The PM2.5 and PM10 particles in urban areas of Taiwan

This study conducted an atmospheric aerosol sampling to measure the PM10 (particles < 10 microns in aerodynamic diameter) and PM2.5 (particles < 2.5 microns in aerodynamic diameter) mass concentrations from October 1996 to June 1997 in northern (Taipei), central (Taichung) and southern (Kaohsiung), the three largest cities of Taiwan. Seventy-eight samples were obtained to measure the mass concentrations of PM10 and PM2.5 from nine sampling sites. According to those results, the PM10 mass concentrations in Taipei, Taichung and Kaohsiung were 42.19, 60.99 and 77.10 micrograms/m3, respectively. The corresponding PM2.5 mass concentrations were 23.09, 39.97 and 48.47 micrograms/m3, respectively. The PM2.5 fraction accounted for 61-67% of the PM10 mass in central and southern Taiwan, but was lower (54-59%) in northern Taiwan. Some samples in which the PM2.5 fraction was overwhelmingly dominant could reach as high as 80-95% of the PM10 mass. In addition, the PM2.5, PM10 levels and PM2.5/PM10-2.5 (particles with aerodynamic diameters ranging from 2.5 to 10 microns) ratios in metropolitan Taiwan significantly fluctuated from site-to-site and over time. Moreover, ambient daily PM2.5 and PM10-2.5 mass concentrations did not correlate well with each other at most of the sampling sites, indicated that they originated from different kinds of sources and emitted variedly over time.     

The characteristic study of TSP, PM2.5-10 and PM2.5 in the rural site of central Taiwan.

The total suspended particle (TSP), PM2.5-10 (aerodynamic diameter less than 10 microns) and PM2.5 concentration (aerodynamic diameter less than 2.5 microns) concentrations were sampled by PS-1 and Universal sampler on the roof (25 m) of the Medical and Engineering Building in the campus of Hungkuang Institute of Technology (HKIT) which is located at a height of 500 m on Da Du Mountain. The results indicated that average TSP, PM2.5-10 and PM2.5 concentrations are 0.42, 0.34 and 0.019 mg/m3 in the day time, respectively and are 0.32, 0.26 and 0.017 mg/m3 in the night time, respectively. The ratios of PM2.5-10/TSP were from 76% to 85% and from 50% to 91% for day and night period, respectively. It indicated that the major composition in the total suspended particles was PM2.5-10 in the rural site. The relationship between TSP and PM2.5-10 is TSP = 1.16PM2.5-10 + 0.027 and TSP = 1.01 PM2.5-10 + 0.058 in the day and night time, respectively. The correlation coefficient (R2) is 0.98 and 0.97 for day and night period, respectively. The relationship between PM2.5-10 and PM2.5 is PM2.5 = 0.0005PM2.5-10 + 0.019 and PM2.5 = 0.037PM2.5-10 + 0.0076 in the day and night period, respectively. The correlation coefficient (R2) is 3E-5 and 0.67 for day and night period, respectively. The relationships between TSP, PM2.5-10, PM2.5 particle concentrations and wind speed (R2) in the day time are 0.71, 0.64, 0.43, respectively and are 0.83, 0.79, 0.57, respectively in the night time. The proposed reasons are that there are more activities caused by people (students) and natural living animals which absorbed some of the particles during the day time. Thus, the correlation coefficients for the night time are better than those of day time. The particle size distributions are both bimodel in the day and night time. The major peaks in the day time appear in the particle diameter between 0.031-0.056 micron and 3.16-5.62 microns in the day period and appear between 0.017-0.031 micron and 1.78-3.16 microns in the night period. The results indicate that the particle size distribution in the day time tends to be of larger particle size mode than the night time.     

Characterization of chemical species in PM2.5 and PM10 aerosols in suburban and rural sites of central Taiwan.

