Xinghua Li 1, Bing Ruan1, Philip K. Hopke2,3, Tariq Mehmood1

School of Space and Environment, Beihang University, Beijing 100191, China
Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699, USA
Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA


Received: March 4, 2019
Revised: June 23, 2019
Accepted: August 28, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2019.03.0103  

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Cite this article:

Li, X., Ruan, B., Hopke, P.K. and Mehmood, T. (2019). On the Performance Parameters of PM2.5 and PM1 Size Separators for Ambient Aerosol Monitoring. Aerosol Air Qual. Res. 19: 2173-2184. https://doi.org/10.4209/aaqr.2019.03.0103


Highlights

  • Performance of size separator was investigated based on measured urban PSDs.
  • D50 and σg for the PM1 size separator are proposed to be 1.0 ± 0.02 µm and ≤ 1.2.
  • D50 = 2.5 ± 0.2 µm and σg ≤ 1.3 for the PM2.5 size separator suit for urban condition.
 

ABSTRACT


The performance of particle size separators that are characterized by their cutpoint (d50) and the steepness of their penetration curves (σg) is a critical feature of measurement methods for ambient PM2.5 and PM1 monitoring. The parameters d50 and σg for PM2.5 size separators are regulated in the official measurement methods of many countries and regions. However, those parameters for PM1 size separators have not been previously specified. The interaction of particle size distributions (PSDs) and the particle size separator performance characteristics has significant influence on the PM sampling results. Atmospheric PSDs for each of the four seasons covering a range of particulate pollution concentrations were measured in urban Beijing. The relative differences (RD) between estimated PM2.5/PM1 sampler concentrations and actual concentrations associated with the interaction between performance characteristics of size separator and actually measured urban PSDs, as well as for three idealized ambient PSDs, have been systematically investigated. For the PM2.5 size separator, when tolerance of cutpoint is 2.5 ± 0.2 µm and σg is no greater than 1.3, RD values for most typical urban atmospheric aerosol conditions were within the permissible error range (± 5%). For the PM1 size separator, when tolerance of cutpoint and sharpness is 1.00 ± 0.02 µm and σg no greater than 1.2, respectively, the RD values for typical urban atmospheric aerosol conditions are within the permissible range. Those parameters could also meet the requirements for sampling of the idealized ambient aerosol distribution.


Keywords: PM2.5; PM1; Size separator; Cutpoint; Sharpness; Particle size distribution.


INTRODUCTION


PM2.5 (particles with aerodynamic diameter < 2.5 micrometers [µm]) has been promulgated as an indicator in ambient air quality standards (AAQS) of many countries and regions, such as the United State of America (USA), the European Union (EU), Japan, and China (Cao et al., 2013). The regulatory limits on the ambient mass concentration on annual and daily average bases have been set. To regulate PM2.5, accurate measurement of its concentrations is crucial (Cheng, 2018; Lin et al., 2018). Accordingly, the measurement methods for PM2.5 were specified along with the implementation of AAQS. Factors influencing the accuracy of the measured results include ambient aerosol concentration and composition, particle size distribution (PSD), sample volumetric flow rate, sampling time, sampler inlet geometry, the performance of the sampler’s size-selective separator, sampler internal particle losses, and all of the associated sampling and analysis procedures (Vanderpool et al., 2001).

Among these factors, the performance of the particle size separator is a critical feature of the measurement method. The PM2.5 sampler generally utilizes a separator, such as impactor or a cyclone to remove PM greater than 2.5 µm from the airstream. The performance of the particle size separator is normally described by either a collection efficiency curve or penetration curve. The penetration curve is characterized by its cutpoint (d50) and the steepness of the selection curve (σg). The cutpoint is the particle aerodynamic diameter at which probability of penetration through the size separator is 50%. The steepness of the penetration curve is calculated as the square root of the particle diameter associated with 16% penetration (d16) divided by the particle diameter associated with 84% penetration (d84). The steepness is equivalent to the geometric standard deviation of a cumulative lognormal curve fitted to the penetration data, and both are often interchanged (Kenny and Gussman, 2000; Peters et al., 2001).

