Concentrations,properties, and health risk of PM2.5 in the Tianjin City subway system

A campaign was conducted to assess and compare the personal exposure in L3 of Tianjin subway, focusing on PM_2.5 levels, chemical compositions, morphology analysis, as well as the health risk of heavy metal in PM_2.5. The results indicated that the average concentration of the PM_2.5 was 151.43 μg/m³ inside the train of the subway during rush hours. PM_2.5 concentrations inside car under the ground are higher than those on the ground, and PM_2.5 concentrations on the platform are higher than those inside car. Regarding metal concentrations, the highest element in PM_2.5 samples was Fe; the level of which is 17.55 μg/m³. OC is a major component of PM_2.5 in Tianjin subway. Secondary organic carbon is the formation of gaseous organic pollutants in subway. SEM–EDX and TEM–EDX exhibit the presence of individual particle with a large metal content in the subway samples. For small Fe metal particles, iron oxide can be formed easily. With regard to their sources, Fe-containing particles are generated mainly from mechanical wear and friction processes at the rail–wheel–brake interfaces. The non-carcinogenic risk to metals Cr, Ni, Cu, Zn and Pb, and carcinogenic hazard of Cr and Ni were all below the acceptable level in L3 of Tianjin subway.

     

Quantifying road dust resuspension in urban environment by Multilinear Engine: A comparison with PMF2

Atmospheric PM pollution from traffic comprises not only direct emissions but also non-exhaust emissions because resuspension of road dust that can produce high human exposure to heavy metals, metalloids, and mineral matter. A key task for establishing mitigation or preventive measures is estimating the contribution of road dust resuspension to the atmospheric PM mixture. Several source apportionment studies, applying receptor modeling at urban background sites, have shown the difficulty in identifying a road dust source separately from other mineral sources or vehicular exhausts. The Multilinear Engine (ME-2) is a computer program that can solve the Positive Matrix Factorization (PMF) problem. ME-2 uses a programming language permitting the solution to be guided toward some possible targets that can be derived from a priori knowledge of sources (chemical profile, ratios, etc.). This feature makes it especially suitable for source apportionment studies where partial knowledge of the sources is available.In the present study ME-2 was applied to data from an urban background site of Barcelona (Spain) to quantify the contribution of road dust resuspension to PM₁₀ and PM_2.5 concentrations. Given that recently the emission profile of local resuspended road dust was obtained (Amato, F., Pandolfi, M., Viana, M., Querol, X., Alastuey, A., Moreno, T., 2009. Spatial and chemical patterns of PM₁₀ in road dust deposited in urban environment. Atmospheric Environment 43 (9), 1650–1659), such a priori information was introduced in the model as auxiliary terms of the object function to be minimized by the implementation of the so-called “pulling equations”.ME-2 permitted to enhance the basic PMF solution (obtained by PMF2) identifying, beside the seven sources of PMF2, the road dust source which accounted for 6.9 μg m^?3 (17%) in PM₁₀, 2.2 μg m^?3 (8%) of PM_2.5 and 0.3 μg m^?3 (2%) of PM₁. This reveals that resuspension was responsible of the 37%, 15% and 3% of total traffic emissions respectively in PM₁₀, PM_2.5 and PM₁. Therefore the overall traffic contribution resulted in 18 μg m^?3 (46%) in PM₁₀, 14 μg m^?3 (51%) in PM_2.5 and 8 μg m^?3 (48%) in PM₁. In PMF2 this mass explained by road dust resuspension was redistributed among the rest of sources, increasing mostly the mineral, secondary nitrate and aged sea salt contributions.     

Installation of platform screen doors and their impact on indoor air quality: Seoul subway trains

In this study, variations of particulate matter (PM) concentrations in subway trains following installation of platform screen doors (PSDs) in the Seoul subway system were investigated. PM samples were collected in the trains on subway lines 1–8 before and after installation of PSDs. It was found that the mean PM₁₀ concentration in the trains after PSDs installation increased significantly by 29.9% compared to that before installation. In particular, the increase of PM₁₀ in line 6 was the highest at 103%. When the relationship between PM₁₀ and PM_2.5 was compared, coefficients of determination (r²) before and after PSDs installations were 0.696 and 0.169, respectively. This suggests that air mixing between the platform and the tunnel after PSDs installation was extremely restricted. In addition, the indoor/outdoor PM₁₀ ratio following PSDs installation increased from 1.32 to 2.97 relative to the period with no installed PSDs. Furthermore, this study revealed that PM levels in subway trains increased significantly after all underground PSDs were put in use. Several potential factors were examined that could result in this PM increase, such as train ventilation systems, operational conditions, passenger volume, subway depth, and the length of underground segments.