Aerosol samples for PM2.5 (particulate matter with aerodynamic diameters less than 2.5 microns), PM2.5-10 (particulate matter with aerodynamic diameters between 2.5 and 10 microns) and TSP were collected from June to September 1998 at THU (suburban) and HKIT (rural) sites in central Taiwan. The ratios of PM2.5/PM10 averaged 0.70 for the daytime and 0.63 for the nighttime at THU, respectively. At HKIT, the PM2.5/PM10 ratios averaged 0.56 for the daytime and 0.72 in the nighttime, respectively. These results indicated that the PM2.5 concentrations contribute the majority of the PM10 concentration and PM10 concentrations contribute the majority of the TSP at both sites. The averaged PM2.5 concentrations at THU are higher than those measured at HKIT during the daytime period. However, the average PM2.5-10 concentrations in THU are lower than those measured at HKIT during nighttime. The samples collected were also analyzed by atomic absorption spectrophotometry for the elemental analysis of Ca, Fe, Pb, Zn, Cu, Mn and Cr. Meanwhile ion chromatography was used to analyze for the water-soluble ions: sulphate, nitrate and chloride in the Universal samples. The concentrations of heavy metals in PM10 during daytime were all higher than nighttime at THU. However, the averaged concentrations of metal elements in PM10 during day and night period were distributed irregularly at HKIT. The results indicated that for metal elements collected at HKIT have different emission sources. The concentrations of metal elements during daytime in PM10 at THU were generally higher than HKIT. The phenomena owing to the averaged PM2.5 particle concentrations at THU (suburban) were higher than those measured at HKIT (rural) and PM2.5 occupied the major portions of PM10 for both sites during the day period. For anion species, there are no significant differences between day and night period in PM10 concentrations at both suburban and rural sites.     

Hospital indoor PM10/PM2.5 and associated trace elements in Guangzhou, China

PM10 and PM2.5 samples were collected in the indoor environments of four hospitals and their adjacent outdoor environments in Guangzhou, China during the summertime. The concentrations of 18 target elements in particles were also quantified. The results showed that indoor PM2.5 levels with an average of 99 microg m(-3) were significantly higher than outdoor PM2.5 standard of 65 microg m(-3) recommended by USEPA [United States Environmental Protection Agency. Office of Air and Radiation, Office of Air Quality Planning and Standards, Fact Sheet. EPA's Revised Particulate Matter Standards, 17, July 1997] and PM2.5 constituted a large fraction of indoor respirable particles (PM10) by an average of 78% in four hospitals. High correlation between PM2.5 and PM10 (R(2) of 0.87 for indoors and 0.90 for outdoors) suggested that PM2.5 and PM10 came from similar particulate emission sources. The indoor particulate levels were correlated with the corresponding outdoors (R(2) of 0.78 for PM2.5 and 0.67 for PM10), demonstrating that outdoor infiltration could lead to direct transportation into indoors. In addition to outdoor infiltration, human activities and ventilation types could also influence indoor particulate levels in four hospitals. Total target elements accounted for 3.18-5.56% of PM2.5 and 4.38-9.20% of PM10 by mass, respectively. Na, Al, Ca, Fe, Mg, Mn and Ti were found in the coarse particles, while K, V, Cr, Ni, Cu, Zn, Cd, Sn, Pb, As and Se existed more in the fine particles. The average indoor concentrations of total elements were lower than those measured outdoors, suggesting that indoor elements originated mainly from outdoor emission sources. Enrichment factors (EF) for trace element were calculated to show that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) were highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Factor analysis was used to identify possible pollution source-types, namely street dust, road traffic and combustion processes.     

不同建筑环境空气中颗粒物PM1.0、PM2.5和PM10的污染水平及对比研究

为了解人们常停留的建筑室内空气中不同粒径段颗粒物的污染水平,本文对写字楼、地铁站台、餐饮环境、大学教室和宿舍等不同类型建筑室内和室外空气中的颗粒物PM10、PM2.5和PM10的质量浓度水平进行了测试、统计分析和对比研究,同时对不同建筑环境内不同粒径段颗粒物的浓度大小、占比情况和主要来源进行了分析探讨.结果 表明:1)...     