The U.S. EPA (2013) requires PM2.5 samplers to have a cutpoint of 2.5 ± 0.2 µm, but no steepness values for the sampler are listed because there is a design standard outlined in 40CFR50, Appendix L (https://www.law.cornell.edu/cfr/text/40/appendix-L_to_part_50). The design standard specifies the construction of the WINS impactor (Peters et al., 2001a, b) or the use of a very sharp cut cyclone (VSCC) (Kenny et al., 2000, 2004). The VSCC has a σg of 1.16 (Kenny et al., 2004) while the WINS impactor σg is 1.18 (Peters et al., 2001a). The European Committee for Standardization (2014) do not specify d50 and steepness values, but adopts a design standard outlined in EN 12341 and specifies the use of an impactor size separator with d50 and σg around 2.5 µm and 1.2, respectively. The Japanese Industrial Standards Committee (JISC, 2008) and Ministry of Environmental Protection of China (MEP, 2013) regulated both σg and d50 values. Both stipulated the same d50 values as U.S. EPA. JISC (2008) required σg = d20/d80 < 1.5, where d20, d80 are the particle diameter associated with 20%, and 80% penetration, respectively. MEP (2013) regulated σg = 1.2 ± 0.1, where σg is defined as d50/d16, and d84/d50, d16, d50 and d84 were the particle diameter associated with 16%, 50%, and 84% collection efficiency, respectively. In general, d50/d16, and d84/d50 in MEP (2013) are comparable in steepness.

Peters et al. (2001a) measured the penetration curves of five PM2.5 size separators widely used in the speciation samplers and integrated the curves with three idealized ambient PSDs to obtain estimates of the measured PM2.5 concentration and predict bias relative to the PM2.5 reference size separator. d50 and σg measured for five separator ranged from 2.35 to 2.72 µm, 1.25 to 1.30, respectively. Estimated PM2.5 concentration biases relative to the reference separator were within 5% for the idealized fine and typical ambient distributions for all separators. Bias ranged between 4% and 8% for the idealized coarse distribution, relatively higher than for the fine and typical distributions. Buser et al. (2007) theoretically estimated the PM2.5 sampling errors associated with the interaction of the PSDs and the sampler performance characteristics. In their study, d50 and σg were selected as 2.5 ± 0.2 µm, σg = 1.3 ± 0.03, respectively, and PSDs were three idealized ambient PSDs and other assumed PSDs. They also found that differences between the actual sampler and the EPA idealized sampler were within 5% for the idealized fine and typical ambient distributions, while differences exceeded 5% when d50 was 2.5 µm and σg was 1.33, or d50 was 2.7 µm and σg was 1.3 ± 0.03 for an idealized coarse distribution. For assumed PSDs with mass median diameter from 5 to 20 µm and geometric standard deviation 1.5 to 2.0, differences ranged from 8% up to 1214%. These studies indicated that the interaction of the PSDs and the sampler performance characteristics had significant influences on the PM2.5 sampling results.

Recently, increasing frequency of PM1 (particle with aerodynamic diameter < 1 µm) measurements have been reported (Bari et al., 2015; Qiao et al., 2015; Wang et al., 2015; Galindo et al., 2016; Kunwar et al., 2016). Since PM1 has not been adopted as a criterion in current AAQS regulations, these measurement methods including the performance metrics of the PM1 size separators have not yet been specified.

The objectives of this paper are: (1) Investigate the relative differences (RD) between estimated PM2.5 sampler concentrations and actual PM2.5 concentrations associated with the interaction between performance characteristics for PM2.5 size separator and actually measured typical urban PSDs in four seasons covering all pollution levels and thereby, further evaluate the existing performance parameters for a PM2.5 size separator under real world conditions of high PM2.5 concentrations; (2) Investigate the relative differences (RD) between estimated PM1 sampler concentration and actual PM1 concentrations associated with the interaction between performance characteristics for a PM1 size separator and actually measured typical urban PSDs, and propose the performance parameters for PM1 size separator; and (3) Evaluate the performance parameters for PM1 size separators for the three idealized ambient PSDs developed by Vanderpool et al. (2001).


METHODS



Method General Description

Previous studies have elucidated the basic form of atmospheric PM size distribution (PSD), and typical PSDs showed one or more lognormal shapes (Whitby et al., 1972; Liu et al., 1974). In this case, the lognormal mass distribution can be expressed as:

 

where dp, MMD and GSD is the particle diameter, mass median diameter, and geometric standard deviation of PSD, respectively (Hinds, 1999).

