杭州城市可吸入颗粒物污染与气象条件关系的分级评价和BP神经网络评估

为了解杭州城市环境空气质量与气象条件之间的关系,利用杭州市区2003-2007年的可吸入颗粒物(PM10)浓度数据和气象资料,通过分级评价的方法和基于BP神经网络的污染物浓度评估模型,得到PM10浓度与气象条件的对应关系.结果表明,随着日降水量的增大,PM10浓度减小;风速与PM10浓度呈明显的负相关,随着风速的增大,PM10浓度明显减小;气象因素与PM10浓度之间呈非线性关系,大气能见度对PM10和相对湿度的变化极为敏感.随着PM10浓度的增大,大气能见度迅速降低,相对湿度越高,大气能见度则越低;近几年杭州市气象条件不利于大气污染物的扩散和清洗,是杭州城市环境空气质量上升缓慢的主要原因之一.     

杭州市区大气能见度变化趋势及其与主要污染物相关性分析

利用2004-2006年地面气象观测资料和同期环境空气质量自动监测数据,分析了杭州市区大气能见度变化趋势及其与主要污染物的相关性.结果表明,杭州市区能见度的日分布特征为14时最好,8时最差;季节变化特征为夏季>春季>秋季>冬季,全年仅7月能见度超过10 km;SO2、NO2、PM10浓度均随能见度增高而逐渐降低;影响能见度的首要因子为相对湿度和PM2.5,能见度与PM2.5浓度具有较好的相关性.     

成都市大气颗粒物污染特征及与气象因子的关联性分析

利用成都市2013年6月至2014年5月的PM10和PM2.5浓度监测数据,分析大气颗粒物污染特征,并探讨其与气温、相对湿度、降雨、风向、风速等气象因子的关联性。结果表明:成都市大气PM2.5污染较严重;PM10和PM2.5浓度及超标率均表现为冬季秋季春季夏季,秋季和冬季为大气颗粒物污染高发期;PM2.5对PM10贡献显著;气温超过10℃时,PM10和PM2.5最高浓度大体随气温升高而降低;相对湿度为40%~80%时,PM10和PM2.5浓度随相对湿度增加而升高;相对湿度超过80%时,易发生降雨,PM10和PM2.5浓度降低;降雨对PM10的清除量高于PM2.5,但降雨后PM10和PM2.5浓度较快回升;PM10和PM2.5浓度在偏西风下高于其他风向;PM10主要受局地源影响,而PM2.5主要受西北方向上的外来源影响。     

杭州城市大气消光系数和能见度的影响因子研究

为了解杭州市能见度下降与大气污染之间的关系，在2001年5月至2002年5月对不同粒径的颗粒物（PM10、PM2.5）的质量浓度进行了观测，结合晴天天气条件下的大气能见度，推算污染物和水汽分子对大气的消光散射，发现细微颗粒物的散射消光特性对杭州市能见度下降起主要作用，并得到能见度与细微颗粒物浓度比值（PM2．5／PM10）的关系；分析了大气能见度和消光系数与PM2．5／PM10和相对湿度的相关系数。     

广州PM2.5污染特征及与气象因子的关系分析

基于2014—2016年广州PM_(2.5)浓度逐时观测数据,研究了广州PM_(2.5)污染变化特征及其与气象因子的关系,确定了影响广州大气能见度的PM_(2.5)浓度阈值。结果表明:(1)2014—2016年广州PM_(2.5)质量浓度平均为32.7μg/m3,广州1月PM_(2.5)污染最重,轻度、中度、重度污染频率合计达20.16%;(2)PM_(2.5)浓度与风速、降水、气温、能见度呈负相关,与相对湿度、气压呈正相关;(3)广州地区在南风的条件下PM_(2.5)浓度最低,风速小于2m/s的偏北风下易出现污染;(4)PM_(2.5)浓度与相对湿度共同影响广州能见度的变化,随着相对湿度的增加,PM_(2.5)浓度的敏感阈值不断减小,通常当PM_(2.5)高于37.3μg/m3时,控制PM_(2.5)对改善城市能见度成效相对缓慢,而当PM_(2.5)浓度低于此阈值时,降低PM_(2.5)将显著提高大气能见度。     

长沙地区雾霾特征及影响因子分析

根据长沙地区1970—2012年气象观测资料及环境监测数据,对近43年长沙雾霾特征及影响因子进行了分析。结果表明,长沙地区雾的年际变化具有显著的倒"U"型特征,霾整体上呈上升趋势;雾霾天气主要集中在秋冬季节,春夏季节较少;从空间分布来看,望城区(县)和宁乡县雾霾天气最多,浏阳市次之,长沙市区最少。在一次持续性雾霾天气过程中(10.2~10.12),相对湿度、PM2.5质量浓度与能见度呈现显著负相关,说明PM2.5质量浓度和相对湿度是雾霾天气形成的首要影响因子。     