ImplicationsPM₁₀ concentrations inside the subway trains increased after PSDs installation. This indicates that air quality in trains was very seriously impacted by PSDs. PM₁₀ levels were also influenced by the tunnel depth and length of the underground segments. To prevent the adverse effect on human health by PM₁₀ emitted from the tunnel, an applicable ventilation system to reduce PM₁₀ is required inside trains and tunnels.     

Concentration and characterization of airborne particles in Tehran’s subway system

Particulate matter is an important air pollutant, especially in closed environments like underground subway stations. In this study, a total of 13 elements were determined from PM₁₀ and PM_2.5 samples collected at two subway stations (Imam Khomeini and Sadeghiye) in Tehran’s subway system. Sampling was conducted in April to August 2011 to measure PM concentrations in platform and adjacent outdoor air of the stations. In the Imam Khomeini station, the average concentrations of PM₁₀ and PM_2.5 were 94.4?±?26.3 and 52.3?±?16.5 μg m^?3 in the platform and 81.8?±?22.2 and 35?±?17.6 μg m^?3 in the outdoor air, respectively. In the Sadeghiye station, mean concentrations of PM₁₀ and PM_2.5 were 87.6?±?23 and 41.3?±?20.4 μg m^?3 in the platform and 73.9?±?17.3 and 30?±?15 μg m^?3, in the outdoor air, respectively. The relative contribution of elemental components in each particle fraction were accounted for 43 % (PM₁₀) and 47.7 % (PM_2.5) in platform of Imam Khomeini station and 15.9 % (PM₁₀) and 18.5 % (PM_2.5) in the outdoor air of this station. Also, at the Sadeghiye station, each fraction accounted for 31.6 % (PM₁₀) and 39.8 % (PM_2.5) in platform and was 11.7 % (PM₁₀) and 14.3 % (PM_2.5) in the outdoor. At the Imam Khomeini station, Fe was the predominant element to represent 32.4 and 36 % of the total mass of PM₁₀ and PM_2.5 in the platform and 11.5 and 13.3 % in the outdoor, respectively. At the Sadeghiye station, this element represented 22.7 and 29.8 % of total mass of PM₁₀ and PM_2.5 in the platform and 8.7 and 10.5 % in the outdoor air, respectively. Other major crustal elements were 5.8 % (PM₁₀) and 5.3 % (PM_2.5) in the Imam Khomeini station platform and 2.3 and 2.4 % in the outdoor air, respectively. The proportion of other minor elements was significantly lower, actually less than 7 % in total samples, and V was the minor concentration in total mass of PM₁₀ and PM_2.5 in both platform stations.     

Particulate matter source apportionment in a village situated in industrial region of Central Europe

The bilinear receptor model positive matrix factorization (PMF) was used to apportion particulate matter with an aerodynamic diameter of 1–10 μm (PM_1–10) sources in a village, B?ezno, situated in an industrial region of northern Bohemia in Central Europe. The receptor model analyzed the data sets of 90- and 60-min integrations of PM_1–10 mass concentrations and elemental composition for 27 elements. The 14-day sampling campaigns were conducted in the village in summer 2008 and winter 2010. Also, to ensure seasonal and regional representativeness of the data sets recorded in the village, the spatial-temporal variability of the 24-hr PM₁₀ and PM_1–10 within 2008–2010 in winter and summer across the multiple sites was evaluated. There were statistically significant interseasonal differences of the 24-hr PM data, but not intrasummer or intrawinter differences of the 24-hr PM_1–10 data across the multiple sites. PMF resolved seven sources of PM_1–10. They were high-temperature coal combustion; combustion in local heating boilers; marine aerosol; mineral dust; primary biological/wood burning; road dust, car brakes; and gypsum. The main summer factors were assigned to mineral dust (38.2%) and primary biological/wood burning (33.1%). In winter, combustion factors dominated (80%) contribution to PM_1–10. The conditional probability function (CPF) helped to identified local sources of PM_1–10. The source of marine aerosol from the North Sea and English Channel was indicated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT).