PM samplers have been widely used to collect information on atmospheric PM. For sampling specific PM sizes such as PM10, PM2.5, and PM1, the corresponding size separators are adopted. For an ideal size separator, all particles greater than the specific size should be collected by the size separator, and all particle less than or equal to the specific size should penetrate it and be captured by the subsequent filter. The penetration curve of an ideal size separator is a step function as follows:

 

However, it is not possible to design a size separator with an ideal cut. In an actual PM sampler, a portion of PM greater than the specific size that should be collected by the size separator will penetrate and be collected by the filter (over-collected) while a portion of PM less than or equal to the specific size that should be captured by the filter will be collected by the size separator (under-collected). If both are equal, the actual cut is equal to ideal cut. However, the two are usually not equal, i.e., the sampling results will deviate from the actual values. Thus, the deviations need to be estimated. Based on the introduction of Buser et al. (2006a, b), the methods to estimate sampler concentration and actual concentration and their relative difference were developed as follows.

The cumulative penetration efficiency of the PM sampler is defined as

 

where P(dp, d50, σg) is the sampler penetration efficiency for particles having diameters less than dp.

For unimodal mass size distribution, according to Eqs. (1) and (3), particle concentrations collected by the sampler (sampler concentration, Cs) can be estimated by:

 

where Cm is the ambient particle concentration.

For actual concentrations, the ideal sampler should collect all PM in the size range less than d50, and exclude all PM with size greater than d50. According to Eqs. (1) and (2), the actual concentration Cr can be estimated by

 

For a bimodal mass size distribution, the sampler concentration (Cs) in each mode can be estimated separately according to Eq. (4) and then summed to give:

 

where Cs1, MMD1, GSD1, and Cm1 are the estimated sampler concentration, the mass median diameter, geometric standard deviation, and ambient particle matter concentration of accumulation-mode, respectively. Cs2, MMD2, GSD2, and Cm2 are the estimated sampler concentration, the mass median diameter, geometric standard deviation, and ambient particle matter concentration of the coarse-mode, respectively.

Accordingly, the actual concentration (Cr) can be estimated by Eq. (7).

 

where Cr1, and Cr2 are the estimated actual concentration of the accumulation mode and the coarse mode, respectively.

As previously discussed, the relative differences (RD) between estimated sampler concentration and actual concentration for unimodal or bimodal mass size distribution can be also calculated as:

 


Sampling of Ambient Size-resolved PM

The mass median diameter (MMD), the geometric standard deviation (GSD), and the mass concentration of the ambient PSD mode are the important input data for method calculation. To obtain those parameters, a Dekati@ low-pressure impactor (DLPI, Dekati Ltd, Finland) was used to determine the size distributions of the actual ambient particulate matter. Operating at a flow rate of 10 L min–1, the DLPI has 50% cut-point aerodynamic diameters of 0.027, 0.054, 0.093, 0.16, 0.26, 0.38, 0.61, 0.94, 1.59, 2.38, 3.97, 6.64, and 9.85 µm for stages 1 to stage 13, respectively. The DLPI was installed on the roof of a building approximately 25 m above the ground level on the campus of Beihang University (BHU, 39°59'N, 116°21'E) adjacent to the ambient PM samplers. The site is surrounded by educational and residential districts without major industrial sources. Major nearby roads are the North Fourth Ring Road about 900 m to the north, North Third Ring Road about 1.2 km to the south, and Xueyuan Road about 350 m to the east. The site is representative of urban Beijing.

Four sampling campaigns were conducted in autumn (September–October 2012), winter (January–February 2013), spring (March 2013), summer (July–August 2018). Sampling was conducted for 23 h per sample when the air was polluted, starting from 13:00 on that day to 12:00 on the next day. Sampling was extended to 47 h when the air quality was better, starting from 13:00 on that day to 12:00 on the next two days.

Aluminum foil was used as the collection substrates in the impactor. Before and after sample collection, all substrates were conditioned for 24 h at about 40% RH and 25°C in an air-conditioned room and weighed on a microbalance with a resolution of 1 µg.

Inversion of DLPI data were conducted using the Twomey algorithm (Twomey, 1975; Winklmayr et al., 1990; Marjamaki et al., 2000; Bian et al., 2014). MMD, GSD, and the mass concentrations for different modes (Cm) were derived for each sample. The goodness of fit (R2) of inversion date versus observed date ranged from 0.84 to 0.99, and averaged 0.96.


Method Application for Actual Atmospheric PM

Generally, atmospheric PSDs showed one or more mode (Whitby et al., 1972, Liu et al., 1974). Based on the method described in Section 2.1, the actual atmospheric PM was described by its PSD with either unimodal or bimodal distributions. These cases are discussed separately.


