Rates of particulate pollution deposition onto leaf surfaces: Temporal and inter-species magnetic analyses

Evaluation of health impacts arising from inhalation of pollutant particles <10 μm (PM₁₀) is an active research area. However, lack of exposure data at high spatial resolution impedes identification of causal associations between exposure and illness. Biomagnetic monitoring of PM₁₀ deposited on tree leaves may provide a means of obtaining exposure data at high spatial resolution. To calculate ambient PM₁₀ concentrations from leaf magnetic values, the relationship between the magnetic signal and total PM₁₀ mass must be quantified, and the exposure time (via magnetic deposition velocity (MV_d) calculations) known. Birches display higher MV_d (∼5 cm⁻¹) than lime trees (∼2 cm⁻¹). Leaf saturation remanence values reached ‘equilibrium’ with ambient PM₁₀ concentrations after ∼6 ‘dry’ days (<3 mm/day rainfall). Other co-located species displayed within-species consistency in MV_d; robust inter-calibration can thus be achieved, enabling magnetic PM₁₀ biomonitoring at unprecedented spatial resolution.     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

Characterization of Particulate Matter in California

ABSTRACT

The size, composition, and concentration of particulate matter (PM) vary with location and time. Several monitoring/sampling programs are operated in California to characterize PM less than 2.5 and 10 µm in aerodynamic diameter (PM_2.5 and PM₁₀). This paper presents a broad summary of the spatial and temporal variations observed in ambient PM_2.5 and PM₁₀ concentrations in California. Many areas that have high PM₁₀ concentrations also have relatively high PM_2.5 concentrations, and data indicate that a significant portion of the PM₁₀ air quality problem is caused by PM_2.5. To develop effective plans for attaining the ambient PM standards, improved understanding of these unique problems is needed. Since 1989, pollution control efforts—whether specifically targeted for particulate matter or indirectly via controls on gaseous emissions—have caused annual average PM_2.5 and PM₁₀ concentrations to decline at most sites in California.     

Observations and Interpretations from the Canadian Fine Particle Monitoring Program

ABSTRACT

Canadian particle monitoring programs examining PM10, PM2.5, and particle composition have been in operation for over 10 years. Until recently, the measurements were manual/filter-based with 24-hr sample collection varying in frequency from daily to every sixth day, using GrasebyAnderson dichotomous samplers. In the past few years, these monitoring activities have been expanded to include hourly measurements using tapered element oscillating microbalances (TEOMs). This continuous monitoring program started operation focusing on PM_10, but now emphasizes PM_2.5 through the addition of more TEOMs and switching of the inlets of some of the existing units. The data from all of these measurement activities show that there are broad geographical differences and also local- to regional-scale spatial differences in mass and composition of PM2.5. Due to variations in sources, significantly different PM2.5 concentrations are not uncommon within the same city. Comparison of nearby urban and rural sites indicates that 30 and 40% of the PM_2.5 is from local urban sources in Montreal and Toronto, respectively. Hourly PM_2.5 measurements in Toronto suggest that vehicular emissions are an important contributor to urban PM_2.5. There has been a decreasing trend in urban PM_2.5, with annual average concentrations between the 1987–1990 and 1993–1995 periods decreasing by 11 to 39%, depending upon the site. The largest declines were in Montreal and Halifax, and the smallest decline was in Toronto. Comparison of 24-hr TEOM and manual dichotomous sampler PM_2.5 measurements from a site in Toronto indicates that the TEOM results in lower concentrations. The magnitude of this difference is relatively small in the warmer months, averaging about 12%. During the colder months the difference averages about 23%, but can be as large as 50%.     

Spatial Variability of Different Fractions of Particulate Matter within an Urban Environment and between Urban and Rural Sites

ABSTRACT

The spatial variability of different fractions of particulate matter (PM) was investigated in the city of Basel, Switzerland, based on measurements performed throughout 1997 with a mobile monitoring station at six sites and permanently recorded measurements from a fixed site. Additionally, PM₁₀ measurements from the following year, which were concurrently recorded at two urban and two rural sites, were compared.

