基于GIS的石家庄市PM10和PM2.5时空分布研究

利用2018年261个乡镇环境空气自动监测站监测数据,结合GIS空间分析技术,对石家庄市PM10和PM2.5的时空污染特征进行了研究。结果表明,石家庄地区PM10和PM2.5污染的空间分布整体表现为西北部山区好于东南部的平原地区,主城区好于周边县(市、区)的特征。采暖期PM10和PM2.5的污染程度明显重于非采暖期。PM2.5稳定性差于PM10,PM10和PM2.5的稳定性与污染程度具有一定的负相关性,表现出污染越轻的区域稳定性越差。两者的日均值浓度变化在时间序列上呈极强正相关,且污染越重的区域时间相关性越强。与日均值相关性不同,污染程度越轻的区域PM10和PM2.5年均值的线性相关性越强。     

Particulate matter concentration levels during intense haze event in an urban environment

This study assessed concentration levels of particulate matter (PM) in the ambient environment of Ilorin metropolis, Nigeria, during haze episodes. Meteorological data (wind speed and direction, rainfall data, sunshine data, relative humidity and temperature) were obtained. Aerocet 531S particle counter (MetOne Instruments, USA) was used to measure four mass concentration ranges of PM (PM_1.0, PM_2.5, PM₁₀ and the total suspended particles (TSP)) in 10 locations taking into consideration land use patterns. Surfer® version 8 (Golden Software LLC, USA) was used to model the spatial variation of particulate matter concentration levels using kriging interpolation griding method. Human exposure assessment was done using the total respiratory deposition dose (TRDD) estimates and statutory limit breach (SLB) approaches. The appearance of dominating weak southern atmospheric wind flow was observed as wind speed ranged from 0 to 6.811 m/s while solar radiation periods ranged from 0.3 to 3.5 h/day. The relative humidity of the metropolis ranged between 28 and 57%, while daily temperature was 15 to 36 °C. Highest concentration levels of PM measured were 73.4, 562.7, 7066.3 and 9907.8 μg/m³ for PM_1.0, PM_2.5, PM₁₀ and TSP, respectively. Very strong negative correlations existed between the PM concentration levels and microclimatic parameters. Spatial variation of the concentration level as modelled using Surfer® version 8 indicated that particulate concentration level increases from south to north. Concentration levels of PM for the 24-h averaging period were generally above the 24-h threshold limit value set by the regulatory agencies for all the locations.     

Temporal and spatial variations of the quality of ambient air in the Kingdom of Bahrain during 2007

The main objective of the present investigation is to study the temporal and spatial variations of the quality of ambient air in the Kingdom of Bahrain. The non-parametric Kruskal-Wallis (KW) test showed significant spatial variations and interactions of spatial-temporal among five mobile monitoring stations for 11 air pollutants. The Mann Whitney (MW) test demonstrated the seasonality of spring over winter for the PM(10), PM(2.5), NO(2), CO and p-xylene, the seasonality of winter over spring for O(3), and no significant seasonal variation for NH(3), benzene, SO(2), toluene and H(2)S. It is concluded that emissions from automobile exhaust, industrial and developmental projects are responsible for the spatial air pollution, and that air temperature is the controlling factor for the seasonal variations.     

Characterization of fine aerosol and its inorganic components at two rural locations in New York State

