城市化对空气污染人群暴露贡献的定量方法研究

短期快速城市化引发一系列生态环境问题,尤其是近年来以细颗粒物(PM_(2.5))为代表的城市与区域空气污染问题。人群的污染暴露一方面是因为污染区范围的扩张,另外一方面则归因于城市化引发的人口迁移,目前的研究重点关注于前者的贡献,而忽略了后者的贡献。因此,建立了城市化对空气污染人群暴露贡献的定量方法,并选取我国PM_(2.5)污染最为严重的京津冀城市群开展了实证研究,通过利用2000、2005、2010、2015年PM_(2.5)浓度和人口栅格数据以及人口自然增长率数据,定量评估了城市化引发的人口迁移对空气污染人群暴露的贡献。研究结果显示:(1)京津冀地区受污染影响面积和人口变化显著,造成大量的人口暴露于PM_(2.5)污染。(2)城市化引发的人口迁移与自然增长贡献率方面:总体上,2000—2015年,京津冀城市群总的人口迁移贡献率为48%,北京市和天津市总的人口迁移贡献率分别为94%和88%,而河北省污染总的人口迁移贡献率为-32%。其中在污染保持区,北京市和天津市的人口迁移贡献率均接近100%,而河北省的迁移贡献率为-26%,尤其在2010—2015年,河北省衡水市的人口迁移贡献率达到-6613%;在污染新增区,北京市和天津市的人口迁移贡献率分别为86%和84%,而河北省污染的人口迁移贡献率为-757%。本研究建立了定量化的方法揭示了城市化在空气污染人群暴露中的定量贡献,为科学引导城市化发展提供了定量的手段,为合理规划京津冀城市群地区的人口流动与空气污染奠定了数据基础。     

中国主要城市森林对大气PM2.5的削减量及其占大气PM2.5总量比例

空气中的细颗粒物(PM_2.5)是我国城市空气污染的主要污染物之一,严重威胁着城市居民的健康,限制城市发展的可持续性。PM_2.5去除的自然途径有两种,分别是干沉降和湿沉降,其中干沉降占据主导作用,且干沉降的过程和效率与城市森林紧密关联。目前针对城市森林对干沉降作用的研究主要是在小尺度中从不同树种、不同群落结构、不同景观类型等角度来估算并比较其滞尘量,较少关注其占空气污染总量的比率,从而可能影响对城市森林滞尘服务能力的判断。因此,利用城市森林效益(Urban Forest Effect, UFORE)模型中的大气污染干沉降模块的核心算法,以2015年为例,估算了我国主要城市辖区的城市森林一年内对大气中的PM_2.5削减量以及其占空气中PM_2.5污染总量的比重。结果显示：(1)2015年全国主要城市单位绿地面积日均滞尘量较高地区主要集中在华北地区、华东地区、以及东北地区。其中北京30.47mg/m²,苏州24.63mg/m²,沈阳28.55mg/m²     

大气颗粒物(PM2.5、PM10)对地表景观结构的响应研究进展

颗粒物PM_(2.5)、PM_(10)是近年来我国大气首要污染物,威胁环境和人类健康。地表景观结构直接或间接影响PM_(2.5)、PM_(10)浓度,了解其影响过程和机理对于改善生态环境具有重要意义。系统总结了国内外关于PM_(2.5)、PM_(10)对地表景观结构响应的研究成果,指出研究中出现不确定性的可能影响因素,并对今后的发展方向进行展望。得出基本结论:(1)地表景观类型的构成及其格局显著影响大气颗粒物浓度,对PM_(2.5)、PM_(10)起到"源"和"汇"的作用。(2)地表景观结构引起局地气候变化并影响颗粒物的迁移转化,但其影响过程和机理复杂,研究结论并不明确。(3)颗粒物浓度和地表景观数据主要通过实际监测或遥感处理方法获得,但因为获取方法、监测点微观环境及遥感影像等因素影响,导致数据具有不确定性,加上时空尺度相对应的复杂性,大大限制了地表景观结构与PM_(2.5)、PM_(10)响应关系的研究进展,是未来要突破的难点。(4)PM_(2.5)、PM_(10)对地表景观结构响应的区域时空差异及过程,局地小气候变化对颗粒物浓度的影响过程和强度,主要景观类型尤其是水体、湿地景观对大气颗粒物浓度的影响过程、机理与贡献程度等是未来需要关注的方向。     