Implications: This is the first application of PMF to highly time/size resolved PM data in Czech Republic. The coarse aerosol fraction, PM_1–10, was chosen with regard to industrial character of the region, sampling site near the coal strip mine and coal power stations. Contrary to expectation, source apportionment did not show dominance of emissions from the coal strip mine. The results will enable local authorities and state bodies responsible for air quality assessment to focus on sources most responsible for air pollution in this industrial region.

Supplemental Materials:?Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for (1) details of measurement campaigns; (2) CPF for each of the sources contributing to PM_1–10; (3) factors contribution to PM_1–10 resolved by PMF; (4) diurnal pattern of road dust, car brake factor in summer and winter; (5) trajectories during the marine aerosol episode in winter 2010; and (6) temporal temperature, concentration, and wind speed relationships during the summer 2008 campaign and winter 2010 campaign.     

Source identification of particulate matter collected at underground subway stations in Seoul,Korea using quantitative single-particle analysis

Subway particle samples collected at four underground subway stations in Seoul, Korea were characterized by a single-particle analytical technique, low-Z particle electron probe X-ray microanalysis. To clearly identify indoor sources of subway particles, four sets of samples collected in tunnels, at platforms, near ticket offices, and outdoors were investigated. For the samples collected in tunnels, Fe-containing particles predominate, with relative abundances of 75–91% for the four stations. The amounts of Fe-containing particles decrease as the distance of sampling locations from the tunnel increases. In addition, samples collected at the platform in subway stations with platform screen doors (PSDs) that limit air-mixing between the platform and the tunnel showed marked decreases in relative abundances of Fe-containing particles, clearly indicating that Fe-containing subway particles are generated in the tunnel. PM₁₀ mass concentration levels are the highest in the tunnels, becoming lower as the distance of sampling locations from the tunnel increases. The extent of the decrease in PM₁₀ in stations with PSDs is also larger than that in stations without PSDs. The results clearly indicate that Fe-containing particles originating in tunnels predominate in the indoor microenvironment of subway stations, resulting in high indoor PM₁₀ levels, and that PSDs play a significant role in reducing Fe-containing particles at platforms and near ticket offices.     

Coarse Particulate Matter Apportionment around a Steel Smelter Plant

Abstract

The results from a study carried out in the urban area of Genoa, Italy, where a large steel smelter recently shut down are presented. We had the opportunity to sample particulate matter (PM) before and after plant closure and, therefore, to measure the changes in concentration and composition of PM₁₀ (atmospheric PM with aerodynamic diameter <10 µm). Elemental concentrations of Na to Pb were obtained through energy dispersive X-ray fluorescence (ED-XRF), and the contributions of specific sources of PM₁₀ were calculated by positive matrix factorization (PMF). The PM₁₀ average concentration turned out to be surprisingly similar before and after closing of the smelter. Nevertheless, the comparison among data collected in the two periods (plant operating and closed), even with the limited information provided by ED-XRF, allowed us to single out two sources of PM related to the smelter activities, to extract their emission profile, and to quantify the impact of the plant on PM₁₀ levels.     

PM2.5 concentrations in London for 2008–A modeling analysis of contributions from road traffic

We report on the analysis of contributions from road traffic emissions to fine particulate matter (PM_2.5) concentrations within London for 2008 with the OSCAR Air Quality Assessment System. A spatiotemporal evaluation of the OSCAR system has been conducted with measurements from the London air quality network (LAQN). For the predicted and measured hourly time series of concentrations at 18 sites in London, the medians of correlation, mean absolute error, index of agreement, and factor of two (FAC2) of all stations were 0.80, 4.1 μg/m³, 0.86, and 74%, respectively. Spatial evaluation of modeled and observed annual mean concentrations also showed a fairly good agreement, with all the values falling within the FAC2 range. According to model predictions, the urban increment (including the contributions from urban traffic and other urban sources) was evaluated to be on the average 18%, 33%, 39%, and 43% of the total PM_2.5 in suburban environments, in the urban background, near roads, and near busy roads, respectively. However, the highest values of the urban traffic increment can be around 50% of the total PM_2.5 concentrations near motorways and major roads. The total concentrations (including regional background, and the contributions from urban traffic and other urban sources) can therefore be almost three times the regional background. The total urban increment close to busy roads was around 7–8 μg/m³, in which the estimated traffic contribution is more than 2 μg/m³. On the average, urban traffic contributes approximately 1 μg/m³ of PM_2.5 to the urban background across London. According to modeling, approximately two-thirds of the traffic increment originated from exhaust emissions and most of the rest was due to brake and tire wear.