U
nimodal PSDs

An ambient sample with a unimodal mass size distribution (accumulation mode, sampling date: 12 January 2013) was selected to apply the method for the atmospheric PSDs with one mode (Fig. 1). Open circles represent the measured PSD data. The red solid curve is the fitted unimodal PSD with MMD of 1.36 µm, GSD of 1.93, and PM concentration Ca of 665 µg cm–3. A PM2.5 sampler equipped with a size separator with parameters (d50 = 2.5 µm, σg = 1.2) was used to collect the corresponding PM2.5 sample.


Fig. 1. An example of unimodal lognormal PSD and sampling difference with PM2.5 sampler.Fig. 1. An example of unimodal lognormal PSD and sampling difference with PM2.5 sampler.

The area from the intersection of the left side of black solid line (located at 2.5 µm) and the red solid curve represents the actual PM2.5 concentration (Cr). The area from the intersection between the red solid curve and the blue solid curve represents the PM2.5 concentration collected by the sampler (Cs). Mass 1 (highlighted in green) and Mass 2 (highlighted in yellow) represent under-collected and over-collected PM mass, respectively. According to Eqs. (4) and (5), Cs and Cr can be estimated. Then the relative difference (RD) between these two concentrations can be estimated by Eq. (8).


PSDs with 
Bimodal Distribution

A sample showing a bimodal mass size distribution (accumulation mode and coarse mode, sampling date: 8–9, September 2012) was selected to illustrate the method for atmospheric PSDs with two modes (Fig. 2). The open circles represent the measured PSD data. The red solid curve and green solid curve are the fitted PSDs. The red curve is the accumulation mode with an MMD1 of 0.65 µm, GSD1 of 1.74, and PM concentration Ca1 of 77.0 µg cm3. The green curve is the coarse mode with an MMD2 of 5.14 µm, GSD2 of 1.89, and PM concentration Ca2 of 55.2 µg cm3. A PM2.5 sampler equipped with the corresponding size separator (d50 = 2.5 µm, σg = 1.2) is supposed to collect PM2.5.


Fig. 2. An example of bimodal lognormal PSD and sampling error with PM2.5 sampler. (a) Sampling difference in accumulation mode; (b) Sampling difference in coarse mode.Fig. 2. An example of bimodal lognormal PSD and sampling error with PM2.5 sampler. (a) Sampling difference in accumulation mode; (b) Sampling difference in coarse mode.

In Fig. 2(a), the area from the left side of black solid line (located at 2.5 µm) to the red solid curve represents the actual concentration (Cr1) in accumulation mode. The area from the red solid curve to the blue solid curve represents the concentration of accumulation mode collected by the sampler (Cs1). MassA,1 (highlighted in green) and MassA,2 (highlighted in yellow) represent under-collected and over-collected PM mass in accumulation mode, respectively.

In Fig. 2(b), the area from the intersection between the left side of black solid line (located at 2.5 µm) and the green solid curve represents actual concentration (Cr2) of coarse mode. The area from the yellow solid curve and the blue solid curve represents concentration of coarse mode collected by the sampler (Cs2). MassC,1 (highlighted in green) and MassC,2 (highlighted in yellow) represent under-collected and over-collected PM mass in coarse mode, respectively.

Cs and Cr can be estimated from Eqs. (6) and (7). The relative difference (RD) between these two concentrations can be estimated by Eq. (8).


RESULTS AND DISCUSSION



Size Distribution Characteristics of Atmosphere PM

A total of 45 samples sets were collected; 12 sets, 13 sets, 11 sets, and 9 sets of samples were collected in autumn, winter, spring, and summer, respectively.

From the PM2.5 concentrations calculated from the measured ambient size-resolved PM and Technical Regulation on Ambient Air Quality Index issued by MEP (2012), the measured ambient size-resolved PM samples were assigned to six levels, i.e., excellent (I), good (II), slight pollution (III), moderate pollution (IV), heavy pollution (V), and severe pollution (VI). The corresponding number of samples in each level was 5, 14, 9, 6, 9, 2, respectively, covering all of the pollution levels (Table 1).


Table 1. Mass median aerodynamic diameters (MMD, µm), standard deviation (GSD) and modal concentrations (C m, µg m−3) for ambient PSD with in different pollution levels.

The average measured DLPI data and inversion of PSDs for the different air quality levels are illustrated in Fig. 3.


