青藏高原东缘冬季PM2.5中碳组分特征和来源解析

于2017年冬季12月13—21日在青藏高原东缘理塘地区分昼夜采集PM_2.5样品,并用DRI2001A热光碳分析仪测定了有机碳(OC)和元素碳(EC)的质量浓度,研究青藏高原PM_2.5中碳组分的化学特征及主要来源,以期为理塘地区制定污染排放政策提供参考。结果表明,2017年冬季青藏高原东缘理塘地区PM_2.5平均质量浓度为44.34μg·m^?3,OC和EC的质量浓度为12.72μg·m^?3和3.85μg·m^?3,分别占PM_2.5质量浓度的29.61%和8.96%。通过经验公式,计算得到总碳气溶胶(TCA)质量浓度为24.20μg·m^?3,占PM_2.5的54.84%,说明碳质气溶胶对青藏高原东缘理塘地区PM_2.5有着十分重要的贡献。OC和EC在白天和夜间都有较高的相关性(相关系数分别为0.74和0.91),表明OC和EC的来源基本一致,受燃烧源影响较大。其中白天的相关系数低于夜间,说明青藏高原东缘理塘地区白天碳组分来源相对复杂。昼夜浓度对比显示,青藏高原东缘理塘地区PM_2.5白天和夜间的质量浓度分别为53.88μg·m^?3和33.44μg·m^?3,OC和EC浓度白天高于夜间,表明白天人为排放相对较高。冬季观测期间,PM_2.5中二次有机碳(SOC)昼夜浓度分别为1.11μg·m^?3和3.03μg·m^?3,分别占OC质量浓度的7.09%、26.59%,表明青藏高原东缘理塘城区白天碳组分主要为一次源。利用PMF 5.0软件对理塘城区碳组分进行进一步的解析,结果显示燃煤和生物质燃烧的混合源对总碳(TC)的贡献高达47.84%,占比最高;其次是汽车尾气和柴油车尾气源,贡献率分别为28.62%和23.54%。     

中国典型沿海城市冬季PM2.5中碳组分的污染特征及来源解析

为研究中国典型沿海城市冬季PM_2.5中碳组分的污染特征及来源,于2018年12月5日—2019年1月30日分别在天津(TJ)、上海(SH)和青岛(QD)同步采集PM_2.5样品。结果表明,天津、上海和青岛PM_2.5的平均浓度分别为(116.96±66.93)、(31.21±25.62)、(74.93±54.60)μg·m^-3,OC和EC的空间分布均为天津(18.69±7.95)μg·m^-3和(4.98±2.08)μg·m^-3>青岛(16.45±8.94)μg·m^-3和(2.01±1.04)μg·m^-3>上海(7.28±3.11)μg·m^-3和(1.05±1.25)μg·m^-3。3个站点的OC和EC均呈现较好的相关性,表明OC和EC具有相似的来源;OC/EC比值范围在2.37—7.53、5.47—46.41和4.77—13.36之间,证明各采样点均存在二次有机碳(SOC)的生成;采用最小R²法(MRS)估算SOC浓度,得到3个采样点SOC的平均质量浓度为(5.09±4.68)、(3.90±1.65)、(4.21±4.31)μg·m^-3,分别占OC总量的27.2%、55.8%和19.5%,其中上海的SOC在OC中的占比最大,说明上海二次有机碳污染较为严重,这主要归因于冬季严重污染源排放和有利的二次转化气象条件,而天津和青岛的碳组分主要来自污染源的直接排放。主成分分析(PCA)结果发现,天津PM_2.5中碳组分主要来源于道路尘、生物质燃烧和机动车尾气,上海PM_2.5中碳组分主要来源于生物质燃烧、道路扬尘和机动车尾气。青岛PM_2.5中碳组分主要来源于道路扬尘、机动车尾气。后向轨迹聚类分析表明,来自西北方向的气团对天津的影响较大,PM_2.5和碳组分的浓度值最大;而对上海而言,主要受北方气溶胶经过海面又传输回上海的气团的影响;青岛站点主要受华北地区污染物和本地排放源的影响。     