Implications: The urban increment and traffic contribution to the total PM_2.5 are significant and spatially heterogeneous across London. The highly heterogeneous distribution of PM_2.5 hence requires detailed modeling studies to be carried out at high spatial resolution, which can be particularly important for exposure and health impact assessment. This type of information can be used to quantify health impacts resulting from specific sources of PM_2.5 such as traffic emissions, to aid city and national decision makers when formulating pollution control strategies.     

PM10 source apportionment in a Swiss Alpine valley impacted by highway traffic

Although trans-Alpine highway traffic exhaust is one of the major sources of air pollution along the highway valleys of the Alpine regions, little is known about its contribution to residential exposure and impact on respiratory health. In this paper, source-specific contributions to particulate matter with an aerodynamic diameter?<?10 μm (PM₁₀) and their spatio-temporal distribution were determined for later use in a pediatric asthma panel study in an Alpine village. PM₁₀ sources were identified by positive matrix factorization using chemical trace elements, elemental, and organic carbon from daily PM₁₀ filters collected between November 2007 and June 2009 at seven locations within the village. Of the nine sources identified, four were directly road traffic-related: traffic exhaust, road dust, tire and brake wear, and road salt contributing 16 %, 8 %, 1 %, and 2 % to annual PM₁₀ concentrations, respectively. They showed a clear dependence with distance to highway. Additional contributions were identified from secondary particles (27 %), biomass burning (18 %), railway (11 %), and mineral dust including a local construction site (13 %). Comparing these source contributions with known source-specific biomarkers (e.g., levoglucosan, nitro-polycyclic aromatic hydrocarbons) showed high agreement with biomass burning, moderate with secondary particles (in winter), and lowest agreement with traffic exhaust.     

Application of positive matrix factorization in characterization of PM(10) and PM(2.5) emission sources at urban roadside

The 24-h average coarse (PM₁₀) and fine (PM_2.5) fraction of airborne particulate matter (PM) samples were collected for winter, summer and monsoon seasons during November 2008-April 2009 at an busy roadside in Chennai city, India. Results showed that the 24-h average ambient PM₁₀ and PM_2.5 concentrations were significantly higher in winter and monsoon seasons than in summer season. The 24-h average PM₁₀ concentration of weekdays was significantly higher (12-30%) than weekends of winter and monsoon seasons. On weekends, the PM_2.5 concentration was found to slightly higher (4-15%) in monsoon and summer seasons. The chemical composition of PM₁₀ and PM_2.5 masses showed a high concentration in winter followed by monsoon and summer seasons.The U.S.EPA-PMF (positive matrix factorization) version 3 was applied to identify the source contribution of ambient PM₁₀ and PM_2.5 concentrations at the study area. Results indicated that marine aerosol (40.4% in PM₁₀ and 21.5% in PM_2.5) and secondary PM (22.9% in PM₁₀ and 42.1% in PM_2.5) were found to be the major source contributors at the study site followed by the motor vehicles (16% in PM₁₀ and 6% in PM_2.5), biomass burning (0.7% in PM₁₀ and 14% in PM_2.5), tire and brake wear (4.1% in PM₁₀ and 5.4% in PM_2.5), soil (3.4% in PM₁₀ and 4.3% in PM_2.5) and other sources (12.7% in PM₁₀ and 6.8% in PM_2.5).     

Characteristics and origins of air pollutants in Wuhan,China, based on observations and hybrid receptor models