Generally, the spatial variability of PM₄, PM₁₀, and total suspended particulates (TSP) within this Swiss urban environment (area = 36 km²) was rather limited. With the exception of one site in a street canyon next to a traffic light, traffic density had only a weak tendency to increase the levels of PM. Mean PM₁₀ concentration at six sites with different traffic densities was in the range of less than ±10% of the mean urban PM₁₀ level. However, comparing the mean PM levels on workdays to that on weekends indicated that the impact of human activities, including traffic, on ambient PM levels may be considerable.

Differences in the daily PM₁₀ concentrations between urban and more elevated rural sites were strongly influenced by the stability of the atmosphere. In summer, when no persistent surface inversions exist, differences between urban and rural sites were rather small. It can therefore be concluded that spatial variability of annual mean PM concentration between urban and rural sites in the Basel area may more likely be caused by varying altitude than by distance to the city center.     

区域大气环境中PM2.5/PM10空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM_2.5/PM₁₀空间分布的方法，并在2004年12月进行了宁波市全市域PM_2.5/PM₁₀空间分布的研究。数据显示：相同路径所代表的地区PM_2.5和PM₁₀具有很好的相关性，多数路径上PM_2.5与PM₁₀数据的相关系数平方在0.95以上，而不同路径上PM_2.5与PM₁₀的比值不同。文中给出了宁波市PM_2.5/PM₁₀污染的空间分布图，直观地显示出PM_2.5/PM₁₀污染的空间分布情况，突出了污染的重点点位和地区。     

Evaluation and application of biomagnetic monitoring of traffic-derived particulate pollution

Inhalation of particulate pollutants below 10 μm in size (PM₁₀) is associated with adverse health effects. Here we use magnetic remanence measurements of roadside tree leaves to examine levels of vehicle-derived PM around Lancaster, UK. Leaf saturation remanence (SIRM) values exhibit strong correlation with both the SIRM and particulate mass of co-located, pumped-air samples, indicating that these leaf magnetic values are an effective proxy for ambient PM₁₀ concentrations. Biomagnetic monitoring using tree leaves can thus provide high spatial resolution data sets for assessment of particulate pollution levels at pedestrian-relevant heights. Leaf SIRM values not only increase with proximity to roads with higher traffic volumes, but are also ～100% higher at 0.3 m than at ～1.5–2 m height. Magnetic and SEM data indicate that the particle populations are dominated by spherical, iron-rich particles ～0.1–1 μm in diameter, with fewer larger, more angular, silica-rich particles. Comparison of the roadside leaf-calculated PM₁₀ concentrations with PM₁₀ concentrations predicted by a widely-used atmospheric dispersion model indicates some agreement between them. However, the model under-predicts PM₁₀ concentrations at ‘urban hotspots’ such as major–minor road junctions and traffic lights. Conversely, the model over-predicts PM₁₀ concentrations with distance from the road wherever one tree is screened by another, indicating the filtering/protective effect of roadside trees in leaf.     

Spatial variation of particle number and mass over four European cities

The number of ultrafine particles may be a more health relevant characteristic of ambient particulate matter than the conventionally measured mass. Epidemiological time series studies typically use a central site to characterize human exposure to outdoor air pollution. There is currently very limited information how well measurements at a central site reflect temporal and spatial variation across an urban area for particle number concentrations (PNC).The main objective of the study was to assess the spatial variation of PNC compared to the mass concentration of particles with diameter less than 10 or 2.5 μm (PM₁₀ and PM_2.5).Continuous measurements of PM₁₀, PM_2.5, PNC and soot concentrations were conducted at a central site during October 2002–March 2004 in four cities spread over Europe (Amsterdam, Athens, Birmingham and Helsinki). The same measurements were conducted directly outside 152 homes spread over the metropolitan areas. Each home was monitored during 1 week. We assessed the temporal correlation and the variability of absolute concentrations.For all particle indices, including particle number, temporal correlation of 24-h average concentrations was high. The median correlation for PNC per city ranged between 0.67 and 0.76. For PM_2.5 median correlation ranged between 0.79 and 0.98. The median correlation for hourly average PNC was lower (range 0.56–0.66). Absolute concentration levels varied substantially more within cities for PNC and coarse particles than for PM_2.5. Measurements at the central site reflected the temporal variation of 24-h average concentrations for all particle indices at the selected homes across the urban area. A central site could not assess absolute concentrations across the urban areas for particle number.     