天津市夏季PM2.5中碳组分时空变化特征及来源解析

为研究天津市夏季PM_2.5中碳组分的时空变化特征及来源,于2019年7—8月设立2个点位分昼夜采集天津市PM_2.5样品,并测定了其中有机碳(OC)和元素碳(EC)的含量。结果表明,城区PM_2.5、OC和EC浓度日均值分别为(53.4±20.8)μg·m^-3、(8.72±2.56)μg·m^-3和(1.67±0.90)μg·m^-3,郊区PM_2.5、OC和EC浓度日均值分别为(54.2±24.5)μg·m^-3、(7.54±2.50)μg·m^-3和(1.82±1.06)μg·m^-3;白天PM_2.5、OC、EC的平均浓度分别为(47.3±16.1)μg·m^-3、(8.7±2.1)μg·m^-3和(1.5±0.6)μg·m^-3,夜间PM_2.5、OC、EC的平均浓度分别为(60.2±26.2)μg·m^-3、(7.5±2.9)μg·m^-3和(2.0±1.2)μg·m^-3。OC浓度表现为城区高于郊区,白天高于夜间;EC及PM_2.5浓度表现为郊区高于城区,夜间高于白天。OC/EC比值分析得,城区(6.04)高于郊区(5.08);白天(6.58)高于夜间(4.54)。城区OC与EC相关性弱于郊区,白天OC与EC相关性弱于夜间。采用EC示踪法与MRS模型对SOC含量进行估算,得到白天与夜间SOC浓度分别为(5.71±1.35)μg·m^-3和(3.81±1.20)μg·m^-3,白天SOC污染比夜间严重。丰度分析与主成分分析的结果表明,天津市夏季城郊区PM_2.5中碳组分均主要来源于燃煤和机动车尾气排放。     

川南四座城市PM2.5化学组分污染特征及其源解析

为探究川南地区大气气溶胶中化学组分与来源特征,于2015年9月—2016年8月在四川盆地南部4个典型代表城市(泸州、内江、宜宾、自贡)采集了226个PM_2.5样品,对PM_2.5的质量浓度和主要化学组分(水溶性离子和碳质组分)进行测定,并利用颗粒物源解析受体模型对PM_2.5来源进行解析.结果表明:川南地区PM_2.5日均浓度为46.4—68.0μg·m^-3,均高于国家环境空气质量标准年均PM_2.5限值(35.0μg·m^-3).OC、EC和水溶性二次离子(SO₄^2-、NO₃-和NH₄+)分别占PM_2.5质量的15.7%—22.8%、4.2%—6.4%和28.6%—55.8%.PM_2.5及其主要化学组分浓度有显著的季节变化,即冬季浓度显著高于其他季节,夏季浓度最低.泸州除夏季外,其他季节SO₄2-、NO₃-同源性较好;其他城市在冬季,SO₄2-、NO₃-同源性较好.NH₄+主要存在形式为NH₄NO₃、(NH₄)2SO₄、NH₄HSO₄.OC、EC来源复杂,主要为机动车源、煤燃烧源和生物质燃烧源.川南地区PM_2.5的来源主要受8种因子影响,按总体贡献排序依次为:二次硫酸盐、生物质燃烧、工业源、二次硝酸盐、机动车源、煤燃烧、道路尘埃和建筑尘埃.此外,相比较而言,机动车源贡献在泸州市较凸显,煤燃烧源贡献在宜宾市较凸显.     