To identify the characteristics of air pollutants and factors attributing to the formation of haze in Wuhan, this study analyzed the hourly observations of air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO) from March 1, 2013, to February 28, 2014, and used hybrid receptor models for a case study. The results showed that the annual average concentrations for PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO during the whole period were 89.6 μg m^?3, 134.9 μg m^?3, 54.9 μg m^?3, 32.4 μg m^?3, 62.3 μg m^?3, and 1.1 mg m^?3, respectively. The monthly variations revealed that the peak values of PM_2.5, PM₁₀, NO₂, SO₂, and CO occurred in December because of increased local emissions and severe weather conditions, while the lowest values occurred in July mainly due to larger precipitation. The maximum O₃ concentrations occurred in warm seasons from May to August, which may be partly due to the high temperature and solar radiation. Diurnal analysis showed that hourly PM_2.5, PM₁₀, NO₂, and CO concentrations had two ascending stages accompanying by the two traffic peaks. However, the O₃ concentration variations were different with the highest concentration in the afternoon. A case study utilizing hybrid receptor models showed the significant impact of regional transport on the haze formation in Wuhan and revealed that the mainly potential polluted sources were located in the north and south of Wuhan, such as Baoding and Handan in Hebei province, and Changsha in Hunan province. Implications: Wuhan city requires a 5% reduction of the annual mean of PM_2.5 concentration by the end of 2017. In order to accomplish this goal, Wuhan has adopted some measures to improve its air quality. This work has determined the main pollution sources that affect the formation of haze in Wuhan by transport. We showed that apart from the local emissions, north and south of Wuhan were the potential sources contributing to the high PM_2.5 concentrations in Wuhan, such as Baoding and Handan in Hebei province, Zhumadian and Jiaozuo in Henan province, and Changsha and Zhuzhou in Hunan province.     

Multiple-year black carbon measurements and source apportionment using Delta-C in Rochester,New York

Black carbon (BC), an important component of the atmospheric aerosol, has climatic, environmental, and human health significance. In this study, BC was continuously measured using a two-wavelength aethalometer (370 nm and 880 nm) in Rochester, New York, from January 2007 to December 2010. The monitoring site is adjacent to two major urban highways (I-490 and I-590), where 14% to 21% of the total traffic was heavy-duty diesel vehicles. The annual average BC concentrations were 0.76 μg/m³, 0.67 μg/m³, 0.60 μg/m³, and 0.52 μg/m³ in 2007, 2008, 2009, and 2010, respectively. Positive matrix factorization (PMF) modeling was performed using PM_2.5 elements, sulfate, nitrate, ammonia, elemental carbon (EC), and organic carbon (OC) data from the U.S. Environmental Protection Agency (EPA) speciation network and Delta-C (UVBC_370nm – BC_880nm) data. Delta-C has been previously shown to be a tracer of wood combustion factor. It was used as an input variable in source apportionment models for the first time in this study and was found to play an important role in separating traffic (especially diesel) emissions from wood combustion emissions. The result showed the annual average PM_2.5 concentrations apportioned to diesel emissions in 2007, 2008, 2009, and 2010 were 1.34 μg/m³, 1.25 μg/m³, 1.13 μg/m³, and 0.97 μg/m³, respectively. The BC conditional probability function (CPF) plots show a large contribution from the highway diesel traffic to elevated BC concentrations. The measurements and modeling results suggest an impact of the U.S Environmental Protection Agency (EPA) 2007 Heavy-Duty Highway Rule on the decrease of BC and PM_2.5 concentrations during the study period.

Implications: This study suggests that there was an observable impact of the U.S EPA 2007 Heavy-Duty Highway Rule on the ambient black carbon concentrations in Rochester, New York. Aethalometer Delta-C was used as an input variable in source apportionment models and it allowed the separation of traffic (especially diesel) emissions from wood combustion emissions.     

Chemical characteristics and source apportionment of PM<Subscript>2.5</Subscript> using PCA/APCS,UNMIX, and PMF at an urban site of Delhi,India

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM_2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM_2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM_2.5 was recorded as 121.9 ± 93.2 μg m^?3 (range 25.1–429.8 μg m^?3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ～17% of PM_2.5. Strong seasonal variation was observed in PM_2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM_2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM_2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM_2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM_2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM_2.5 from the north-west India and Pakistan were mostly pertinent.     

PM10 concentration levels at an urban and background site in Cyprus: The impact of urban sources and dust storms