Source Apportionment of Ambient Total Suspended Particulates and Coarse Particulate Matter in Urban Areas of Jiaozuo,China

Abstract

Approximately 750 total suspended particulates (TSPs) and coarse particulate matter (PM₁₀) filter samples from six urban sites and a background site and >210 source samples were collected in Jiaozuo City during January 2002 to April 2003. They were analyzed for mass and abundances of 25 chemical components. Seven contributive sources were identified, and their contributions to ambient TSP/PM₁₀ levels at the seven sites in three seasons (spring, summer, and winter days) and a “whole” year were estimated by a chemical mass balance (CMB) receptor model. The spatial TSP average was high in spring and winter days at a level of approximately 530 ～g/m³ and low in summer days at 456 ～g/m³; however, the spatial PM₁₀ average exhibited little variation at a level of approximately 325 ～g/m³, and PM_10-to-TSP ratios ranged from 0.58 to 0.81, which suggested heavy particulate matter pollution existing in the urban areas. Apportionment results indicated that geological material was the largest contributor to ambient TSP/PM₁₀ concentrations, followed by dust emissions from construction activities, coal combustion, secondary aerosols, vehicle movement, and other industrial sources. In addition, paved road dust and re-entrained dust were also apportioned to the seven source types and found soil, coal combustion, and construction dust to be the major contributors.     

Evaluating the dynamical characteristics of particle matter emissions in an open ore yard with industrial operation activities

A study to investigate the dynamical characteristics of particle matter emissions in a working open yard is conducted in Caofeidian Port of Hebei Province, China. The average diurnal concentrations of the total suspended particulate (TSP) matter and respirable particulate matter (PM₁₀ and PM₅) are monitored during the field measurement campaign. Sampling is performed at a regular interval at 8 monitoring stations in the yard with normal industrial activities. The average TSP, PM₁₀ and PM₅ concentrations range from 285 to 568, 198 to 423 and 189 to 330 μg.m-3 in the yard, respectively. The linear regression correlation coefficient of TSP/PM₁₀ and TSP/PM₅ is 0.95±0.01 and 0.88±0.02, respectively.By using the Spearman correlation method, the wind speed and relative humidity are both weakly correlated with the PM₁₀ and PM₅ concentrations according to the measurements. In addition, industrial operation activities, such as vehicular traffic in the yard and the loading time of stackers, are significantly positively correlated with the PM concentration. Using the multivariate regression method, the main parameters influencing the TSP concentration variations are integratedly analysed. The traffic volume is found to be a significant predictor of TSP concentration variation, with the smallest P value (P<0.05).To understand the dynamical characteristics of particle emissions in the yard, the emissions from the truck transports, that is, from unpaved haul roads and from the loading process, are established. Then, the dynamical emission factor (EF_D) based on the industrial activities in the yard is proposed. The dynamical emissions average 5.25x10⁵ kg.year^-1 and EF_D is evaluated to be 0.29 kg.(ton.day)^-1 during the measurement period. These outcomes have meaningful implications not only for understanding the dynamical characteristics of particle emissions in the working stockyard but also for implementing effective control measures at appropriate sites in the harbour area.     

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.     

Polychlorinated dibenzo-p-dioxin and dibenzofuran in urban air of an Andean city

Particle-bound polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) in ambient air were monitored together with particulate matter less than 10 μm (PM₁₀) at three sampling sites of the Andean city of Manizales, Colombia; during September 2009 and July 2010. PCDD/Fs ambient air emissions ranged from 1 fg WHO-TEQ m⁻³ to 52 fg WHO-TEQ m⁻³ in particulate fraction. The PM₁₀ concentrations ranged from 23 μg m⁻³ to 54 μg m⁻³. Concentrations of PM₁₀ and PCDD/Fs in ambient air observed for Manizales - a medium sized city with a population of 380 000 - were comparable to concentrations in larger cities. The highest concentrations of PCDD/Fs and PM₁₀ found in this study were determined at the central zone of the city, characterized by public transportation density, where diesel as principal fuel is used. In addition, hypothetical gas fractions of PCDD/Fs were calculated from theoretical K_p data. Congener profiles of PCDD/Fs exhibited ratios associated with different combustion sources at the different sampling locations, ranging from steel recycling to gasoline and diesel engines. Taking into account particle and gas hypothetical fraction of PCDD/Fs, Manizales exhibited values of PCDD/Fs equivalent to rural and urban-industrial sites in the southeast and center of the city respectively. Poor correlation of PCDDs with PM₁₀ (r = −0.55 and r = 0.52) suggests ambient air PCDDs were derived from various combustion sources. Stronger correlation was observed of PCDFs with PM₁₀. Poor correlation between precipitation and reduced PM₁₀ concentration in ambient air (r = −0.45) suggested low PM₁₀ removal by rainfall.     