高交通密度道路周边乔灌草型绿地对大气颗粒物的影响

在杭州临安一高交通密度道路周边的乔灌草型绿地中监测了PM1浓度、PM2.5浓度、PM10浓度、温度、湿度、风速、气压、CO2浓度,研究了颗粒物的日变化规律、乔灌草群落对其的消减影响及与气象因子的关系。结果表明:(1)不同粒径的大气颗粒物PM1、PM2.5、PM10的日变化特征一致,表现为"早晚高、中午低"的现象,3者与同一气象因子的决定系数基本相同;(2)道路两边的绿地宽度并不一定越宽越好,还应考虑植物种类的配置结构、植被密集程度及经济性;(3)大气颗粒物浓度与温度、风速成负相关关系,与湿度、气压成正相关关系,其中风速是影响颗粒物浓度的最关键气象因子。     

西安市灰霾天气下PM_(2.5)浓度与气象条件分析

分析了2013年1—3月西安市12个空气监测子站监测的PM10、PM2.5以及相关气象参数;绘制了不同月的主城区浓度分布等值线图。运用单样本K-S非参数检验法检验表明,PM2.5浓度符合对数正态分布;各站点间的PM2.5浓度相关性非常高,变化趋势一致;PM10和PM2.5的变化规律呈现"W"型三峰分布;PM2.5日均值与能见度、净辐射量、平均气温、最高气温、最低气温均呈现显著负相关,且相关性较强;与平均湿度、最大湿度、最小湿度呈现显著正相关;与总辐射量、日照时数、气压、露点温度的相关性较弱;节日烟花燃放、沙尘天气容易造成严重大气污染,其中节日烟花燃放、沙尘天气对PM10的贡献量大于对PM2.5的贡献。     

基于可吸入颗粒物评估的广西能见度观测资料订正

对能见度观测值进行湿度修订,以凸显能见度与可吸入颗粒物(PM10)的关系。以广西3个典型台站(南宁站、桂林站、北海站)为研究对象,利用2001年7月至2011年12月样本数据建立3个台站能见度订正模型,分别采用建模期样本数据与非建模期(2012年1月至2012年12月)样本数据对订正模型进行评估,发现订正后能见度与PM10相关系数绝对值有所提高,即订正后能见度可以更好地表征PM10污染。最后,分析了湿度影响导致的能见度观测偏差以及订正前后能见度分布频率、趋势变化的差异。     

重庆市核心区黑碳气溶胶浓度特征以及影响因素分析

2013年4月至2014年2月期间利用重庆市大气超级站的黑碳气溶胶(black carbon,BC)、气态污染物(SO2、NOx和O3)和颗粒物观测数据,分析了重庆市BC浓度的变化特征及与能见度、颗粒物以及SO2、NOx和O3气态污染物的相关性。观测期间BC年日均值为(4.86±2.37)μg/m3,浓度范围为1.32~11.54μg/m3。秋冬季BC日均浓度及相对偏差比春夏季高。BC和能见度呈负相关性。4个季度的BC与PM10、PM2.5和PM1日均值显著正相关,相关系数最小在夏季,最大在秋季。BC与O3日均值呈负相关性。BC与SO2,NOx日均值显著正相关,表明重庆市BC与SO2,NOx来源相近,即为燃煤和机动车尾气排放。     

Long-term trends and characteristics of visibility in two megacities in southwest China: Chengdu and Chongqing

Visibility is a good indicator of air quality because it reflects the combined influences of atmospheric pollutants and synoptic processes. Trends in visibility and relationships with various factors in Chengdu and Chongqing, two megacities in southwest China, were analyzed using daily data from National Climatic Data Center and the Air Pollution Index (API) of the Ministry of Environmental Protection of China. Average annual visibility during the period of 1973–2010 was 8.1 ± 3.9 in Chengdu and 6.2 ± 4.3 km in Chongqing. PM₁₀ dominates the reported primary pollutants in both cities, although concentrations have decreased from a high of 127.9 and 150 µg m³ before 2005 to 100.4 and 93.5 µg m^?3 in Chengdu and Chongqing, respectively. Low average visibility and extremely high levels of PM10 were observed in winter, whereas relative humidity had irregular and weak seasonal variations. Visibility in both cities has deteriorated in comparison to the 1960s and 1970s, mostly due to the elevation of optical depth caused by anthropogenic pollution.

