerosol based on its substantial contribution to biogenic oxidation products. This secondary factor contributed more than 50% of the summertime average OC. Reasonable agreement was observed between the PMF results and those of a previously published chemical mass balance (CMB) analysis of the same molecular marker dataset. Individual PMF factors are correlated with specific CMB sources, but systematic biases exist between the two estimates. These biases were generally within the uncertainty of the two estimates, but there is also evidence that PMF is not cleanly differentiating between source classes.     

Characteristics of PM10, PM2.5, CO2 and CO monitored in interiors and platforms of subway train in Seoul, Korea

This study was performed to investigate the concentration of PM₁₀ and PM_2.5 inside trains and platforms on subway lines 1, 2, 4 and 5 in Seoul, KOREA. PM₁₀, PM_2.5, carbon dioxide (CO₂) and carbon monoxide (CO) were monitored using real-time monitoring instruments in the afternoons (between 13:00 and 16:00). The concentrations of PM₁₀ and PM_2.5 inside trains were significantly higher than those measured on platforms and in ambient air reported by the Korea Ministry of Environment (Korea MOE). This study found that PM₁₀ levels inside subway lines 1, 2 and 4 exceeded the Korea indoor air quality (Korea IAQ) standard of 150 μg/m³. The average percentage that exceeded the PM₁₀ standard was 83.3% on line 1, 37.9% on line 2 and 63.1% on line 4, respectively. PM_2.5 concentration ranged from 77.7 μg/m³ to 158.2 μg/m³, which were found to be much higher than the ambient air PM_2.5 standard promulgated by United States Environmental Protection Agency (US-EPA) (24 h arithmetic mean: 65 μg/m³). The reason for interior PM₁₀ and PM_2.5 being higher than those on platforms is due to subway trains in Korea not having mechanical ventilation systems to supply fresh air inside the train. This assumption was supported by the CO₂ concentration results monitored in tube of subway that ranged from 1153 ppm to 3377 ppm. The percentage of PM_2.5 in PM₁₀ was 86.2% on platforms, 81.7% inside trains, 80.2% underground and 90.2% at ground track. These results indicated that fine particles (PM_2.5) accounted for most of PM₁₀ and polluted subway air. GLM statistical analysis indicated that two factors related to monitoring locations (underground and ground or inside trains and on platforms) significantly influence PM₁₀ (p < 0.001, R² = 0.230) and PM_2.5 concentrations (p < 0.001, R² = 0.172). Correlation analysis indicated that PM₁₀, PM_2.5, CO₂ and CO were significantly correlated at p < 0.01 although correlation coefficients were different. The highest coefficient was 0.884 for the relationship between PM₁₀ and PM_2.5.