Sources and processes affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (U.K.)

Hourly average concentrations of PM₁₀ and PM_2.5 have been measured simultaneously at a site within Birmingham U.K. between October 1994 and October 1995. Comparison of PM₁₀ and NO_x data with two other sites in the same city shows comparable summer and winter mean concentrations and highly significant inter-site correlations for both hourly and daily mean data. Over a four-month period samples were also collected for chemical analysis of sulphate, nitrate, chloride, ammonium and elemental and organic carbon. Analysis of the data indicates a marked difference between summer and winter periods. In the winter months PM_2.5 comprises about 80% of PM₁₀ and is strongly correlated with NO_x indicating the importance of road traffic as a source. In the summer months, coarse particles (PM₁₀−PM_2.5) account for almost 50% of PM₁₀ and the influence of resuspended surface dusts and soils and of secondary particulate matter is evident. The chemical analysis data are also consistent with three sources dominating the PM₁₀ composition: vehicle exhaust emissions, secondary ammonium salts and resuspended surface dusts. Coarse particles from resuspension showed a positive dependence on windspeed, whilst elemental carbon derived from road traffic exhibited a negative dependence.     

Characterization of summertime coarse particulate matter in the Desert Southwest—Arizona,USA

A year-long study was conducted in Pinal County, AZ, to characterize coarse (2.5 – 10 μm aerodynamic diameter, AD) and fine (< 2.5 μm AD) particulate matter (PMc and PMf, respectively) to further understand spatial and temporal variations in ambient PM concentrations and composition in rural, arid environments. Measurements of PMc and PMf mass, ions, elements, and carbon concentrations at one-in-six day resolution were obtained at three sites within the region. Results from the summer of 2009 and specifically the local monsoon period are presented.

The summer monsoon season (July – September) and associated rain and/or high wind events, has historically had the largest number of PM₁₀ NAAQS exceedances within a year. Rain events served to clean the atmosphere, decreasing PMc concentrations resulting in a more uniform spatial gradient among the sites. The monsoon period also is characterized by high wind events, increasing PMc mass concentrations, possibly due to increased local wind-driven soil erosion or transport. Two PM₁₀ NAAQS exceedances at the urban monitoring site were explained by high wind events and can likely be excluded from PM₁₀ compliance calculations as exceptional events. At the more rural Cowtown site, PM₁₀ NAAQS exceedances were more frequent, likely due to the impact from local dust sources.

PM mass concentrations at the Cowtown site were typically higher than at the Pinal County Housing and Casa Grande sites. Crustal material was equal to 52-63% of the PMc mass concentration on average. High concentrations of phosphate and organic carbon found at the rural Cowtown were associated with local cattle feeding operations. A relatively high correlation between PMc and PMf (R²?=?0.63) indicated that the lower tail of the coarse particle fraction often impacts the fine particle fraction, increasing the PMf concentrations. Therefore, reductions in PMc sources will likely also reduce PMf concentrations, which also are near the value of the 24-hr PM_2.5 NAAQS.

Implications: In the desert southwest, summer monsoons are often associated with above average PM₁₀ (<10 μm AD) mass concentrations. Competing influences of monsoon rain and wind events showed that rain suppresses ambient concentrations while high wind increase them. In this region, the PMc fraction dominates PM₁₀ and crustal sources contribute 52-63% to local PMc mass concentrations on average. Cattle feedlot emissions are also an important source and a unique chemical signature was identified for this source. Observations suggest monsoon wind events alone cannot explain PM₁₀ NAAQS exceedances, thus requiring these values to remain in compliance calculations rather than being removed as exceptional wind events.     