Comparative PM10-PM2.5 source contribution study at rural, urban and industrial sites during PM episodes in Eastern Spain

In this study a set of 340 PM10 and PM2.5 samples collected throughout 16 months at rural, an urban kerbside and an industrial background site (affected by the emissions from the ceramic manufacture and other activities) were interpreted. On the regional scale, the main PM10 sources were mineral dust (mainly Al2O3, Fe, Ti, Sr, CaCO3, Mg, Mn and K), emissions derived from power generation (SO4=, V, Zn and Ni), vehicle exhausts (organic and elemental carbon, NO3- and trace elements) and marine aerosol (Na, Cl and Mg). The latter was not identified in PM2.5. At the industrial site, additional PM10 sources were identified (tile covering in the ceramic production, petrochemical emissions and bio-mass burning from a large orange tree cultivation area). The contribution of each PM source to PM10 and PM2.5 levels experiences significant variations depending on the type of PM episode (Local-urban mainly in autumn-winter, regional mainly in summer, African or Atlantic episode), which are discussed in this study. The results show that it would be very difficult to meet the EU limit values for PM10 established for 2010. The annual mean PM levels are 22.0 microg PM10/m3 at the rural and 49.5 microg PM10/m3 and 33.9 microg PM2.5/m3 at the urban site. The natural contribution in this region, estimated at 6 microg/m3 of natural mineral dust (resulting from the African events and natural resuspension) and 2 microg/m3 of marine aerosol, accounts for 40% of the 2010 EU annual limit value (20 microg PM10/m3). Mineral dust concentrations at the urban and industrial sites are higher than those at the rural site because of the urban road dust and the ceramic-production contributions, respectively. At the urban site, the vehicle exhaust contribution (17 microg/m3) alone is very close to the 2010 EU PM10 limit value. At the rural site, the African dust is the main contributor to PM10 levels during the highest daily mean PM10 events (100th-97th percentile range). At the urban site, the vehicle exhaust product is the main contributor to PM10 and PM2.5 levels during the highest daily mean PM events (100th-85th percentile range). Mineral dust concentrations during African dust events accounts for 20-30 microg/m3 in PM10 and 10-15 microg/m3 in PM2.5. During non-African dust events, mineral dust derived from anthropogenic activities (e.g. urban road dust) is also a significant contributor to PM10, but not to PM2.5.     

Approaching PM(2.5) and PM(2.5-10) source apportionment by mass balance analysis, principal component analysis and particle size distribution

A chemical characterization was carried out for PM(2.5) and PM(2.5-10) samples collected in a suburban area and the concentrations of 12 elements were determined in 8 size segregated fractions using a Berner Impactor. Two main objectives were proposed in this work: 1) to test for closure among chemical and gravimetric measurements of PM(2.5) and PM(2.5-10) and 2) evaluate the performance of Multilinear Regression Analysis (MLRA) and Mass Balance Analysis (MBA) in the determination of source contribution to Particulate Matter (PM) concentrations. The fraction unaccounted for by chemical analysis comprised on average 17% and 34% of gravimetric PM(2.5) and PM(2.5-10), respectively. The lack of closure in PM(2.5) and PM(2.5-10) mass (i.e., constituent concentrations not adding up to gravimetrically measured) could partly result from the presence of water associated with particles and errors in the estimation of unmeasured species. MLRA and MBA showed very similar results for the temporal variation of the source contributions. However, quantitatively important discrepancies could be observed, principally due to the lack of mass closure in PM(2.5) and PM(2.5-10). Both methods indicated that the major PM(2.5) aerosol mass contributors included secondary aerosol and vehicle exhaust. In the coarse fraction, marine and mineral aerosol contributions were predominant.     

不同城市绿地类型对PM 2.5 、PM 10 以及TSP的消减效应研究

一直以来,绿地系统都是城市户外 公共活动、文化展示、景观美化等功能的重 要载体。此外,基于景观生态学的绿地系统 研究使其生态调节功能进一步强化。作为城 市的主要户外公共空间,城市绿地的空气质 量关乎整个城市居民的健康,虽然已有部分 研究开始初步探索城市绿地在消减空气颗 粒物上的作用,但仍缺乏更加系统性的对比 研究,以揭示不同绿地类型中绿地率、植物 群落结构等特征对空气颗粒物的不同消减 效应。本文采用定量测定的方法,对重庆市 典型的绿地类型进行全年的测试分析。研究 表明：绿地率最高的公园绿地对于空气颗粒 物的消减率最明显;在一年四季中,TSP在夏 季的消减率最高,PM 10 的消减率春季最高, PM 2.5 的消减率冬季最高;在不同空气颗粒物 的消减率对比分析中,各种城市绿地对TSP 的消减率最高,能达到15%以上,对PM 2.5 的 消减率最低,在夏季甚至出现负值。由此可见,城市绿地对总悬浮颗粒物（TSP）和粗颗粒物（PM 10 ）都具有很好的消减效果,但是在对细 颗粒物(PM 2.5 )的消减效果则不明显。     