Relations between indoor and outdoor PM2.5 and constituent concentrations

Factors impacting indoor-outdoor relations are introduced. Sulfate seems a fine tracer for other non-volatile species. Particulate nitrate and ammonium desorb during outdoor-to-indoor transport. OC load increases during the transport due to sorption of indoor SVOCs. Outdoor PM_2.5 influences both the concentration and composition of indoor PM_2.5. People spend over 80% of their time indoors. Therefore, to assess possible health effects of PM_2.5 it is important to accurately characterize indoor PM_2.5 concentrations and composition. Controlling indoor PM_2.5 concentration is presently more feasible and economic than decreasing outdoor PM_2.5 concentration. This study reviews modeling and measurements that address relationships between indoor and outdoor PM_2.5 and the corresponding constituent concentrations. The key factors in the models are indoor-outdoor air exchange rate, particle penetration, and deposition. We compiled studies that report I/O ratios of PM_2.5 and typical constituents (sulfate (SO₄^2-), nitrate (NO₃^-), ammonium (NH₄⁺), elemental carbon (EC), and organic carbon (OC), iron (Fe), copper (Cu), and manganese (Mn)). From these studies we conclude that: 1) sulfate might be a reasonable tracer of non-volatile species (EC, Fe, Cu, and Mn) and PM_2.5 itself; 2) particulate nitrate and ammonium generally desorb to gaseous HNO₃ and NH₃ when they enter indoors, unless, as seldom happens, they have strong indoor sources; 3) indoor-originating semi-volatile organic compounds sorb on indoor PM_2.5, thereby increasing the PM_2.5 OC load. We suggest further studies on indoor-outdoor relationships of PM_2.5 and constituents so as to help develop standards for healthy buildings.     

Long-term trends of fine particulate matter and chemical composition in the Pearl River Delta Economic Zone (PRDEZ), China

Understanding the trends in PM_2.5 levels is essential for formulating clean air plans. This paper analyzes PM_2.5 data from various published sources for the years 2000 to 2010 in the Pearl River Delta Economic Zone (PRDEZ). The long-term variation in PM_2.5 mass concentration is analyzed. Results show that PM_2.5, organic carbon (OC), elemental carbon (EC), and SO42− show a similar trend, increasing before 2005 and then decreasing slightly. The annual average PM_2.5 concentration ranges from 49.1 μg·m⁻³ in 2000 to 64.3 μg·m⁻³ in 2010, with a peak of 84.1 μg·m⁻³ in 2004. None of these 11 years meets the new National Ambient Air Quality standard (NAAQS) for PM_2.5 (35 μg·m⁻³). Overall average concentrations of OC, EC, and SO42− are 13.0, 6.5, and 11.8 μg·m⁻³, respectively. NO3− and NH4+ respectively have concentrations of 1.5 μg·m⁻³ and 2.9 μg·m⁻³ in 2000 and 6.4 μg·m⁻³ and 5.3 μg·m⁻³ in 2010, with a statistically significant average annual trend of+ 0.2 μg·m⁻³·yr⁻¹ and+ 0.1 μg·m⁻³·yr⁻¹. In certain geographic regions, OC and EC contribute most of the PM_2.5, while in other regions secondary water-soluble ions are more important. In general, OC and SO42− are the dominant components of PM_2.5, contributing 20.6% and 18.6%, respectively. These results provide, for the first time, a better understanding of the long-term PM_2.5 characteristics and trends, on a species-by-species basis, in the PRDEZ. The results indicate that PM_2.5 abatement needs to prioritize secondary species.     

天津市采暖季PM2.5组分消光特性及来源分析

为了解天津市采暖季细颗粒物组分对能见度的影响、明确消光组分来源,对天津市2017年采暖季大气PM_2.5样品进行了为期一月的连续采集,并测定水溶性离子、有机碳和元素碳的含量,通过修正IMPROVE方程研究了细颗粒物消光特性,并采用主成分分析—多元线性回归模型(PCA-MLR)对其来源进行解析,同时应用潜在源贡献因子(PSCF)和浓度权重轨迹(CWT)明确PM_2.5质量浓度的潜在污染源区域。结果表明,OC、EC以及SNA(NO3^?、NH₄⁺、SO₄^2?)的生成和积累对于能见度的下降具有重要影响,且能见度随SOR和NOR二次转化程度的升高而下降;2017年天津市采暖季日均消光系数为(294.56±262.89)Mm^?1,其中OM(34.86%)、硝酸盐(22.84%)、硫酸盐(11.59%)和EC(11.54%)为主要消光组分,硝酸盐和硫酸盐的增加对于能见度的下降起主要影响作用;根据PCA分析结果可知,天津市采暖季PM_2.5中的碳组分和水溶性离子主要来源于燃煤、生物质燃烧(68%),受扬尘(22%)和海盐(8%)的影响较小;区域传输分析结果表明天津市采暖季PM_2.5污染源潜在区域主要分布在河北中西部、河南北部、山西北部和内蒙古中部、西部。     