Samples of PM(2.5) were collected to measure the concentrations of its chemical constituents at two rural locations, Potsdam and Stockton, NY from November 2002 to August 2005. These samples were collected on multiple filters at both sites, every third day for a 24-h interval with a speciation network sampler. The Teflo(R) filters were analyzed for PM(2.5) mass by gravimetry, and elemental composition by X-ray fluorescence (XRF). Nylasorb(R) filters and Teflo(R) filters were leached with water and analyzed for anions and cations, respectively, by ion chromatography (IC). Fine particulate matter (PM(2.5)) mass and its inorganic component measurements were statistically characterized, and the temporal behavior of these species were assessed. Over the entire study period, PM(2.5) mass concentrations were lower at Potsdam (8.35 mug/m(3)) than at Stockton (10.24 mug/m(3)). At both locations, organic matter (OM) was the highest contributor to mass. Sulfate was the second highest contributor to mass at 27.0% at Potsdam, and 28.7% at Stockton. Nitrate contributions to mass of 8.9 and 9.5% at Potsdam and Stockton, respectively, were the third highest. At both locations, fine PM mass exhibited an annual cycle with a pronounced summer peak and indications of another peak during the winter, consistent with an overall increase in the rate of secondary aerosol formation during the summer, and increased partitioning of ammonium nitrate to the particle phase and condensation of other semi-volatiles during the winter, respectively. An ion-balance analysis indicated that at both locations, during the summers as well as in the winters, the aerosol was acidic. Lognormal frequency distribution fits to the measured mass concentrations on a seasonal basis indicated the overall increase in particle phase secondary aerosol (sulfate and SOA) concentrations during the summers compared to the winters at both locations.     

乌鲁木齐市2011年冬季PM2.5/PM10浓度特征分析

利用乌鲁木齐市PM2.5//PM10自动监测数据,分析PM2.5与PM10的浓度分布特征和时间变化规律。结果表明,按照《环境空气质量标准》(二次征求意见稿)的标准限值,乌鲁木齐市冬季PM2.5污染重于PM10。PM2.5浓度为0.164mg/m3,超过二级年标准限值的3.7倍,超标率为73.9%。PM2.5浓度日变化曲线昼高夜低,呈单峰型,峰值出现在13:00~14:00(北京时间)。PM10中PM2.5所占比例较高,PM2.5/PM10为0.79,相关分析和检验显示PM2.5与PM10的线性相关显著,相关系数为0.92。     

Source apportionment of PM10 and PM(2.5) at Tocopilla, Chile (22 degrees 05' S, 70 degrees 12' W)

Tocopilla is located on the coast of Northern Chile, within an arid region that extends from 30 degrees S to the border with Perú. The major industrial activities are related to the copper mining industry. A measurement campaign was conducted during March and April 2006 to determine ambient PM10 and PM(2.5) concentrations in the city. The results showed significantly higher PM10 concentrations in the southern part of the city (117 microg/m3) compared with 79 and 80 (microg/m3) in the central and northern sites. By contrast, ambient PM2.5 concentrations had a more uniform spatial distribution across the city, around 20 (microg/m3). In order to conduct a source apportionment, daily PM10 and PM(2.5) samples were analyzed for elements by XRF. EPA's Positive Matrix Factorization software was used to interpret the results of the chemical compositions. The major source contributing to PM(2.5) at sites 1, 2 and 3, respectively are: (a) sulfates, with approximately 50% of PM2.5 concentrations at the three sites; (b) fugitive emissions from fertilizer storage and handling, with 16%, 21% and 10%; (c) Coal and residual oil combustion, with 15%, 15% and 4%; (d) Sea salt, 5%, 6% and 16%; (e) Copper ore processing, 4%, 5% and 15%; and (f) a mixed dust source with 11%, 7% and 4%. Results for PM10--at sites 1, 2 and 3, respectively--show that the major contributors are: (a) sea salt source with 36%, 32% and 36% of the PM10 concentration; (b) copper processing emissions mixed with airborne soil dust with 6.6%, 11.5% and 41%; (c) sulfates with 31%, 31% and 12%; (d) a mixed dust source with 16%, 12% and 10%, and (e) the fertilizer stockpile emissions, with 11%, 14% and 2% of the PM10 concentration. The high natural background of PM10 implies that major reductions in anthropogenic emissions of PM10 and SO2 would be required to attain ambient air quality standards for PM10; those reductions would curb down ambient PM(2.5) concentrations as well.     