不同污染程度下毛白杨叶表面PM2.5颗粒的数量及性质和叶片气孔形态的比较研究

选择了北京市环境PM_(2.5)浓度不同的两个采样点的毛白杨(Populus tomentosa Carr.)作为研究对象,利用环境扫描电镜及X-射线能谱仪对杨树叶片表面滞留的PM_(2.5)颗粒进行了观察、统计和成分分析,并研究了叶片气孔对环境颗粒物污染的适应性变化。结果表明:夏秋两季西直门叶片样品上下表面的PM_(2.5)数量均多于森林公园样品这说明环境PM_(2.5)浓度是影响叶片表面滞留颗粒物数量的主要原因;其中叶片上表面是滞留PM_(2.5)颗粒的主要区域。森林公园样品中PM_(2.5)颗粒性质比较单一,硅铝酸盐颗粒和石英颗粒占很大比例,二者的主要来源均为天然源,如土壤扬尘、矿物颗粒等;而西直门采样点叶片样品滞留的PM_(2.5)颗粒的元素组成更为复杂,其中50%以上的硅铝酸盐颗粒检测出了明显的铜、钾、氯、钠等元素的谱峰其来源主要是工业排放;西直门样品PM_(2.5)的含硫量高于森林公园样品,且夏季明显高于秋季。研究还发现有少数PM_(2.5)颗粒进入了毛白杨叶片的气孔而且不同污染程度下气孔的形态特征存在差异。与森林公园毛白杨叶片的气孔相比,西直门处的毛白杨叶片气孔的长度、宽度、面积和气孔密度均较小,说明较高的PM_(2.5)污染程度对毛白杨叶片的形态发育有一定影响。研究结果可以为揭示植物叶片阻滞、吸收大气颗粒污染物的机制、合理选择和优化城市绿化树种从而改善空气质量提供一定的科学理论依据。     

Urban atmospheric environmental capacity and atmospheric environmental carrying capacity constrained by GDP–PM2.5

China faces a contradictory period of economic development and environmental protection, with it being essential to control total emissions within the limit of atmospheric environmental capacity (AEC) by promoting atmospheric environmental carrying capacity (AECC). This implies that well-calculated AEC and AECC values are the key macro-criteria for improving environmental quality and supporting the challenging coordinated development of economy and environment. When considering compound air pollution characterised as fine particulate matter (PM_2.5), conventional methods are not capable of calculating AEC and AECC, but the system dynamics (SD) method retains the advantage of simplicity in resolving complex problems. In the present study, we first describe the background and definitions of AEC and AECC, which are different from Western concepts, and their dialectical relationships. Then, with the statistical data from Wuhan city in 2014, we establish an ‘economy–energy–atmospheric environment’ dynamic model using the SD method, which does not need to simulate the complicated physicochemical processes of atmospheric transmission and diffusion. Instead, it uses the pollutants’ proportionality factors and conversion rates to establish quantitative connections among different types of variables. Finally, we simulate the dynamic trends of gross domestic production (GDP), PM_2.5, and six air pollutant emissions between 2015 and 2030 in four different scenarios and calculate the results of AEC and AECC constrained by GDP and PM_2.5.     

Environmental Risk Assessment: Comparison of Receptor and Air Quality Models for Source Apportionment

Source apportionment of particulate matter has been commonly performed using receptor models, but studies suggest that the assumptions in receptor models limit the accuracy of results. An alternative approach is the use of three-dimensional source-oriented air quality models. Here, a comparison is conducted between the PM_2.5 apportioned from the Chemical Mass Balance (CMB) receptor model using organic tracers as molecular markers with those from the source-based Community Multiscale Air Quality (CMAQ) model. Source apportionment was conducted at sites in the southeastern United States for July 2001 and January 2002. PM_2.5 source apportionment results had moderate discrepancies, which originate from different spatial scales, fundamental limitations, and uncertainties of the two models. Results from CMB fluctuated temporally more than real variation due to measurement and source profile errors and uncertainties, whereas those from CMAQ could not capture daily variation well. In addition, results from CMB are mass contributions for the monitoring location, whereas those from CMAQ represent the average mass contributions of the model's grid. It is difficult to assess which approach is “better.” Indeed, both models have strengths and limitations, and each model's strengths can be utilized to help overcome the other model's limitations.     