Spatial and temporal variability of outdoor coarse particulate matter mass concentrations measured with a new coarse particle sampler during the Detroit Exposure and Aerosol Research Study

The Detroit Exposure and Aerosol Research Study (DEARS) provided data to compare outdoor residential coarse particulate matter (PM_10–2.5) concentrations in six different areas of Detroit with data from a central monitoring site. Daily and seasonal influences on the spatial distribution of PM_10–2.5 during Summer 2006 and Winter 2007 were investigated using data collected with the newly developed coarse particle exposure monitor (CPEM). These data allowed the representativeness of the community monitoring site to be assessed for the greater Detroit metro area. Multiple CPEMs collocated with a dichotomous sampler determined the precision and accuracy of the CPEM PM_10–2.5 and PM_2.5 data.CPEM PM_2.5 concentrations agreed well with the dichotomous sampler data. The slope was 0.97 and the R² was 0.91. CPEM concentrations had an average 23% negative bias and R² of 0.81. The directional nature of the CPEM sampling efficiency due to bluff body effects probably caused the negative CPEM concentration bias.PM_10–2.5 was observed to vary spatially and temporally across Detroit, reflecting the seasonal impact of local sources. Summer PM_10–2.5 was 5 μg m^?3 higher in the two industrial areas near downtown than the average concentrations in other areas of Detroit. An area impacted by vehicular traffic had concentrations 8 μg m^?3 higher than the average concentrations in other parts of Detroit in the winter due to the suspected suspension of road salt. PM_10–2.5 Pearson Correlation Coefficients between monitoring locations varied from 0.03 to 0.76. All summer PM_10–2.5 correlations were greater than 0.28 and statistically significant (p-value < 0.05). Winter PM_10–2.5 correlations greater than 0.33 were statistically significant (p-value < 0.05). The PM_10–2.5 correlations found to be insignificant were associated with the area impacted by mobile sources during the winter. The suspected suspension of road salt from the Southfield Freeway, combined with a very stable atmosphere, caused concentrations to be greater in this area compared to other areas of Detroit. These findings indicated that PM_10–2.5, although correlated in some instances, varies sufficiently across a complex urban airshed that that a central monitoring site may not adequately represent the population's exposure to PM_10–2.5.     

Bayesian hierarchical modeling of personal exposure to particulate matter

In the US EPA's 1998 Baltimore Epidemiology-Exposure Panel Study, a group of 16 residents of a single building retirement community wore personal monitors recording personal fine particulate air pollution concentrations (PM_2.5) for 27 days, while other monitors recorded concurrent apartment, central indoor, outdoor and ambient site PM_2.5 concentrations. Using the Baltimore panel study data, we develop a Bayesian hierarchical model to characterize the relationship between personal exposure and concentrations of PM_2.5 indoors and outdoors. Personal exposure is expressed as a linear combination of time spent in microenvironments and associated microenvironmental concentrations. The model incorporates all available monitoring data and accounts for missing data and sources of uncertainty such as measurement error and individual differences in exposure. We discuss the implications of using personal versus ambient PM_2.5 measurements in characterization of personal exposure to PM_2.5.     

Hierarchical space-time modelling of PM10 pollution

In this paper, we propose a hierarchical spatio-temporal model for daily mean concentrations of PM₁₀ pollution. The main aims of the proposed model are the identification of the sources of variability characterising the PM₁₀ process and the estimation of pollution levels at unmonitored spatial locations. We adopt a fully Bayesian approach, using Monte Carlo Markov Chain algorithms. We apply the model on PM₁₀ data measured at 11 monitoring sites located in the major towns and cities of Italy's Emilia-Romagna Region. The model is designed for areas with PM₁₀ measurements available; the case of PM₁₀ level estimation from emissions data is not handled. The model has been carefully checked using Bayesian p-values and graphical posterior predictive checks. Results show that the temporal random effect is the most important when explaining PM₁₀ levels.     