郑州市 PM2．5和 PM10质量浓度变化特征分析

根据郑州市2013年PM2．5和PM10颗粒物连续自动监测数据,对郑州市各国控站点的PM2．5和PM10的达标情况、变化趋势等进行探讨分析。结果表明：2013年郑州市PM10和PM2．5的年均质量浓度均超过了新标准规定的年均值二级标准限值。 PM10和PM2．5月均值峰值出现在1月和10月,谷值出现在8月,各月PM2．5的超标天数都大于PM10。PM10和PM2．5冬季的日均值浓度明显高于其他季节,呈双峰型,夜晚浓度整体高于白天;PM2．5春、夏、秋三季日变化呈单峰型,PM10夏季和秋季呈单峰型,春季呈双峰型。 PM2．5和PM10日均值有着非常显著的线性相关关系,PM2．5和PM10浓度的比值（p）10月最高。     

沈阳市大气中PM2.5来源解析研究

通过2015年在沈阳市采集PM2.5样品及源类样品,分析样品的质量浓度和化学组成,用化学质量平衡(CMB)模型对该市PM2.5来源进行解析。结果表明:沈阳市大气中PM2.5浓度时空变化特征明显;各主要源类对沈阳市PM2.5的分担率依次为煤烟尘(28.03%)、二次无机离子(22.63%)、机动车尾气尘(17.27%)、城市扬尘(13.28%)、建筑尘(5.94%)、土壤风沙尘(5.82%)、道路尘(3.04%)、生物质燃烧尘(2.74%)和冶金尘(1.25%)。燃煤和机动车的有效控制既能降低本类源的贡献,也能降低二次无机离子,体现了多源类综合治理原则。     

Characteristics of particulate matter pollution in the Pearl River Delta region, China: an observational-based analysis of two monitoring sites

Two-year monitoring data (2006 and 2009), collected at the sub-urban site (WQS) and the background site (TH), were used to study the characteristics of Particulate Matter (PM) pollution in the Pearl River Delta region, China. Similar levels of PM(2.5) concentration measured at both sites seem to confirm that the fine particles have emerged as a major regional pollution issue. The seasonal variation of PM(2.5) concentration is associated with the regional monsoon circulations while the diurnal variation is related to land-sea breeze, traffic emissions and boundary layer development. Negative correlation was found in PM(2.5)-wind speed and PM(2.5)-humidity. Analysis of radiation, temperature and ozone suggests the existence of secondary aerosol formation. Transport effect may be another contributing factor to high PM pollution in the region, such as occasional long-distance dust intrusion and trans-boundary effects from upwind areas.     

成渝地区一次区域重污染过程来源解析及减排效果研究

基于嵌套网格空气质量预报模式(NAQPMS)及耦合的污染来源追踪模块模拟2017年12月16日至2018年1月3日成渝地区一次区域重污染过程,定量解析成渝地区主要城市PM2.5来源,评估过程中应急减排的成效。结果表明,天气静稳和风向辐合是造成此次重污染过程的重要因素,污染峰值阶段,成渝地区多个城市PM2.5日均质量浓度超过150μg/m3,达到重度污染级别。污染过程中,成都市PM2.5本地排放的贡献率为42%,眉山和德阳贡献率将近30%;重庆市PM2.5本地排放的贡献率为60%,外来输送以湖北、湖南和其他地区为主,贡献率为24%,成都和重庆市的工业源和交通源的贡献最大。区域联防联控应急减排对成渝各城市空气质量改善效果显著,成渝地区PM2.5浓度降低率为5%~11%,对于未实施应急预警方案的地区(如眉山市)受周边城市减排影响,浓度降低可达6%。     

Diurnal and intra-urban particle concentrations in relation to windspeed and stability during the dry season in three African cities