粤港澳大湾区PM2.5时空分布特征及其与气象要素的关系

细颗粒物（PM_2.5）污染不仅是现代社会城市化进程中的痛点,也是城市大气环境研究不可忽略的重要焦点。粤港澳大湾区作为世界级城市群,既是城市区域经济社会文化发展的重要体现,更是国家区域发展战略的重要构成与政策实施落脚点,其生态环境的优劣尤其受瞩目。对1999-2016年大湾区地表PM_2.5浓度栅格数据集进行了时空分布特征分析,其中空间自相关分析选取莫兰指数（Moran''I指数）作为度量;并利用多元线性回归模型探讨研究区内PM_2.5与气象要素之间关系。结果表明：粤港澳大湾区1999-2016年历年PM_2.5浓度呈先增加后减小的趋势,2008年为时间拐点,该时间节点之后空气质量显著提高,且1999、2009、2016三年,年平均PM_2.5浓度相似值趋于聚集分布。冷热点分析结果表明：热点区域集中于湾区行政核心区域范围内;冷点集中于核心边缘区域,空气质量较优。利用皮尔森相关分析最终筛选出实际蒸散量（aet）、太阳辐射（srad）、最低温度（tmmn）、蒸汽压（vap）、饱和水汽压差（vpd）、风速（ws）等6个气象因子,利用回归分析判断影响PM_2.5浓度时空分布的显著因子。结果表明：本研究区太阳辐射与PM_2.5浓度关系呈负相关,该结果与其他城市相关研究有较大差异,最小温度与PM_2.5浓度呈正相关,风速与PM_2.5浓度呈负相关,饱和水气压差与PM_2.5浓度呈正相关。     

Risk Assessment as a Decision-Making Tool for Treatment of Emissions at a New Aluminum Smelter in Iceland: 2. Human Health Risk Assessment

Alcoa recently constructed the Fjarðaál aluminum smelter in Reyðarfjörður, East Iceland. The smelter is designed to produce a maximum of 346,000 metric tons per year of aluminum. A risk assessment was conducted to evaluate the differential human health risk related to estimated potential air emissions from the planned Fjarðaál smelter with and without seawater scrubbers. Air-dispersion modeling results provided for particulate matter (PM₁₀), sulfur dioxide (SO₂), hydrogen fluoride (HF), and polycyclic aromatic hydrocarbons (PAHs) were compared to ambient air standards or air quality guidelines from Norway, Iceland, or European directives and from the U.S. Environmental Protection Agency. Risk estimates were calculated for PAHs. Modeled air estimates were mapped geospatially, to identify potential receptors, including onsite outdoor worker, seagoing worker, hypothetical fence-line resident, future hypothetical resident, closest current resident, residents in neighboring villages, closest farmer, and a visitor to the nearby Holmanes Nature Reserve. Both with and without seawater scrubbers, the predicted exceedances of standards per year for SO₂ were well below the maximum number allowed. Use of seawater scrubbers was predicted to decrease average SO₂ air concentration estimates in the short term; however, annual estimates were lower without seawater scrubbers. Risk estimates for carcinogenic and non-carcinogenic PAHs, and modeled air concentrations of HF and PM₁₀, were well within acceptable levels.     