Development of a PM2 5 Emissions Inventory for the South Coast Air Basin

ABSTRACT

With the promulgation of a national PM_2.5 ambient air quality standard, it is important that PM_2.5 emissions inventories be developed as a tool for understanding the magnitude of potential PM2.5 violations. Current PM₁₀ inventories include only emissions of primary particulate matter (1 ^ï PM), whereas, based on ambient measurements, both PM₁₀ and PM2.5 emissions inventories will need to include sources of both 1^ï PM and secondary particulate matter (2^ï PM). Furthermore, the U. S. Environmental Protection Agency’s (EPA) current edition of AP-42 includes size distribution data for 1o PM that overestimate the PM_2.5 fraction of fugitive dust sources by at least a factor of 2 based on recent studies.

This paper presents a PM_2.5 emissions inventory developed for the South Coast Air Basin (SCAB) that for the first time includes both 1^ï PM and 2^ï PM. The former is calculated by multiplying PM₁₀ emissions estimates by the PM_2.5/PM₁₀ ratios for different sources. The latter is calculated from estimated emission rates of gas-phase aerosol precursor and gas to aerosol conversion rates consistent with the measured chemical composition of ambient PM_2.5 concentrations observed in the SCAB. The major finding of this PM_2.5 emissions inventory is that the ^2ï aerosol component is more than twice the ^1ï aerosol component, which may result in widely different control strategies being required for fine PM and coarse PM.     

Interpretation of air quality in relation to monitoring station's surroundings

This study explores the appropriateness of the locality of air monitoring stations which are meant to indicate air quality in the area. Daily variations in NO₂ and PM₁₀ concentrations at 14 monitoring stations in Hong Kong are examined. The daily variations in NO₂ at a number of background monitoring stations exhibit patterns similar to variations in traffic volume while variations in PM₁₀ concentration exhibit less discernible pattern. Principal component analysis (PCA) and cluster analysis (CA) are applied to analyse NO₂ and PM₁₀ measurements between January 2001 and December 2005. The results show that NO₂ concentrations at background stations within the urban area are highly influenced by vehicle emissions. The effect vehicle emission has on NO₂ at stations within new towns is smaller. CA results also show that variations in PM₁₀ concentrations are distinguished by the area the station is located in. PCA results show that there are two principal components (PC's) associated with variations in roadside concentration of PM₁₀. The strong influence of roadside emissions towards concentrations of NO₂ and PM₁₀ at a number of urban background stations may be due to their close proximity to busy roadways and the high density of surrounding tall buildings, which creates an enclosure that hinders dispersion of roadside emissions and results in air pollution behaviour that reflects variation in traffic.     

Application of a Combined Measurement and Modeling Method to Quantify Windblown Dust Emissions from the Exposed Playa at Mono Lake,California

ABSTRACT

Particulate matter ≤10 μm (PM₁₀) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM₁₀ monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km² area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM₁₀ concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM₁₀ concentrations to back-calculate the ratio of vertical PM₁₀ flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K _f) were found to change seasonally, ranging from 1.3 × 10^?5 to 5.1 × 10^?5 for sand flux measured at 15 cm above the surface (q ₁₅). Hourly PM₁₀ emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F?=?K _f ×?q ₁₅). The maximum hourly PM10 emission rate from the study area was 76 g/m²·hr (10-m wind speed?=?23.5 m/sec). Maximum daily PM₁₀ emissions were estimated at 450 g/m²·day, and annual emissions at 1095 g/m²·yr. Hourly PM₁₀ emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM₁₀ ranging from 16 to over 60,000 μg/m³ (slope?=?0.89, R ²?=?0.77).

IMPLICATIONS Under a U.S. Environmental Protection Agency (EPA)-approved plan, the method described in this paper has been used since 2000 at Owens Lake, CA, to identify and successfully mitigate dust from over 100 km² of the dry lakebed. It continues to be used to monitor dust control compliance at Owens Lake. Scaled-down versions of the Owens Lake network can be implemented in other areas in a manner similar to the Mono Lake study. Once K-factors are established, low-cost CSC samplers (about $35 U.S.) may be used for periodic monitoring (e.g., daily, weekly, or monthly) to estimate PM₁₀ emissions or to evaluate dust control compliance.