The spatial and temporal variations of PM(2.5), PM(10) and TSP in three African cities of different sizes (Dar es Salaam, Ouagadougou and Gaborone) were investigated using portable particle counters. Three different areas (downtown, green residential and traditional residential) and a reference site were designated in each of the cities in order to detect intra-urban and temporal variability. Morning, noon and night measurements were conducted in the urban areas while observations at reference stations were made continuously over the field periods. A clear diurnal pattern in particle concentrations was found in inland Gaborone and Ouagadougou, with morning and night peaks where the latter was the highest. However, in coastal Dar es Salaam the night peak was almost absent due to delayed stabilisation of the air. Particle concentrations at the Ouagadougou reference station were extreme. The direct contribution of vehicle emissions are of secondary importance since the PM(2.5)/PM(10) ratios are low (0.1-0.3). Much of the particles are supposed to be soil particles that are entrained in the air by daytime high windspeeds followed by nighttime subsidence as the air is stabilised and windspeed decreases. However, in all three cities, resuspension are important as areas with a network of unpaved roads showed the highest concentrations of suspended particles. Generally, the central business district had the lowest concentrations of particulate matter.     

A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA

Land use regression (LUR) models have been used successfully for predicting local variation in traffic pollution, but few studies have explored this method for deriving fine particle exposure surfaces. The primary purpose of this method is to develop a LUR model for predicting fine particle or PM(2.5) mass over the five county metropolitan statistical area (MSA) of Los Angeles. PM(2.5) includes all particles with diameter less than or equal to 2.5 microns. In the Los Angeles MSA, 23 monitors of PM(2.5) were available in the year 2000. This study uses GIS to integrate data regarding land use, transportation and physical geography to derive a PM(2.5) dataset covering Los Angeles. Multiple linear regression was used to create the model for predicting the PM(2.5) surface. Our parsimonious model explained 69% of the variance in PM(2.5) with three predictors: (1) traffic density within 300 m, (2) industrial land area within 5000 m, and (3) government land area within 5000 m of the monitoring site. These results suggest the LUR method can refine exposure models for epidemiologic studies in a North American context.     

Air pollution exposure monitoring and estimating. Part I. Integrated air quality monitoring system

This paper presents an integrated exposure monitoring system, based on an expansion of existing air quality monitoring systems using dispersion modelling. The system allows: (1) identifying geographical areas whose inhabitants are most exposed to ambient pollution; (2) identifying how many people in an area are exposed to concentrations of pollution exceeding air quality guidelines; (3) describing the exposure of population subgroups (e.g. children); (4) planning pollution abatement measures and quantifying their effects; (5) establishing risk assessment and management programs, and (6) investigating the short- and long-term effects of both pollutants and pollution sources on public health. The effect of pollution is rarely very large and in order to discover it, exposure estimation must provide data that reflects both spatial and temporal variations. Estimates of pollution exposure are obtained using an integrated approach that combines results of measurements from monitoring programs with dispersion calculations. These values can serve as estimates for individual short-term or long-term exposure. The grouped data allows the expression of ambient pollution concentrations as the spatial distribution of estimates such as the mean or 98th percentile of such compounds as SO2, O3, NO2, PM10 and PM2.5. This integrated approach has been combined into a single software package, AirQUIS.     

Evaluation of the Impact of Long-range Transport and Aerosol Concentration Temporal Variations at the Eastern Coast of the Baltic Sea

Ambient particles vary greatly in their ability to affect visibility, climate and human health. The fine fraction of aerosol is responsible for greater and wider effects on human health; thus, investigation of this fraction is very important. Continuous measurements of PM2.5 (particulate matter below 2.5 μm in size) concentrations at the Preila monitoring station started in 2003. During a period of 2 years, the episodes of high daily and semi-hourly concentrations of PM2.5 were measured. These episodes did not depend on the season or time of day. The substantial role of long-range transport of pollutants to these increases in concentration was shown using chemical and statistical analysis. It was found that most of the severe episodes occurred when air masses came from a specific site besides it was established that air masses of different origin were characterized by different mixing layer depth. Lower mixing depth was observed in air masses characterized by higher observed concentrations at the measuring site and vice versa. PM2.5 concentrations showed diurnal and seasonal variations whose pattern reflected the regional origin of the aerosol. The regional pollution level was evaluated by the statistical analysis of PM2.5 concentrations. The background annual average of PM2.5 mass concentration for the eastern coast of the Baltic Sea was 15.1 ± 0.8 μg m⁻³.     