Chemical composition of PM2.5 and PM10 in Mexico City during winter 1997

PM2.5 and PM10 were measured over 24-h intervals at six core sites and at 25 satellite sites in and around Mexico City from 23 February to 22 March 1997. In addition, four 6-h samples were taken each day at three of the core sites. Sampling locations were selected to represent regional, central city, commercial, residential, and industrial portions of the city. Mass and light transmission concentrations were determined on all of the samples, while elements, ions and carbon were measured on approximately two-thirds of the samples. PM10 concentrations were highly variable, with almost three-fold differences between the highest and lowest concentrations. Fugitive dust was the major cause of PM10 differences, although carbon concentrations were also highly variable among the sampling sites. Approximately 50% of PM10 was in the PM2.5 fraction. The majority of PM mass was comprised of carbon, sulfate, nitrate, ammonium and crustal components, but in different proportions on different days and at different sites. The largest fine-particle components were carbonaceous aerosols, constituting approximately 50% of PM2.5 mass, followed by approximately 30% secondary inorganic aerosols and approximately 15% geological material. Geological material is the largest component of PM10, constituting approximately 50% of PM10 mass, followed by approximately 32% carbonaceous aerosols and approximately 17% secondary inorganic aerosols. Sulfate concentrations were twice as high as nitrate concentrations. Sulfate and nitrate were present as ammonium sulfate and ammonium nitrate. Approximately two-thirds of the ammonium sulfate measured in urban areas appears to have been transported from regions outside of the study domain, rather than formed from emissions in the urban area. Diurnal variations are apparent, with two-fold increases in concentration from night-time to daytime. Morning samples had the highest PM2.5 and PM10 mass, secondary inorganic aerosols and carbon concentrations, probably due to a shallow surface inversion and rush-hour traffic.     

地铁车站PM2.5和PM10颗粒物浓度实测及分析

为了进一步了解地铁车站内环境中的颗粒物浓度分布情况,在2015年11月对上海市A、B两个地铁车站进行了实地监测,分析了PM2.5和PM10颗粒物浓度在一天中的变化规律及其影响因素.测试结果显示站厅公共区,站台公共区与轨行区的PM2.5浓度在监测时段内逐时变化规律相似.站厅公共区,站台公共区PM10与PM2.5在监测时段...     

Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at urban traffic intersection during winter period

There are several models that can be used to evaluate roadside air quality. The comparison of the operational performance of different models pertinent to local conditions is desirable so that the model that performs best can be identified. Three air quality models, namely the 'modified General Finite Line Source Model' (M-GFLSM) of particulates, the 'California Line Source' (CALINE3) model, and the 'California Line Source for Queuing & Hot Spot Calculations' (CAL3QHC) model have been identified for evaluating the air quality at one of the busiest traffic intersections in the city of Guwahati. These models have been evaluated statistically with the vehicle-derived airborne particulate mass emissions in two sizes, i.e. PM10 and PM2.5, the prevailing meteorology and the temporal distribution of the measured daily average PM10 and PM2.5 concentrations in wintertime. The study has shown that the CAL3QHC model would make better predictions compared to other models for varied meteorology and traffic conditions. The detailed study reveals that the agreements between the measured and the modeled PM10 and PM2.5 concentrations have been reasonably good for CALINE3 and CAL3QHC models. Further detailed analysis shows that the CAL3QHC model performed well compared to the CALINE3. The monthly performance measures have also led to the similar results. These two models have also outperformed for a class of wind speed velocities except for low winds (<1 m s(-1)), for which, the M-GFLSM model has shown the tendency of better performance for PM10. Nevertheless, the CAL3QHC model has outperformed for both the particulate sizes and for all the wind classes, which therefore can be optional for air quality assessment at urban traffic intersections.     