Air quality in Cyprus is influenced by both local and transported pollution, including desert dust storms. We examined PM₁₀ concentration data collected in Nicosia (urban representative) from April 1, 1993, through December 11, 2008, and in Ayia Marina (rural background representative) from January 1, 1999, through December 31, 2008. Measurements were conducted using a Tapered Element Oscillating Micro-balance (TEOM). PM₁₀ concentrations, meteorological records, and satellite data were used to identify dust storm days. We investigated long-term trends using a Generalized Additive Model (GAM) after controlling for day of week, month, temperature, wind speed, and relative humidity. In Nicosia, annual PM₁₀ concentrations ranged from 50.4 to 63.8 μg/m³ and exceeded the EU annual standard limit enacted in 2005 of 40 μg/m³ every year. A large, statistically significant impact of urban sources (defined as the difference between urban and background levels) was seen in Nicosia over the period 2000–2008, and was highest during traffic hours, weekdays, cold months, and low wind conditions. Our estimate of the mean (standard error) contribution of urban sources to the daily ambient PM₁₀ was 24.0 (0.4) μg/m³. The study of yearly trends showed that PM₁₀ levels in Nicosia decreased from 59.4 μg/m³ in 1993 to 49.0 μg/m³ in 2008, probably in part as a result of traffic emission control policies in Cyprus. In Ayia Marina, annual concentrations ranged from 27.3 to 35.6 μg/m³, and no obvious time trends were observed. The levels measured at the Cyprus background site are comparable to background concentrations reported in other Eastern Mediterranean countries. Average daily PM₁₀ concentrations during desert dust storms were around 100 μg/m³ since 2000 and much higher in earlier years. Despite the large impact of dust storms and their increasing frequency over time, dust storms were responsible for a small fraction of the exceedances of the daily PM₁₀ limit.

ImplicationsThis paper examines PM₁₀ concentrations in Nicosia, Cyprus, from 1993 to 2008. The decrease in PM₁₀ levels in Nicosia suggests that the implementation of traffic emission control policies in Cyprus has been effective. However, particle levels still exceeded the European Union annual standard, and dust storms were responsible for a small fraction of the daily PM₁₀ limit exceedances. Other natural particles that are not assessed in this study, such as resuspended soil and sea salt, may be responsible in part for the high particle levels.     

Source analysis of high particulate matter days in Hong Kong

This study identifies major contributing sources of high particulate matter (PM) days in Hong Kong and conducive meteorological conditions leading to high PM. The PM₁₀ chemical composition of 3393 ambient samples collected at ten monitoring stations in Hong Kong during 1998–2005 were used as input for positive matrix factorization (PMF) modeling to identify and quantify the aerosol sources in Hong Kong. Days with PM₁₀ levels exceeding 56 μg m^?3, the average plus one standard deviation of the mass concentration of all samples, are defined as high PM days. A total of 401 samples fell in the high PM category during the study period. Biomass burning, secondary sulfate and secondary nitrate were found to be the major contributors leading to high PM, responsible for 68–73% of PM₁₀ mass on high PM days. The contributions by these sources on high PM days were 140–180% higher than their respective average concentration contributions. These sources were identified to be regional sources on the grounds of little spatial variation in their concentrations among the monitoring stations and a temporal pattern of higher in the winter and lower in the summer. Sampling days of high PM in 2004 and 2005 were individually examined for weather charts and regional surface wind maps. Weak high pressures over mainland China were the most important synoptic event leading to high PM days in the fall and winter, while typhoon episodes were responsible for most summer cases. Approximately 80% of the high PM days were in the fall and winter months (September–February). Almost all the high PM days were associated with northwesterly, northerly or northeasterly regional transport. Anthropogenic primary sources (coal combustion, vehicular exhaust, and residue oil combustion) showed the highest contributions associated with northwesterly wind, indicating the strong influence of the more urbanized areas to the northwest of Hong Kong in the Pearl River Delta region.     