地表植被景观对PM2.5浓度空间分布的影响研究

我国当前城市日益频发的雾霾问题引发公众广泛关注,PM_2.5被认为是雾霾的主要成因。研究认为,在某一区域短时间尺度上（如日）,PM_2.5浓度主要受气象条件影响。但在较长时间尺度上（如季,年）,由于气象条件基本相似,则PM_2.5浓度主要受土地利用特别是地表植被景观的影响。如何耦合地表植被景观格局与PM_2.5浓度信息,定量分析其影响是当前相关科学研究的一个难点,需要引入新思路。首先基于季节气象条件基本相似的科学假设,采用土地利用回归模型分四季高精度模拟PM_2.5浓度空间分布。其次,采用像元二分模型分四季估算研究区植被覆盖度。在此基础上采用随机抽样法通过统计回归模型耦合植被覆盖度与PM_2.5空间分布,定量研究植被覆盖度对PM_2.5分布影响及其尺度效应。研究结果表明：1）植被覆盖度与PM_2.5浓度在本研究选择的空间尺度上,都显著负相关,说明植被覆盖度对PM_2.5具有显著影响;同一个季节不同尺度上,以及不同季节同一尺度上的植被覆盖度对PM_2.5浓度的影响存在一定差异。2）植被覆盖度对PM_2.5浓度的影响方式比较复杂,不同的季节的表现方式不同,总体来说PM_2.5浓度与植被覆盖度曲线回归模型的拟合度高于线性回归模型,说明植被覆盖度对PM_2.5的影响具有非线性特征。3）不同的PM_2.5浓度水平下,植被覆盖度对PM_2.5浓度的影响程度存在差异。PM_2.5浓度越高,植被覆盖度对其浓度的影响越明显。本研究提出的区域尺度耦合地表植被覆盖与PM_2.5浓度的思路与方法,有效的揭示了植被覆盖度对PM_2.5浓度分布的影响方式与尺度效应,为通过优化城市植被缓解大气污染提供一定参考。     

Effects of Urban Landscape Pattern on PM2.5 Pollution—A Beijing Case Study

PM_2.5 refers to particulate matter (PM) in air that is less than 2.5μm in aerodynamic diameter, which has negative effects on air quality and human health. PM_2.5 is the main pollutant source in haze occurring in Beijing, and it also has caused many problems in other cities. Previous studies have focused mostly on the relationship between land use and air quality, but less research has specifically explored the effects of urban landscape patterns on PM_2.5. This study considered the rapidly growing and heavily polluted Beijing, China. To better understand the impact of urban landscape pattern on PM_2.5 pollution, five landscape metrics including PLAND, PD, ED, SHEI, and CONTAG were applied in the study. Further, other data, such as street networks, population density, and elevation considered as factors influencing PM_2.5, were obtained through RS and GIS. By means of correlation analysis and stepwise multiple regression, the effects of landscape pattern on PM_2.5 concentration was explored. The results showed that (1) at class-level, vegetation and water were significant landscape components in reducing PM_2.5 concentration, while cropland played a special role in PM_2.5 concentration; (2) landscape configuration (ED and PD) features at class-level had obvious effects on particulate matter; and (3) at the landscape-level, the evenness (SHEI) and fragmentation (CONTAG) of the whole landscape related closely with PM_2.5 concentration. Results of this study could expand our understanding of the role of urban landscape pattern on PM_2.5 and provide useful information for urban planning.     

基于城市绿地滞尘模型的上海市绿色空间滞留PM2.5功能评估

绿色空间对大气颗粒物有一定吸收滞留作用,是改善空气环境质量与维护城市生态安全的重要区域。该文基于高分2号卫星影像识别2017年上海市绿色空间,并利用城市绿地滞尘模型,结合上海市降水、风速等气象数据与空气质量监测数据,评估了绿色空间滞留PM_(2.5)功能及其差异。结果表明:2017年上海市绿色空间面积3354 km~2,可滞留PM_(2.5) 3533 t,约合单位面积滞留PM_(2.5) 10.5 kg hm~(-2) a~(-1)。从绿色空间类型来看,林地滞留PM_(2.5)能力最强,可达20.2 kg hm~(-2) a~(-1),远高于草地9.1 kg hm~(-2) a~(-1)和农田8.7 kg hm~(-2) a~(-1)的滞留能力。从季节差异来看,绿色空间夏季滞留PM_(2.5)能力最高,然后依次为秋季、春季和冬季。从植被分布格局来看,林草地和农田吸滞PM_(2.5)的能力随植被盖度的增加而提高。在区域差异上,崇明区绿色空间滞留PM_(2.5)能力最高,其余地区呈现出中心城区低、周边高的趋势。为此建议上海市重点优化中心城区的绿色空间格局,增植立体绿化与高滞尘能力树种。     