Source apportionment of PM10 and PM2.5 in Milan (Italy) using receptor modelling

In this paper a source apportionment of particulate matter pollution in the urban area of Milan (Italy) is given. Results of PM10 and PM2.5 mass and elemental concentrations from a 1-year monitoring campaign are presented. Mean annual and daily PM10 levels are compared with the limits of the EU Air Quality Directive EC/30/1999 and the results show that the limit values established would not be met in the urban area of Milan or the large surrounding area. Moreover, high levels of PM2.5 are registered and this fraction constitutes a high portion of the PM10 mass. In Milan the winter period is characterised by a high degree of air pollution due to a greater contribution of emissions and to adverse meteorological and thermodynamic conditions of the atmosphere. The application of multivariate techniques and receptor modelling (PCFA, APCFA) to the whole data-set led to the identification of the main emitting sources and to the source apportionment of PM10 and PM2.5 in Milan. The most important sources were identified as 'soil dust', 'traffic', 'industry' and 'secondary compounds' for PM10 and as 'soil dust', 'anthropogenic' and 'secondary compounds' for PM2.5, explaining the greatest part of the total variance (91% and 75%, respectively).     

Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM_2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM_2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM_2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM_2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM_2.5 monitoring using low-cost light-scattering sensors.     

居住区室外开敞空间PM2.5、PM10时空分布特征及居民暴露风险评估

研究利用Reynolds Averaged Navier-Stokes Model与Revised Drift Flux Model模拟分析了居住区室外开敞空间中PM2.5、PM10浓度的时空分布,利用行为制图建立场地中居民活动与PM2.5、PM10浓度分布的时空映射,并依据世界卫生组织的空气质量标准(IT-1)评估了...     

国内外PM_(2.5)控制和治理措施评述

<正>PM2.5是指空气中空气动力学当量直径小于等于2.5微米的悬浮颗粒物。它在大气中停留时间长、输送距离远,对人体健康和大气环境质量的危害极大,是目前包括北京在内的世界各国大城市空气污染重点治理对象。环境治理是指为维护城市区域的环境次序和环境安全,实现城市经济可持续发展,城市各级政府依据国家和当地的环境政策、环境法规和标准,运用法律、经济、行政、技术和教育手段调控人类生活行为,限制人类损害城市环境的有关行为的总称。本文主要从经济和技术两个方面综述国内外PM2.5控制和治理措施。     

Source apportionment of PM(10) and PM(2.5) using positive matrix factorization and chemical mass balance in Izmir, Turkey

Atmospheric particulate matter (PM) fractions (PM(10) and PM(2.5)) were sampled concurrently between June 2004 and May 2005 at two sites (urban and suburban) in Izmir, Turkey. The elemental composition of PM (Al, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Sr, V, and Zn) was determined using inductively coupled plasma-optical emission spectrometer. Elemental compositions of several PM sources were also characterized. Positive matrix factorization (PMF) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions to air concentrations. The major contributors to PM were fossil fuel burning, traffic emissions, mineral industries and marine salt according to the PMF results. However, undetermined parts were more than 40%. On the other hand, the contributions to PM could be determined completely by CMB, and the dominant contributor was traffic with >70% at the two sites. Fossil fuel burning, mineral industries, marine salt and natural gas-fired power plant were the minor contributors.     

Concurrent assessment of personal,indoor, and outdoor PM2.5 and PM1 levels and source contributions using novel low-cost sensing devices

The intensity, frequency, duration, and contribution of distinct PM_2.5 sources in Asian households have seldom been assessed; these are evaluated in this work with concurrent personal, indoor, and outdoor PM_2.5 and PM₁ monitoring using novel low-cost sensing (LCS) devices, AS-LUNG. GRIMM-comparable observations were acquired by the corrected AS-LUNG readings, with R² up to 0.998. Twenty-six non-smoking healthy adults were recruited in Taiwan in 2018 for 7-day personal, home indoor, and home outdoor PM monitoring. The results showed 5-min PM_2.5 and PM₁ exposures of 11.2 ± 10.9 and 10.5 ± 9.8 µg/m³, respectively. Cooking occurred most frequently; cooking with and without solid fuel contributed to high PM_2.5 increments of 76.5 and 183.8 µg/m³ (1 min), respectively. Incense burning had the highest mean PM_2.5 indoor/outdoor (1.44 ± 1.44) ratios at home and on average the highest 5-min PM_2.5 increments (15.0 µg/m³) to indoor levels, among all single sources. Certain events accounted for 14.0%-39.6% of subjects’ daily exposures. With the high resolution of AS-LUNG data and detailed time-activity diaries, the impacts of sources and ventilations were assessed in detail.