Attainment Flip-Flops and the Achievability of the Ozone Air Quality Standard

Abstract

This paper presents the results of the first reported study on fine particulate matter (PM) chemical composition at Salamanca, a highly industrialized urban area of Central Mexico. Samples were collected at six sites within the urban area during February and March 2003. Several trace elements, organic carbon (OC), elemental carbon (EC), and six ions were analyzed to characterize aerosols. Average concentrations of PM with aerodynamic diameter of less than 10 μm (PM₁₀) and fine PM with aerodynamic diameter of less than 2.5 μm (PM_2.5) ranged from 32.2 to 76.6 μg m^-3 and 11.1 to 23.7 μg m^-3, respectively. OC (34%), SO₄ ⁼ (25.1%), EC (12.9%), and geological material (12.5%) were the major components of PM_2.5. For PM₁₀, geological material (57.9%), OC (17.3%), and SO₄ ⁼ (9.7%) were the major components. Coarse fraction (PM₁₀ –PM_2.5), geological material (81.7%), and OC (8.6%) were the dominant species, which amounted to 90.4%. Correlation analysis showed that sulfate in PM_2.5 was present as ammonium sulfate. Sulfate showed a significant spatial variation with higher concentrations to the north resulting from predominantly southwesterly winds above the surface layer and by major SO₂ sources that include a power plant and refinery. At the urban site of Cruz Roja it was observed that PM_2.5 mass concentrations were similar to the submicron fraction concentrations. Furthermore, the correlation between EC in PM_2.5 and EC measured from an aethalometer was r² = 0.710. Temporal variations of SO₂ and nitrogen oxide were observed during a day when the maximum concentration of PM_2.5 was measured, which was associated with emissions from the nearby refinery and power plant. From cascade impactor measurements, the three measured modes of airborne particles corresponded with diameters of 0.32, 1.8, and 5.6 μm.     

An evaluation of the PM2.5 trace elemental composition in the Venice Lagoon area and an analysis of the possible sources

Global emissions reported by many authors have shown as natural and anthropic sources can contribute to the principal aerosol classes, but values change according the local scenario. The Venice Lagoon is exposed to different anthropic source emissions like vehicular traffic, industrial thermoelectric power plant, petrochemical plant, incinerator plant, domestic heating, ship traffic, glass factories and airport. Samplings of PM_2.5 were daily performed between March and November 2007 in Sacca San Biagio island (Venice), and values of PM_2.5 concentration and element concentration were obtained. Monthly average concentrations (μg m^?3) during this period show higher values during the spring and the autumn. A good relationship between data obtained and concentration values from environmental local agencies is evidenced, both for PM_2.5 from urban area (Venezia Mestre), and for PM₁₀ sampled in the same area, as well as the influence of some meteorological parameters on PM_2.5 concentration sampled. Trace elements samples were measured by an Inductively Coupled Plasma-Quadrupole Mass Spectrometry (ICP-QMS), and values (ng m^?3 and μg g^?1) for elements regulated by European directives (As, Cd, Ni, Pb), as well as, other elements (Na, Al, K, Ti, V, Mn, Fe, Co, Zn, Se, Ag) are also reported. Data analysis by mean of Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) pointed out four principal groups of elements like Mn–Fe–K, As–Se–Cd, V–Co, and Pb that could be assigned to specific sources of the Venetian wetland basin.     

Temporal variations of fine and coarse particulate matter sources in Jeddah,Saudi Arabia

This study provides the first comprehensive analysis of the seasonal variations and weekday/weekend differences in fine (aerodynamic diameter <2.5 μm; PM_2.5) and coarse (aerodynamic diameter 2.5–10 μm; PM_2.5–10) particulate matter mass concentrations, elemental constituents, and potential source origins in Jeddah, Saudi Arabia. Air quality samples were collected over 1 yr, from June 2011 to May 2012 at a frequency of three times per week, and analyzed. The average mass concentrations of PM_2.5 (21.9 μg/m³) and PM₁₀ (107.8 μg/m³) during the sampling period exceeded the recommended annual average levels by the World Health Organization (WHO) for PM_2.5 (10 μg/m³) and PM₁₀ (20 μg/m³), respectively. Similar to other Middle Eastern locales, PM_2.5–10 is the prevailing mass component of atmospheric particulate matter at Jeddah, accounting for approximately 80% of the PM₁₀ mass. Considerations of enrichment factors, absolute principal component analysis (APCA), concentration roses, and backward trajectories identified the following source categories for both PM_2.5 and PM_2.5–10: (1) soil/road dust, (2) incineration, and (3) traffic; and for PM_2.5 only, (4) residual oil burning. Soil/road dust accounted for a major portion of both the PM_2.5 (27%) and PM_2.5–10 (77%) mass, and the largest source contributor for PM_2.5 was from residual oil burning (63%). Temporal variations of PM_2.5–10 and PM_2.5 were observed, with the elevated concentration levels observed for mass during the spring (due to increased dust storm frequency) and on weekdays (due to increased traffic). The predominant role of windblown soil and road dust in both the PM_2.5 and PM_2.5–10 masses in this city may have implications regarding the toxicity of these particles versus those in the Western world where most PM health assessments have been made in the past. These results support the need for region-specific epidemiological investigations to be conducted and considered in future PM standard setting.