哈密露天煤矿不同环境介质微生物群落特征分析

【背景】干旱区露天煤矿开采过程中产生的粉尘颗粒物加剧了土壤生态环境的恶化和矿区空气质量的下降,针对煤矿区土壤和粉尘颗粒物的微生物群落组成的研究鲜有报道。【目的】研究新疆哈密南湖乡露天煤矿土壤、粉尘及大气PM_2.5颗粒物中的微生物群落结构和多样性特征,并预测潜在的功能类群。【方法】采用高通量测序技术,对煤矿露天采坑区和电厂粉煤灰堆放区的土壤、粉尘及大气PM_2.5颗粒物的微生物真菌及细菌群落组成进行比对分析。【结果】矿区优势真菌类群来自子囊菌门(Ascomycota)和担子菌门(Basidiomycota),优势细菌类群来自变形菌门(Proteobacteria)和放线菌门(Actinobacteria)。真菌和细菌群落的丰富度及α多样性在整个矿区内无显著性差异,大气PM_2.5颗粒物的细菌群落生态位宽度显著大于露天采坑区和粉煤灰区。矿区内的土壤和PM_2.5颗粒物样本中均发现了一些丰度差异显著的功能类群,真菌特征功能类群为腐生营养型类群,细菌特征功能类群主要包括甲烷营养型类群、几丁质酶类细菌类群等。【结论】露天煤矿区粉尘可能对区域内土壤和PM_2.5颗粒物的微生物群落结构产生重要影响,具有煤组分降解功能的特定微生物类群可能是维持矿区土壤生态安全的重要微生物学机制之一。     

寒地校园植物群落对大气细颗粒物浓度的消减作用

近年来随着我国城市的快速发展，空气污染作为城市生态环境破坏的首要问题日益严重。因此探究城市绿地中的植物群落是否能够消减大气细颗粒物浓度及其变化特征成为公众和学者广泛关注的焦点。选取位于寒地城市哈尔滨的东北林业大学作为研究对象，通过对校园PM_2.5浓度进行测定和校园植物群落进行调查，定量地分析不同植物群落对PM_2.5浓度的消减作用、PM_2.5浓度的时间变化规律、PM_2.5浓度与温度和空气相对湿度之间的关系。结果显示：（1）PM_2.5浓度日变化呈"双峰单谷"型，早晚偏高；季节变化是夏季PM_2.5浓度最低，秋季PM_2.5浓度最高。（2）不同结构的植物群落对大气细颗粒物的削减效果略有差异，乔灌草配置型绿地的PM_2.5消减率为30.30%，消减效果最佳；乔草和灌草的PM_2.5消减率分别为14.30%和7.77%，消减效果较差。（3）PM_2.5浓度与温度呈负相关关系，与空气相对湿度呈正相关关系。     

Anemochory of diapausing stages of microinvertebrates in North American drylands

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Temperature modifies the health effects of particulate matter in Brisbane, Australia

A few epidemiological studies have examined whether there was an interactive effect between temperature and ambient particulate matter on cardiorespiratory morbidity and mortality, but the results were inconsistent. The present study used three time-series approaches to explore whether maximum temperature modified the impact of ambient particulate matter less than 10 μm in diameter (PM₁₀) on daily respiratory hospital admissions, cardiovascular hospital admissions, respiratory emergency visits, cardiovascular emergency visits, non-external cause mortality and cardiovascular mortality in Brisbane between 1996 and 2001. The analytical approaches included a bivariate response surface model, a non-stratification parametric model and a stratification parametric model. Results show that there existed a statistically significant interaction between PM₁₀ and temperature on most health outcomes at various lags. PM₁₀ exhibited more adverse health effects on warm days than cold days. The choice of the degree of freedom for smoothers to adjust for confounders and the selection of arbitrary cut-offs for temperature affected the interaction estimates to a certain extent, but did not change the overall conclusion. The results imply that it is important to control and reduce the emission of air particles in Brisbane, particularly when temperature increases.     