A comparative study of particle size distribution from two oxygenated fuels and diesel fuel

Oxygenated fuels are known to reduce particulate matter (PM) emissions from diesel engines. In this study, 100% soy methyl ester (SME) biodiesel fuel (B100) and a blend of 10% acetal denoted by A-diesel with diesel fuel were tested as oxygenated fuels. Particle size and number distributions from a diesel engine fueled with oxygenated fuels and base diesel fuel were measured using an Electrical Low Pressure Impactor (ELPI). Measurements were made at ten steady-state operational modes of various loads at two engine speeds. It was found that the geometric mean diameters of particles from SME and Adiesel were lower than that from base diesel fuel. Compared to diesel fuel, SME emitted more ultra-fine particles at rated speed while emitting less ultra-fine particles at maximum speed. Ultra-fine particle number concentrations of A-diesel were much higher than those of base diesel fuel at most test modes.     

Mass concentrations and temporal profiles of PM10, PM2.5 and PM1 near major urban roads in Beijing

Mass concentrations of PM₁₀, PM_2.5 and PM₁ were measured near major roads in Beijing during six periods: summer and winter of 2001, winter of 2007, and periods before, during and after the 2008 Beijing Olympic Games. Since the control efforts for motor vehicles helped offset the increase of emissions from the rapid growth of vehicles, the averaged PM_2.5 concentrations at roadsides during the sampling period between 2001 and 2008 fluctuated over a relatively small range. With the implementation of temporary traffic control measures during the Olympics, a clear “V” shaped curve showing the concentrations of particulate matter and other gaseous air pollutants at roadsides over time was identified. The average concentrations of PM₁₀, PM_2.5, CO and NO decreased by 31.2%, 46.3%, 32.3% and 35.4%, respectively, from June to August; this was followed by a rebound of all air pollutants in December 2008. Daily PM₁₀ concentrations near major roads exceeded the National Ambient Air Quality Standard (Grade II) for 61.2% of the days in the non-Olympic periods, while only for 12.5% during the Olympics. The mean ratio of PM_2.5/PM₁₀ near major roads remained relatively stable at 0.55 (±0.108) on non-Olympic days. The ratio decreased to 0.48 (±0.099) during the Olympics due to a greater decline in fine particles than in coarse-mode PM. The ratios PM₁/PM_2.5 fluctuated over a wide range and were statistically different from each other during the sampling periods. The average ratios of PM₁/PM_2.5 on non-Olympic days were 0.71.     

Understand the local and regional contributions on air pollution from the view of human health impacts

• PM_2.5-related deaths were estimated to be 227 thousand in BTH & surrounding regions. • Local emissions contribute more to PM_2.5-related deaths than PM_2.5 concentration. • Local controls are underestimated if only considering its impacts on concentrations. • Rural residents suffer larger impacts of regional transport than urban residents. • Reducing regional transport benefits in mitigating environmental inequality. The source-receptor matrix of PM_2.5 concentration from local and regional sources in the Beijing-Tianjin-Hebei (BTH) and surrounding provinces has been created in previous studies. However, because the spatial distribution of concentration does not necessarily match with that of the population, such concentration-based source-receptor matrix may not fully reflect the importance of pollutant control effectiveness in reducing the PM_2.5-related health impacts. To demonstrate that, we study the source-receptor matrix of the PM_2.5-related deaths instead, with inclusion of the spatial correlations between the concentrations and the population. The advanced source apportionment numerical model combined with the integrated exposure–response functions is used for BTH and surrounding regions in 2017. We observed that the relative contribution to PM_2.5-related deaths of local emissions was 0.75% to 20.77% larger than that of PM_2.5 concentrations. Such results address the importance of local emissions control for reducing health impacts of PM_2.5 particularly for local residents. Contribution of regional transport to PM_2.5-related deaths in rural area was 22% larger than that in urban area due to the spatial pattern of regional transport which was more related to the rural population. This resulted in an environmental inequality in the sense that people staying in rural area with access to less educational resources are subjected to higher impacts from regional transport as compared with their more resourceful and knowledgeable urban compatriots. An unexpected benefit from the multi-regional joint controls is suggested for its effectiveness in reducing the regional transport of PM_2.5 pollution thus mitigating the associated environmental inequality.     