Correlations and principal component analysis (PCA) were undertaken to determine the key factors affecting visibility. Visibility was only moderately correlated with PM₁₀. In Chengdu, visibility displayed weak correlations with various factors, whereas visibility in Chongqing was most strongly related to relative humidity due to the atmospheric particulates in the region containing more hygroscopic components. PCA results further confirmed that high relative humidity and low wind speed increased the occurrence of low visibility events under high PM₁₀ concentrations. Temperature and pressure, as indicators of weather systems, also played important roles in affecting visibility. Mathematical models of visibility prediction indicated that wind speed had the largest coefficients among all meteorological factors, and reductions in PM₁₀ concentration only led to minor improvements in visibility.

Implications: Long-term data indicates that visibility in Chengdu and Chongqing has been lower than 10 km since the 1970s, and the poor visibility primarily results from anthropogenic pollution. Although PM₁₀ concentrations have decreased consistently to around 100 μg m^?3, trends of visibility have shown no improvement but much fluctuation. Correlation and principal component analysis demonstrate that low visibility in Chengdu is influenced by high relative humidity, while in Chongqing the degrading visibility is related with high relative humidity and pressure and low wind speed under a stable weather system. The results are important to understand the widespread haze event in the two megacities of southwest China.     

Effect of PM2.5 chemical constituents on atmospheric visibility impairment

PM_2.5 sampling was conducted at a curbside location in Delhi city for summer and winter seasons, to evaluate the effect of PM_2.5 and its chemical components on the visibility impairment. The PM_2.5 concentrations were observed to be higher than the National Ambient Air Quality Standards (NAAQS), indicating poor air quality. The chemical constituents of PM_2.5 (the water-soluble ionic species SO₄^2-, NO₃^?, Cl^?, and NH₄⁺, and carbonaceous species: organic carbon, elemental carbon) were analyzed to study their impact on visibility impairment by reconstructing the light extinction coefficient, b_ext. The visibility was found to be negatively correlated with PM_2.5 and its components. The reconstructed b_ext showed that organic matter was the largest contributor to b_ext in both the seasons which may be attributed to combustion sources. In summer season, it was followed by elemental carbon and ammonium sulfate; however, in winter, major contributions were from ammonium nitrate and elemental carbon. Higher elemental carbon in both seasons may be attributed to traffic sources, while lower concentrations of nitrate during summer, may be attributed to volatility because of higher atmospheric temperatures.

Implications: The chemical constituents of PM_2.5 that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.     

Roadside suspended particulates at heavily trafficked urban sites of Hong Kong – Seasonal variation and dependence on meteorological conditions

In this study, the seasonal variation of different types of particulates was investigated in a fixed roadside station in heavily trafficked urban area of Hong Kong. Aerosol samples for total suspended particles (TSP), PM₁₀ and PM_2.5 were collected from June 1998 to May 1999 at a roadside site. Meteorological conditions such as relative humidity (RH), rainfall and prevailing wind direction were found to affect the mass concentration of TSP, PM₁₀ and coarse particulates at roadside level. Large size particles had an apparent seasonal variation, with higher concentration level in winter and lower in summer. The dry continental winter monsoon and the wet oceanic summer monsoon are the dominating factors. On the other hand, annual variation of PM_2.5 is relatively insignificant, suggesting that they are mainly from local traffic emission. PM₁₀ accounted for 62% of the TSP, while PM_2.5 accounted for 46%. The annual PM_2.5/PM₁₀ is high with PM_2.5 responsible for 74% of PM₁₀. In our heavily trafficked roadside fixed site, TSP exceeded the annual average of the Hong Kong Air Quality Objective by a factor of 1.53 while PM₁₀ exceeded by 1.39. The annual average concentration of PM_2.5 exceeded the National Ambient Air Quality Standard (NAAQS) annual average of 15 μg m⁻³ by a factor of 3.8 and is a cause of concern. A total of the 24 h average PM_2.5 exceeded NAAQS by 33%. According to our data reported, fine particulate pollution is serious in Hong Kong.     

Performance evaluation of the portable MiniVOL particulate matter sampler

The MiniVOL sampler is a popular choice for use in air quality assessments because it is portable and inexpensive relative to fixed site monitors. However, little data exist on the performance characteristics of the sampler. The reliability, precision, and comparability of the portable MiniVOL PM₁₀ and PM_2.5 sampler under typical ambient conditions are described in this paper. Results indicate that the MiniVOL (a) operated reliably and (b) yielded statistically similar concentration measurements when co-located with another MiniVOL (r²=0.96 for PM₁₀ measurements and r²=0.95 for PM_2.5 measurements). Thus, the characterization of spatial distributions of PM₁₀ and PM_2.5 mass concentrations with the MiniVOL can be accomplished with a high level of confidence. The MiniVOL also produced statistically comparable results when co-located with a Dichotomous Sampler (r²=0.83 for PM₁₀ measurements and r²=0.85 for PM_2.5 measurements) and a continuous mass sampling system (r²=0.90 for PM₁₀ measurements). Environmental factors such as ambient concentration, wind speed, temperature, and humidity may influence the relative measurement comparability between these sampling systems.     