Assessment of water quality monitoring for the optimal sensor placement in lake Yahuarcocha using pattern recognition techniques and geographical information systems

Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months. A spatial and temporal statistical approach using pattern recognition techniques, such as cluster analysis (CA) and discriminant analysis (DA), was employed to classify and identify the most important water quality parameters in the lake. The original monitoring network was reduced to four optimal sensor locations based on a fuzzy overlay of the interpolations of concentration variations of the most important parameters.     

Phthalate levels in Norwegian indoor air related to particle size fraction

Phthalates are found in numerous consumer products, including interior materials like polyvinyl chloride (PVC). Several studies have identified phthalates in indoor air. A recent case-control study demonstrated associations between allergic symptoms in children and the concentration of phthalates in dust collected from their homes. Here we have analyzed the content of selected phthalates in particulate matter (PM): PM(10) and PM(2.5) filter samples collected in 14 different indoor environments. The results showed the presence of the phthalates di-n-butyl phthalate (DBP), butyl benzyl phthalate (BBP), dicyclohexyl phthalate (DCHP) and diethyl hexyl phthalate (DEHP) in the samples. The dominating phthalate in both PM(10) and PM(2.5) samples from all locations was DBP. More than a 10-fold variation in the mean concentration of total phthalates between sampling sites was observed. The highest levels of total phthalates were detected in one children's room, one kindergarten, in two primary schools, and in a computer room. The relative contribution of total phthalates in PM(10) and PM(2.5) was 1.1 +/- 0.3% for both size fractions. The contribution of total phthalates in PM(2.5) to total phthalates in PM(10) ranged from 23-81%, suggesting different sources. Of the phthalates that were analyzed in the PM material, DBP was found to be the major phthalate in rubber from car tyres. However, our analyses indicate that tyre wear was of minor importance for indoor levels of both DBP as well as total phthalates. Overall, these results support the notion that inhalation of indoor PM contributes to the total phthalate exposure.     

宁波和温州地区夏季大气中不同粒径颗粒物特征分析

对宁波地区北仑和奉化站、温州地区乐清站3个监测点夏季TSP、PM10、PM2.5和PM1.0进行监测,测试分析各种粒径颗粒物浓度水平和粒径分布特征,并通过化学质量平衡(CMB)受体模型对颗粒物进行源解析。监测结果显示,夏季宁波、温州地区TSP和PM10日均浓度为0.049～0.134mg/m3和0.025～0.084mg/m3,均未超过我国环境空气质量二级标准;PM2.5日均浓度为0.007～0.069mg/m3,按美国2006年EPA最新标准限值0.035mg/m3衡量,奉化、乐清、北仑站的超标天数占总监测天数的比例分别为75%、40%和37.5%。粒径分布统计结果显示,3个监测站点PM10占TSP的比例为48.78%～86.96%;PM2.5占TSP的比例为33.33%～72.46%;奉化和乐清监测点PM10中PM2.5和PM1.0的比例平均值在50%以上。源解析结果显示,夏季TSP主要来源于土壤尘,其次是建筑尘和煤烟尘,其贡献率分别为40.70%～55.49%、9.62%～13.64%和5.85%～17.28%。     