嘉善冬季PM2.5化学组分特征及来源分析

为研究嘉兴地区嘉善冬季污染时段和清洁时段PM_2.5化学组分特征,结合气象数据对2019年1月嘉兴市嘉善县善西超级站在线自动监测PM_2.5及化学组分数据、气态污染物(NO₂和SO₂)进行了分析.结果表明,2019年1月嘉善善西超级站污染时段PM_2.5浓度(97.18μg·m^-3)为清洁时段(36.77μg·m^-3)的2.6倍.污染时段水溶性离子浓度(41.58μg·m^-3)较清洁时段(19.82μg·m^-3)高21.76μg·m^-3,但占比有所降低,含碳组分比例增加.OC;EC比值为3.93,可能受到燃煤及机动车排放的共同影响.低风速及高湿有利于NO₂和SO₂等气态污染物进行二次转化,污染时段硫转化率和氮转化率均比清洁时段高,分别增高7.93%和54.11%,说明NOx向硝酸盐二次转化较为明显,导致颗粒物浓度升高.聚类分析结果显示67.34%气流来自北方,且相应的气流轨迹上污染物浓度比周边高,说明污染物存在一定的长距离输送.结合风玫瑰图可以看出,污染主要为本地及其周边的输送,污染物的长距离输送在短时会使污染浓度突增.因此,在重点关注本地及周边污染的同时,偏北气流下的污染物区域输送不可忽视.     

Identify the contribution of vehicle non-exhaust emissions: a single particle aerosol mass spectrometer test case at typical road environment

● A single particle observation was conducted in a high traffic flow road environment. ● Major particle types were vehicle exhausts, coal burning, and biomass burning. ● Contribution of non-exhaust emissions was calculated via PMF. ● Proportion of non-exhaust emissions can reach 10.1 % at road environment. A single particle aerosol mass spectrometer (SPAMS) was used to accurately quantify the contribution of vehicle non-exhaust emissions to particulate matter at typical road environment. The PM_2.5, black carbon, meteorological parameters and traffic flow were recorded during the test period. The daily trend for traffic flow and speed on TEDA Street showed obvious “M” and “W” characteristics. 6.3 million particles were captured via the SPAMS, including 1.3 million particles with positive and negative spectral map information. Heavy Metal, High molecular Organic Carbon, Organic Carbon, Mixed Carbon, Elemental Carbon, Rich Potassium, Levo-rotation Glucose, Rich Na, SiO₃ and other categories were analyzed. The particle number concentration measured by SPAMS showed a good linear correlation with the mass concentrations of PM_2.5 and BC, which indicates that the particulate matter captured by the SPAMS reflects the pollution level of fine particulate matter. EC, ECOC, OC, HM and crustal dust components were found to show high values from 7:00–9:00 AM, showing that these chemical components are directly or indirectly related to vehicle emissions. Based on the PMF model, 7 major factors are resolved. The relative contributions of each factor were determined: vehicle exhaust emission (44.8 %), coal-fired source (14.5 %), biomass combustion (12.2 %), crustal dust (9.4 %), ship emission (9.0 %), tires wear (6.6 %) and brake pads wear (3.5 %). The results show that the contribution of vehicle non-exhaust to particulate matter at roadside environment is approximately 10.1 %. Vehicle non-exhaust emissions are the focus of future research in the vehicle pollutant emission control field.     