Feasibility of using low-cost portable particle monitors for measurement of fine and coarse particulate matter in urban ambient air

Exposure to ambient particulate matter (PM) is known as a significant risk factor for mortality and morbidity due to cardiorespiratory causes. Owing to increased interest in assessing personal and community exposures to PM, we evaluated the feasibility of employing a low-cost portable direct-reading instrument for measurement of ambient air PM exposure. A Dylos DC 1700 PM sensor was collocated with a Grimm 11-R in an urban residential area of Houston Texas. The 1-min averages of particle number concentrations for sizes between 0.5 and 2.5 µm (small size) and sizes larger than 2.5 µm (large size) from a DC 1700 were compared with the 1-min averages of PM_2.5 (aerodynamic size less than 2.5 µm) and coarse PM (aerodynamic size between 2.5 and 10 µm) concentrations from a Grimm 11-R. We used a linear regression equation to convert DC 1700 number concentrations to mass concentrations, utilizing measurements from the Grimm 11-R. The estimated average DC 1700 PM_2.5 concentration (13.2 ± 13.7 µg/m³) was similar to the average measured Grimm 11-R PM_2.5 concentration (11.3 ± 15.1 µg/m³). The overall correlation (r²) for PM_2.5 between the DC 1700 and Grimm 11-R was 0.778. The estimated average coarse PM concentration from the DC 1700 (5.6 ± 12.1 µg/m³) was also similar to that measured with the Grimm 11-R (4.8 ± 16.5 µg/m³) with an r² of 0.481. The effects of relative humidity and particle size on the association between the DC 1700 and the Grimm 11-R results were also examined. The calculated PM mass concentrations from the DC 1700 were close to those measured with the Grimm 11-R when relative humidity was less than 60% for both PM_2.5 and coarse PM. Particle size distribution was more important for the association of coarse PM between the DC 1700 and Grimm 11-R than it was for PM_2.5.

Implications: The performance of a low-cost particulate matter (PM) sensor was evaluated in an urban residential area. Both PM_2.5 and coarse PM (PM_10-2.5) mass concentrations were estimated using a DC1700 PM sensor. The calculated PM mass concentrations from the number concentrations of DC 1700 were close to those measured with the Grimm 11-R when relative humidity was less than 60% for both PM_2.5 and coarse PM. Particle size distribution was more important for the association of coarse PM between the DC 1700 and Grimm 11-R than it was for PM_2.5.     

Sensitivity analyses of factors influencing CMAQ performance for fine particulate nitrate

Improvement of air quality models is required so that they can be utilized to design effective control strategies for fine particulate matter (PM_2.5). The Community Multiscale Air Quality modeling system was applied to the Greater Tokyo Area of Japan in winter 2010 and summer 2011. The model results were compared with observed concentrations of PM_2.5 sulfate (SO₄^2-), nitrate (NO₃^?) and ammonium, and gaseous nitric acid (HNO₃) and ammonia (NH₃). The model approximately reproduced PM_2.5 SO₄^2? concentration, but clearly overestimated PM_2.5 NO₃^? concentration, which was attributed to overestimation of production of ammonium nitrate (NH₄NO₃). This study conducted sensitivity analyses of factors associated with the model performance for PM_2.5 NO₃^? concentration, including temperature and relative humidity, emission of nitrogen oxides, seasonal variation of NH₃ emission, HNO₃ and NH₃ dry deposition velocities, and heterogeneous reaction probability of dinitrogen pentoxide. Change in NH₃ emission directly affected NH₃ concentration, and substantially affected NH₄NO₃ concentration. Higher dry deposition velocities of HNO₃ and NH₃ led to substantial reductions of concentrations of the gaseous species and NH₄NO₃. Because uncertainties in NH₃ emission and dry deposition processes are probably large, these processes may be key factors for improvement of the model performance for PM_2.5 NO₃^?.

Implications: The Community Multiscale Air Quality modeling system clearly overestimated the concentration of fine particulate nitrate in the Greater Tokyo Area of Japan, which was attributed to overestimation of production of ammonium nitrate. Sensitivity analyses were conducted for factors associated with the model performance for nitrate. Ammonia emission and dry deposition of nitric acid and ammonia may be key factors for improvement of the model performance.     

Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change

We applied a multiple linear regression (MLR) model to study the correlations of total PM_2.5 and its components with meteorological variables using an 11-year (1998–2008) observational record over the contiguous US. The data were deseasonalized and detrended to focus on synoptic-scale correlations. We find that daily variation in meteorology as described by the MLR can explain up to 50% of PM_2.5 variability with temperature, relative humidity (RH), precipitation, and circulation all being important predictors. Temperature is positively correlated with sulfate, organic carbon (OC) and elemental carbon (EC) almost everywhere. The correlation of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains. RH is positively correlated with sulfate and nitrate, but negatively with OC and EC. Precipitation is strongly negatively correlated with all PM_2.5 components. We find that PM_2.5 concentrations are on average 2.6 μg m^?3 higher on stagnant vs. non-stagnant days. Our observed correlations provide a test for chemical transport models used to simulate the sensitivity of PM_2.5 to climate change. They point to the importance of adequately representing the temperature dependence of agricultural, biogenic and wildfire emissions in these models.     

Hygroscopic Organic Aerosols during BRAVO?

Abstract

The hygroscopic properties of the organic fraction of aerosols are poorly understood. The ability of organic aerosols to absorb water as a function of relative humidity (RH) was examined using data collected during the 1999 Big Bend Regional Aerosol and Visibility Observational Study (BRAVO). (On average, organics accounted for 22% of fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM_2.5) mass). Hourly RH exceeded 80% only 3.5% of the time and averaged 44%. BRAVO aerosol chemical composition and dry particle size distributions were used to estimate PM_2.5 light scattering (Bsp) at low and high ambient RH. Liquid water growth associated with inorganic species was sufficient to account for measured Bsp for RH between 70 and 95%.     

Association between particulate matter air pollution and cardiovascular disease mortality in Lanzhou,China

Ambient particulate matter (PM) pollution has been linked to elevated mortality, especially from cardiovascular diseases. However, evidence on the effects of particulate matter pollution on cardiovascular mortality is still limited in Lanzhou, China. This research aimed to examine the associations of daily mean concentrations of ambient air pollutants (PM_2.5, PM_C, and PM₁₀) and cardiovascular mortality due to overall and cause-specific diseases in Lanzhou. Data representing daily cardiovascular mortality rates, meteorological factors (daily average temperature, daily average humidity, and atmospheric pressure), and air pollutants (PM_2.5, PM₁₀, SO₂, NO₂) were collected from January 1, 2014, to December 31, 2017, in Lanzhou. A quasi-Poisson regression model combined with a distributed lag non-linear model (DLNM) was used to estimate the associations. Stratified analyses were also performed by different cause-specific diseases, including cerebrovascular disease (CD), ischemic heart disease (IHD), heart rhythm disturbances (HRD), and heart failure (HF). The results showed that elevated concentration of PM_2.5, PM_C, and PM₁₀ had different effects on mortality of different cardiovascular diseases. Only cerebrovascular disease showed a significant positive association with elevated PM_2.5. Positive associations were identified between PM_C and daily mortality rates from total cardiovascular diseases, cerebrovascular diseases, and ischemic heart diseases. Besides, increased concentration of PM₁₀ was correlated with increased death of cerebrovascular diseases and ischemic heart diseases. For cerebrovascular disease, each 10 μg/m³ increase in PM_2.5 at lag4 was associated with increments of 1.22% (95% CI 0.11–2.35%). The largest significant effects for PM_C on cardiovascular diseases and ischemic heart diseases were both observed at lag0, and a 10 μg/m³ increment in concentration of PM_C was associated with 0.47% (95% CI 0.06–0.88%) and 0.85% (95% CI 0.18–1.52%) increases in cardiovascular mortality and ischemic heart diseases. In addition, it exhibited a lag effect on cerebrovascular mortality as well, which was most significant at lag6d, and an increase of 10 μg/m³ in PM_C was associated with a 0.76% (95% CI 0.16–1.37%) increase in cerebrovascular mortality. The estimates of percentage change in daily mortality rates per 10 μg/m³ increase in PM₁₀ were 0.52% (95% CI 0.05–1.02%) for cerebrovascular disease at lag6 and 0.53% (95% CI 0.01–1.05%) for ischemic heart disease at lag0, respectively. Our study suggests that elevated concentration of atmospheric PM (PM_2.5, PM_C, and PM₁₀) in Lanzhou is associated with increased mortality of cardiovascular diseases and that the health effect of elevated concentration of PM_2.5 is more significant than that of PM_C and PM₁₀.