Impact of novel coronavirus disease (COVID-19) lockdown on ambient air quality of Saudi Arabia

The outbreak of COVID-19 has spread globally affecting human activities but with improvement in ambient air quality. The first case of the virus in the Kingdom of Saudi Arabia was on the 2nd of March 2020. The impact of COVID-19 lockdown on the ambient air quality of the Kingdom of Saudi Arabia for the first time using data from nine cities was determined in this study. Hourly air quality data, based on concentrations of carbon monoxide (CO), particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and ozone (O₃), and meteorological conditions (atmospheric temperature, relative humidity, and wind speed) of the nine cities studied were obtained from Saudi Arabian General Authority of Meteorology and Environmental Protection (GAMEP), for the period between January 2019 to May 2020. Significant variation (p < 0.05) was recorded for the five atmospheric pollutants across the cities before and during the lockdown, with lower concentrations during the lockdown except for the concentration of O₃ in Tabuk, Al Qasim, and Haql. This can be a result of NO and O₃ reaction, causing the inability of effective O₃ depletion. The percentage changes in concentrations of CO (33.60%) and SO₂ (44.16%) were higher in Jeddah; PM₁₀ (91.12%) in Riyadh, while NO₂ (44.35%) and O₃ (18.98%) were highest in Makkah. However, even though there was a decrease in pollutants concentrations during the lockdown, the concentrations for CO, PM₁₀, SO₂, NO_2, and O₃ were still above WHO 24 h and annual mean limit levels. The COVID-19 lockdown in the Kingdom of Saudi Arabia revealed the possibility of significant atmospheric pollutant reduction by controlling traffic, activities by industries, and environmentally friendly transportation programs such as green commuting programs.     

Particulate matter and bacteria characteristics of the Middle East Dust (MED) storms over Ahvaz,Iran

The Middle East Dust storms have greatly affected the south and west parts of Iran during the last decade. The main purpose of this study was to examine and compare culturable airborne bacteria concentration in particulate matter (PM) during normal, semi-dust, and dust event days in different places and seasons in Ahvaz from November 2011 to May 2012. Sampling was performed every 6 days and on dust event days at different sampling stations. The overall mean concentrations of PM₁₀, PM_2.5, and PM₁ for the entire study period were 598.92, 114.8, and 34.5 μg/m³, respectively. The PM concentrations during the dust event days were much higher than normal and semi-dust event days. The highest mean PM concentrations were observed in March 2011. The low PM_2.5/PM₁₀ ratios indicate that these PM are mostly originating from natural sources such as dust storms. The overall mean concentration of total bacteria during the study period was 620.6 CFU/m³. The greatest bacterial concentrations were observed during dust event days and at areas with high traffic and more human activities compared with normal days and greener areas. The percentage of gram-positive bacteria was significantly higher than that during the study period (89 vs 11 %). During this study, 26 genera of culturable bacteria were identified from all the sampling stations. The most dominant genera in all sampling stations were Streptomyces, Bacillus, Kocuria, Corynebacterium, and Paenibacillus. The results also showed that there were positive correlations between PM and bacterial concentrations during the study period (p < 0.05).     

Fine Particulate Air Pollution and Hospital Emergency Room Visits for Respiratory Disease in Urban Areas in Beijing,China, in 2013

BackgroundHeavy fine particulate matter (PM_2.5) air pollution occurs frequently in China. However, epidemiological research on the association between short-term exposure to PM_2.5 pollution and respiratory disease morbidity is still limited. This study aimed to explore the association between PM_2.5 pollution and hospital emergency room visits (ERV) for total and cause-specific respiratory diseases in urban areas in Beijing.MethodsDaily counts of respiratory ERV from Jan 1 to Dec 31, 2013, were obtained from ten general hospitals located in urban areas in Beijing. Concurrently, data on PM_2.5 were collected from the Beijing Environmental Protection Bureau, including 17 ambient air quality monitoring stations. A generalized-additive model was used to explore the respiratory effects of PM_2.5, after controlling for confounding variables. Subgroup analyses were also conducted by age and gender.ResultsA total of 92,464 respiratory emergency visits were recorded during the study period. The mean daily PM_2.5 concentration was 102.1±73.6 μg/m³. Every 10 μg/m³ increase in PM_2.5 concentration at lag₀ was associated with an increase in ERV, as follows: 0.23% for total respiratory disease (95% confidence interval [CI]: 0.11%-0.34%), 0.19% for upper respiratory tract infection (URTI) (95%CI: 0.04%-0.35%), 0.34% for lower respiratory tract infection (LRTI) (95%CI: 0.14%-0.53%) and 1.46% for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) (95%CI: 0.13%-2.79%). The strongest association was identified between AECOPD and PM_2.5 concentration at lag_0-3 (3.15%, 95%CI: 1.39%-4.91%). The estimated effects were robust after adjusting for SO₂, O₃, CO and NO₂. Females and people 60 years of age and older demonstrated a higher risk of respiratory disease after PM_2.5 exposure.ConclusionPM_2.5 was significantly associated with respiratory ERV, particularly for URTI, LRTI and AECOPD in Beijing. The susceptibility to PM_2.5 pollution varied by gender and age.     