Impacts of methanol fuel on vehicular emissions: A review

● Methanol effectively reduces CO, HC, CO₂, PM, and PN emissions of gasoline vehicles. ● Elemental composition of methanol directly affects the reduction of emissions. ● Several physicochemical properties of methanol help reduce vehicle emissions. The transport sector is a significant energy consumer and a major contributor to urban air pollution. At present, the substitution of cleaner fuel is one feasible way to deal with the growing energy demand and environmental pollution. Methanol has been recognized as a good alternative to gasoline due to its good combustion performance. In the past decades, many studies have investigated exhaust emissions using methanol-gasoline blends. However, the conclusions derived from different studies vary significantly, and the explanations for the effects of methanol blending on exhaust emissions are also inconsistent. This review summarizes the characteristics of CO, HC, NO_x, CO₂, and particulate emissions from methanol-gasoline blended fuels and pure methanol fuel. CO, HC, CO₂, particle mass (PM), and particle number (PN) emissions decrease when methanol-blended fuel is used in place of gasoline fuel. NO_x emission either decreases or increases depending on the test conditions, i.e., methanol content. Furthermore, this review synthesizes the mechanisms by which methanol-blended fuel influences pollutant emissions. This review provides insight into the pollutant emissions from methanol-blended fuel, which will aid policymakers in making energy strategy decisions that take urban air pollution, climate change, and energy security into account.     

A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter

A method for quantifying source impacts for secondary PM_2.5 species is derived. The method provides estimates of bias in modeled concentrations. Adjusted concentrations match corresponding observations at monitored locations. Sources impacts on secondary species are estimated over the US for 20 sources. Community Multi-Scale Air Quality (CMAQ) estimates of sulfates, nitrates, ammonium, and organic carbon are highly influenced by uncertainties in modeled secondary formation processes, such as chemical mechanisms, volatilization, and condensation rates. These compounds constitute the majority of PM_2.5 mass, and reducing bias in estimated concentrations has benefits for policy measures and epidemiological studies. In this work, a method for adjusting source impacts on secondary species is developed that provides estimates of source contributions and reduces bias in modeled concentrations compared to observations. The bias correction adjusts concentrations and source impacts based on the difference between modeled concentrations and observations while taking into account uncertainties at the location of interest; and it is applied both spatially and temporally. We apply the method over the US for 2006. The mean bias for initial CMAQ concentrations compared to observations is −0.28 (OC), 0.11 (NO₃), 0.05 (NH₄), and −0.08 (SO₄). The normalized mean bias in modeled concentrations compared to observations was effectively zero for OC, NO₃, NH₄, and SO₄ after applying the secondary bias correction. 10-fold cross-validation was conducted to determine the performance of the spatial application of the bias correction. Cross-validation performance was favorable; correlation coefficients were greater than 0.69 for all species when comparing observations and concentrations based on kriged correction factors. The methods presented here address model uncertainties by improving simulated concentrations and source impacts of secondary particulate matter through data assimilation. Secondary-adjusted concentrations and source impacts from 20 emissions sources are generated for 2006 over continental US.     

Particulate and semivolatile associated aliphatic hydrocarbon exhaust emission from heavy duty diesel vehicles

The exhaust emissions from two heavy duty diesel vehicles running on eight different fuel compositions were investigated regarding their content of high molecular weight (≥ C₁₂) aliphatic/ olefinic hydrocarbons. It was concluded that the emitted amount of semi‐volatile associated aliphatic hydrocarbons (range C₁₂‐C₂₂) depend on the fuel used in the engines and that these emissions mainly consisted of uncombusted fuel components. It was also found that uncombusted engine lubrication oil was the main constituent of the emitted particulate associated aliphatic hydrocarbons (C₁₇‐C₄₀). These constituted between 58% and 95% of the total emissions of the high molecular weight aliphatic compounds. Emission factors for the total of high molecular aliphatic hydrocarbons (C₁₂‐C₄₀) were demonstrated to be in the range of 15–100 mg/km. Some individual aliphatic hydrocarbons with cocarcinogenic effects were identified and quantified in both particulate and semi‐volatile phases of the exhaust. Multivariate data analysis was used to investigate the relationship between fuel parameters and emission of semi‐volatile aliphatic emission.     