Implications: Temporal variations of fine and coarse PM mass, elemental constituents, and sources were examined in Jeddah, Saudi Arabia, for the first time. The main source of PM_2.5–10 is natural windblown soil and road dust, whereas the predominant source of PM_2.5 is residual oil burning, generated from the port and oil refinery located west of the air sampler, suggesting that targeted emission controls could significantly improve the air quality in the city. The compositional differences point to a need for health effect studies to be conducted in this region, so as to directly assess the applicability of the existing guidelines to the Middle East air pollution.     

Particulate matter emissions from on-road vehicles in a freeway tunnel study

Particulate matter, including coarse particles (PM_2.5–10, aerodynamic diameter of particle between 2.5 and 10 μm) and fine particles (PM_2.5, aerodynamic diameter of particle lower than 2.5 μm) and their compositions, including elemental carbon, organic carbon, and 11 water-soluble ionic species, and elements, were measured in a tunnel study. A comparison of the six-hour average of light-duty vehicle (LDV) flow of the two sampling periods showed that the peak hours over the weekend were higher than those on weekdays. However, the flow of heavy-duty vehicles (HDVs) on the weekdays was significant higher than that during the weekend in this study. EC and OC content were 49% for PM_2.5–10 and 47% for PM_2.5 in the tunnel center. EC content was higher than OC content in PM_2.5–10, but EC was about 2.3 times OC for PM_2.5. Sulfate, nitrate, ammonium were the main species for PM_2.5–10 and PM_2.5. The element contents of Na, Al, Ca, Fe and K were over 0.8 μg m^?3 in PM_2.5–10 and PM_2.5. In addition, the concentrations of S, Ba, Pb, and Zn were higher than 0.1 μg m^?3 for PM_2.5–10 and PM_2.5. The emission factors of PM_2.5–10 and PM_2.5 were 18 ± 6.5 and 39 ± 11 mg km^?1-vehicle, respectively. The emission factors of EC/OC were 3.6/2.7 mg km^?1-vehicle for PM_2.5–10 and 15/4.7 mg km^?1-vehicle for PM_2.5 Furthermore, the emission factors of water-soluble ions were 0.028(Mg²⁺)–0.81(SO₄^2?) and 0.027(NO₂^?)–0.97(SO₄^2?) mg km^?1-vehicle for PM_2.5–10 and PM_2.5, respectively. Elemental emission factors were 0.003(V)–1.6(Fe) and 0.001(Cd)–1.05(Na) mg km^?1-vehicle for PM_2.5–10 and PM_2.5, respectively.     

Physicochemical factors and sources of particulate matter at residential urban environment in Kuala Lumpur

Long-term measurements (2004–2011) of PM₁₀ (particulate matter with an aerodynamic diameter <10 μm) and trace gases (carbon monoxide [CO], ozone [O₃], nitrogen oxide [NO], oxides of nitrogen [NO_x], nitrogen dioxide [NO₂], sulfur dioxide [SO₂], methane [CH₄], nonmethane hydrocarbon [NMHC]) have been conducted to study the effect of physicochemical factors on the PM₁₀ concentration. In addition, this study includes source apportionment of PM₁₀ in Kuala Lumpur urban environment. An advanced principal component analysis (PCA) technique coupled with absolute principal component scores (APCS) and multiple linear regression (MLR) has been applied. The average annual concentration of PM₁₀ for 8 yr is 51.3 ± 25.8 μg m^?3, which exceeds the Recommended Malaysian Air Quality Guideline (RMAQG) and international guideline values. Detail analysis shows the dependency of PM₁₀ on the linear changes of the motor vehicles in use and the amount of biomass burning, particularly from Sumatra, Indonesia, during southwesterly monsoon. The main sources of PM₁₀ identified by PCA-APCS-MLR are traffic combustion (28%), ozone coupled with meteorological factors (20%), and windblown particles (1%). However, the apportionment procedure left 28.0 μg m^?3, that is, 51% of PM₁₀ undetermined.

Implications: Air quality is always a top concern around the globe. Especially in the South Asian regions, measures are not yet sufficient; as revealed in our studies, the concentrations of particulate matters exceed the tolerable limits. Long-term data analysis and characterization of particular matters and their sources will aid the policy makers and the concerned authority to adapt measures and policies according to the circumstances. Additionally, similar intensive studies will give insight about future implications of air quality management.