Seasonal variability of endotoxin in ambient fine particulate matter

Endotoxin is a toxic, pro-inflammatory compound that has been detected in indoor air and dust in homes and occupational settings, and also in outdoor air. Data on the outdoor sampling of endotoxin are limited. Currently, little is known about the seasonal variation and influence of temperature on outdoor endotoxin levels. In the present study, we report endotoxin levels in fine fraction particulate matter with a 50% aerodynamic cutoff diameter of 2.5 microm (PM2.5) and describe the seasonal variation of endotoxin in Munich, Germany. In 1999-2000, PM2.5 was collected at forty outdoor monitoring sites across Munich. Approximately four samples were collected at each site for a total of 158 samples. Endotoxin concentrations in the PM2.5 samples were determined using the kinetic chromogenic Limulus Amebocyte Lysate (LAL) assay. The geometric mean endotoxin concentration was 1.07 EU mg PM2.5(-1) (95% C.I.: 0.915-1.251) or 0.015 EU m(-3) of sampled air (95% C.I.: 0.013-0.018). Munich endotoxin levels were significantly related to ambient temperature (p < 0.0001) and percent relative humidity (p < 0.0001). Sampling periods with higher average temperatures yielded higher levels of endotoxin in PM2.5 (r = 0.641), whereas decreases in percent relative humidity were associated with increased endotoxin levels in PM2.5 (r = -0.388). Endotoxin levels were significantly higher during the warmer seasons of spring [means ratio (MR): 2.5-2.7] and summer (MR: 2.1-3.0) than during winter. Although temperature and relative humidity do not explain all of the variability in endotoxin levels, their effects were significant in our data set. Temperature effects and seasonal variation of endotoxin should be considered in future studies of outdoor endotoxin.     

Chemical compositions and sources of atmospheric PM10 in heating, non-heating and sand periods at a coal-based city in northeastern China

Mass concentrations and chemical components (18 elements, 9 ions, organic carbon [OC] and elemental carbon [EC]) in atmospheric PM(10) were measured at five sites in Fushun during heating, non-heating and sand periods in 2006-2007. PM(10) mass concentrations varied from 62.0 to 226.3 μg m(-3), with 21% of the total samples' mass concentrations exceeding the Chinese national secondary standard value of 150 μg m(-3), mainly concentrated in heating and sand periods. Crustal elements, trace elements, water-soluble ions, OC and EC represented 20-47%, 2-9%, 13-34%, 15-34% and 13-25% of the particulate matter mass concentrations, respectively. OC and crustal elements exhibited the highest mass percentages, at 27-34% and 30-47% during heating and sand period. Local agricultural residuals burning may contribute to EC and ion concentrations, as shown by ion temporal variation and OC and EC correlation analysis. Heavy metals (Cr, Ni, Zn, Cu and Mn) from coal combustion and industrial processes should be paid attention to in heating and sand periods. The anion/cation ratios exhibited their highest values for the background site with the influence of stationary sources on its upper wind direction during the sand period. Secondary organic carbon were 1.6-21.7, 1.5-23.0, 0.4-17.0, 0.2-33.0 and 0.2-21.1 μg m(-3), accounting for 20-77%, 44-88%, 4-77%, 8-69% and 4-73% of OC for the five sampling sites ZQ, DZ, XH, WH and SK, respectively. From the temporal and spatial variation analysis of major species, coal combustion, agricultural residual burning and industrial emission including dust re-suspended from raw material storage piles were important sources for atmospheric PM(10) in Fushun at heating, non-heating and sand periods, respectively. It was confirmed by principal component analysis that coal combustion, vehicle emission, industrial activities, soil dust, cement and construction dust and biomass burning were the main sources for PM(10) in this coal-based city.     

2018—2021年安徽省PM2.5和O3时空分布特征及其健康风险分析

利用2018—2021年安徽省空气质量监测数据分析了PM_2.5和O₃时空分布特征及其引发的健康风险。结果表明:从时间分布来看,2018—2021年安徽省PM_2.5年均值下降25.5%,而O₃-8 h年均值则保持持平;PM_2.5和O₃-8 h月均值具有明显的季节变化特征,PM_2.5月均质量浓度和超标天数均在冬季达到最大值,O₃-8 h月均值和超标天数则在夏季达到最大值。从空间分布来看,PM_2.5、O₃-8 h年均值和超标天数均为皖北最高,其次为皖中,最后为皖南。夏季O₃是主要的健康风险因子,冬季PM_2.5是主要的健康风险因子。当PM_2.5超标时,除2021年皖北地区外(PM₁₀是主要的健康风险因子),PM_2.5均是主要的健康风险因子;当O₃-8 h超标时,O₃是主要的健康风险因子。