Local and regional contributions to PM2.5 in the Beijing 2022 Winter Olympics infrastructure areas during haze episodes

• Regional transportation contributed more than local emissions during haze episodes. • Short-range regional transportation contributed the most to the PM_2.5 in the OIAs. • Low wind speeds and low PBLHs led to higher local contributions to Beijing. The 2022 Winter Olympics is scheduled to take place in Beijing and Zhangjiakou, which were defined as OIAs (Olympic infrastructure areas) in this study. This study presents the characteristics and source apportionment of PM_2.5 in the OIAs, China. The entire region of mainland China, except for the OIAs, was divided into 9 source regions, including four regions in the BTH(Beijing-Tianjin-Hebei) region, the four provinces surrounding the BTH and the remaining areas. Using CAMx/PSAT, the contributions of the nine regions to the PM_2.5 concentration in the OIAs were simulated spatially and temporally. The simulated source apportionment results showed that the contribution of regional transportation was 48.78%, and when PM_2.5 concentration was larger than 75 μg/m³ central Hebei was the largest contributor with a contribution of 19.18%, followed by Tianjin, northern Hebei, Shanxi, Inner Mongolia, Shandong, southern Hebei, Henan and Liaoning. Furthermore, the contribution from neighboring regions of the OIAs was 47.12%, which was nearly twice that of long-range transportation. Haze episodes were analyzed, and the results presented the importance of regional transportation during severe PM_2.5 pollution periods. It was also found that they were associated with differences in pollution sources between Zhangjiakou and Beijing. Regional transportation was the main factor affecting PM_2.5 pollution in Zhangjiakou due to its low local emissions. Stagnant weather with a low planetary boundary layer height and a low wind velocity prevented the local emitted pollutants in Beijing from being transported outside, and as a result, local emissions constituted a larger contribution in Beijing.     

武汉市青山区道路尘碳组分污染特征及来源分析

为研究武汉市道路尘中碳组分污染特征及来源,于2018年5月在武汉市青山区采集道路尘样品,用热光碳分析仪测定样品中有机碳(OC)、元素碳(EC)、烟炱(soot)和焦炭(char)含量,并使用特征比值法、相关分析及主成分分析法对道路尘碳组分污染特征和来源进行探讨分析.结果表明,道路尘中OC、EC、soot和char含量平均值分别为1.29、2.21、2.04、0.17 g·kg^-1,说明不同碳组分含量存在较大的空间变异性.相关性分析表明OC和EC的来源存在一定差异,且EC主要贡献来源是soot.OC;EC和char;soot比值和主成分分析结果表明,武汉市青山区道路尘中碳组分主要来源于机动车尾气和燃煤排放,也可能受到生物质燃烧的影响.     

Collaborative control of fine particles and ozone required in China for health benefit

● Increased DAAO offsets 3/4 of the decrease of DAAP in 2013–2020. ● DAAO increases are mainly due to O₃ concentration increase and population aging. ● Health benefit from PM_2.5 reduction after 2017 is larger than that before 2017. ● Reducing PM_2.5 concentration by 1% results in 0.6% reduction of DAAP. ● Reducing O₃ concentration by 1% results in 2% reduction of DAAO. PM_2.5 concentration declined significantly nationwide, while O₃ concentration increased in most regions in China in 2013–2020. Recent evidences proved that peak season O₃ is related to increased death risk from non-accidental and respiratory diseases. Based on these new evidences, we estimate excess deaths associated with long-term exposure to ambient PM_2.5 and O₃ in China following the counterfactual analytic framework from Global Burden Disease. Excess deaths from non-accidental diseases associated with long-term exposure to ambient O₃ in China reaches to 579 (95% confidential interval (CI): 93, 990) thousand in 2020, which has been significantly underestimated in previous studies. In addition, the increased excess deaths associated with long-term O₃ exposure (234 (95% CI: 177, 282) thousand) in 2013–2020 offset three quarters of the avoided excess deaths (302 (95% CI: 244, 366) thousand) mainly due to PM_2.5 exposure reduction. In key regions (the North China Plain, the Yangtze River Delta and the Fen-Wei Plain), the former is even larger than the latter, particularly in 2017–2020. Health benefit of PM_2.5 concentration reduction offsets the adverse effects of population growth and aging on excess deaths attributed to PM_2.5 exposure. Increase of excess deaths associated with O₃ exposure is mainly due to the strong increase of O₃ concentration, followed by population aging. Considering the faster population aging process in the future, collaborative control, and faster reduction of PM_2.5 and O₃ are needed to reduce the associated excess deaths.     