城市机动车道颗粒污染物扩散对绿化隔离带空间结构的响应

通过设置绿化隔离带以减少道路污染物的扩散是目前采用的道路生态防控措施之一。如何减少道路污染对机动车道行人的影响等问题则少有关注。基于对城市非机动车道行人生态安全的考虑,采取现场模拟方法研究道路绿化隔离带对道路中心颗粒污染物向非机动车道扩散的影响。 试验地点选择太原市城区,具体方法是在机动车道和非机动车道之间设置遮阳网。通过增减遮阳网的层数调整模拟隔离带的宽度和疏透度,每50 cm间隔1层遮阳网。为了更接近实际的绿化隔离带和更利于调整疏透度,近机动车道一侧的遮阳网均匀捆扎新鲜的离体植物枝条。实验采用三因素、四水平正交设计方法,通过测定绿带两侧污染物含量的变化评价绿带对非机动车道的净化效应。结果表明,道路绿化隔离带对于减少道路颗粒污染物向非机动车道的扩散具有明显的作用。道路绿化隔离带的诸因子净化非机动车道环境的作用有明显的差异。其中疏透度和高度所起的作用较为显著。本试验中净化PM₁₀过程中三因子的重要性排序依次为高度、疏透度、宽度。净化TSP过程中的重要性依次为疏透度、高度、宽度。三因子净化非机动车道颗粒污染物的最佳水平分别是:疏透度20%,高度350 cm,宽度250 cm,对TSP和PM₁₀的净化百分率分别达到46.10%和42.94%。各因子不同水平的净化效应排序分别为:疏透度20%、30%、40%、10% ,高度350 cm、250 cm、150 cm、50 cm,宽度250 cm、350 cm、150 cm、50 cm。     

Clean air actions in China,PM2.5 exposure,and household medical expenditures: A quasi-experimental study

BackgroundExposure to air pollution, a leading contributor to the global burden of disease, can cause economic losses. Driven by clean air policies, the air quality in China, one of the most polluted countries, has improved rapidly since 2013. This has enabled a unique, quasi-experiment to assess the economic impact of air pollution empirically.Methods and findingsUsing a series of nation-scale longitudinal surveys in 2011, 2013, and 2015, we first examined the questionnaire-based medical expenditure changes before and after the policy intervention for air pollution. Using a state-of-the-art estimator of the historical concentration of particulate matters with diameter less than 2.5 μm (particulate matter (PM)_2.5), we further quantified the association between household medical expenditure and PM_2.5 using mixed-effect models of the repeated measurements from 26,511 households in 126 cities. Regression models suggest a robust linear association between reduced PM_2.5 and saved medical expenditures, since the association did not vary significantly across models with different covariate adjustments, subregions, or subpopulations. Each 10 μg/m³ reduction in PM_2.5 was associated with a saving of 251.6 (95% CI: 30.8, 472.3; p-value = 0.026) Yuan in per capita annual medical expenditure. However, due to limitations in data quality (e.g., self-reported expenditures), and imperfect control for unmeasured confounders or impact from concurrent healthcare reform in China, the causality underlying our findings should be further confirmed or refuted.ConclusionIn this study, we observed that compared with the PM_2.5 reduction in 2013, the PM_2.5 reduction in 2017 was associated with a saving of 552 (95% CI: 68, 1036) Yuan / (person × year), or approximately 736 billion Yuan (equivalent to 111 billion US dollar) per year nationally, which is equivalent to approximately 1% of the national gross domestic product of China.

In this observational study, Tao Xue and colleagues explore associations between medical expenses and changes to clean air policies in China since 2013.