Long-term exposure to air pollution and cerebrovascular disease: findings from Beijing Health Management Cohort study

● This study explored the long-term association by double robust additive models. ● Individual exposure concentrations were assessed by integrating GAM, LUR and BPNN. ● PM_2.5, SO₂ and NO₂ are positively associated with cerebrovascular disease. ● CO could reduce the risk of cerebrovascular disease with the highest robustness. ● The elderly, women and people with normal BMI are at higher risk for air pollution. The relationship between air pollution and cerebrovascular disease has become a popular topic, yet research findings are highly heterogeneous. This study aims to investigate this association based on detailed individual health data and a precise evaluation of their exposure levels. The integrated models of generalized additive model, land use regression model and back propagation neural network were used to evaluate the exposure concentrations. And doubly robust additive model was conducted to explore the association between cerebrovascular disease and air pollution after adjusted for demographic characteristics, physical examination, disease information, geographic and socioeconomic status. A total of 25097 subjects were included in the Beijing Health Management Cohort from 2013 to 2018. With a 1 μg/m³ increase in the concentrations of PM_2.5, SO₂ and NO₂, the incidence risk of cerebrovascular disease increased by 1.02 (95% CI: 1.008–1.034), 1.06 (95% CI: 1.034–1.095) and 1.02 (95% CI: 1.010–1.029) respectively. Whereas CO exposure could decrease the risk, with an odds ratio of 0.38 (95% CI: 0.212–0.626). In the subgroup analysis, individuals under the age of 50 with normal BMI were at higher risk caused by PM_2.5, and SO₂ was considered more hazardous to women. Meanwhile, the protective effect of CO on women and those with normal BMI was stronger. Successful reduction of long-term exposure to PM_2.5, SO₂ and NO₂ would lead to substantial benefits for decrease the risk of cerebrovascular disease especially for the health of the susceptible individuals.     

Source apportionment of PM2.5 in Tangshan,China—Hybrid approaches for primary and secondary species apportionment

The new hybrid approaches for the source apportionment of PM_2.5 were proposed. The hybrid approach can be used for source apportionment of secondary species. The metallurgy industry was the biggest contribution source to PM_2.5 of Tangshan. In winter, the contribution from the coal-fired boilers was the largest one. The objective of this paper is to propose a hybrid approach for the source apportionment of primary and secondary species of PM_2.5 in the city of Tangshan. The receptor-based PMF (Positive Matrix Factorization) is integrated with the emission inventory (EI) to form the first hybrid method for the source apportionment of the primary species. The hybrid CAMx-PSAT-CP (Comprehensive Air Quality Model with Extensions – Particulate Source Apportionment Technology – Chemical Profile) approach is then proposed and used for the source apportionment of the secondary species. The PM_2.5 sources identified for Tangshan included the soil dust, the metallurgical industry, power plants, coal-fired boilers, vehicles, cement production, and other sources. It is indicated that the PM_2.5 pollution is a regional issue. Among all the identified sources, the metallurgy industry was the biggest contribution source to PM_2.5, followed by coal-fired boilers, vehicles and soil dust. The other-source category plays a crucial role for PM_2.5, particularly for the formation of secondary species and aerosols, and these other sources include non-specified sources such as agricultural activities, biomass combustion, residential emissions, etc. The source apportionment results could help the local authorities make sound policies and regulations to better protect the citizens from the local and regional PM_2.5 pollution. The study also highlights the strength of utilizing the proposed hybrid approaches in the identification of PM_2.5 sources. The techniques used in this study show considerable promise for further application to other regions as well as to identify other source categories of PM_2.5.