Fig. 3. Average measured and fitted particle size distributions in different air quality levels. Measured data are shown in histograms. The fitted accumulation mode and coarse mode distributions are shown in red and green dotted lines, respectively. The fitted combined two modes are shown in blue solid line.Fig. 3. Average measured and fitted particle size distributions in different air quality levels. Measured data are shown in histograms. The fitted accumulation mode and coarse mode distributions are shown in red and green dotted lines, respectively. The fitted combined two modes are shown in blue solid line.Fig. 3. Average measured and fitted particle size distributions in different air quality levels. Measured data are shown in histograms. The fitted accumulation mode and coarse mode distributions are shown in red and green dotted lines, respectively. The fitted combined two modes are shown in blue solid line.

The PSDs for air quality levels I to V displayed bimodal distributions, i.e., accumulation mode and coarse mode, while they show unimodal distribution in accumulation mode for air quality level VI. As the particulate air pollution increased, the contribution of the coarse mode to PM mass decreased and the contribution of the accumulation mode increased. The MMD (µm), GSD, and modal concentrations (Cm, µg m–3) values for the unimodal or bimodal distributions for each pollution level were derived and presented in Table 1 for use in the subsequent calculations. 


Evaluation of Performance Parameters of PM2.5 Size Separator

The effect of the variations of cutpoint and sharpness for PM2.5 size separator on the relative differences (RD) between the sampler and actual concentrations were investigated. Since the tolerance of cutpoint for PM2.5 size separator was nominally defined as 2.5 ± 0.2 µm (JISC, 2008; MEP, 2013; U.S. EPA, 2013), cutpoints of 2.3 µm, 2.5 µm, 2.7 µm were considered. Values of σg equal to 1.1, 1.2, 1.3, 1.4, and 1.5 were examined. Based on the method in Sections 2.1 and 2.3, RD values between estimated PM2.5 sampler concentration and actual PM2.5 concentration for all sample sets were calculated. Bias values in the range of –5% to +5% are considered permissible (Kenny et al., 2000, 2004). The number of samples with RD exceeding the permissible range are given in Fig. 4. The RD values as a function of cutpoint and sharpness parameters are shown in Fig. 5 for each of the 6 pollution levels ranging from excellent to extreme pollution.


Fig. 4. The number of samples with relative differences (RD) between the estimated PM2.5 sampler and PM2.5 actual concentrations exceeding the permissible range of ± 5%.Fig. 4. The number of samples with relative differences (RD) between the estimated PM2.5 sampler and PM2.5 actual concentrations exceeding the permissible range of ± 5%.


Fig. 5. Contour plots showing the distributions of the average RD for PM2.5 mass values for the variations in cutpoint size (D50) and sharpness (σg) parameters.Fig. 5. Contour plots showing the distributions of the average RD for PM2.5 mass values for the variations in cutpoint size (D50) and sharpness (σg) parameters.

When the cutpoint was 2.5 µm and sharpness σg was in the range of 1.1 to 1.5, the RD between estimated PM2.5 sampler and actual PM2.5 concentrations varied from –4.6% to 3.4% and are all within the permissible range (Fig. 5). Generally, the RD values increased with increasing σg. Higher σg values were then investigated. When σg was ≥ 1.6, the RD value exceeded the permissible range (not shown).

The only time that the average RD values were < –5% was when σg was above 1.4 and the cutpoint was below ~2.35 for severe pollution days. When σg was at the high end of its range and the air quality was good, individual RD values for some of samples exceeded the permissible range. For example, when σg is 1.4, and 1.5, and air quality was excellent, RD values for only two samples exceeded the permissible range. However, the average RD value was within the suggested limits (Fig. 5).

These results suggest that if the cutpoint tolerance for a PM2.5 size separator is 2.5 ± 0.2 µm and σg is ≤ 1.3, the RD values exceeded the permissible range for only two of the 45 samples across all pollution levels. Hence, the cutpoint tolerance and σg values for an acceptable PM2.5 size separator are suggested to be 2.5 ± 0.2 µm and ≤ 1.3, respectively. From these results, the restriction on cutpoint tolerance (i.e., 2.5 ± 0.2 µm) and σg (i.e., 1.2 ± 0.1) for PM2.5 size separators by China MEP is reasonable (MEP, 2013).


Proposal for Performance Parameters of PM1 Size Separator

The variations of cutpoint and σg for PM1 size separator on the effect of the relative differences (RD) between the sampler concentration and actual concentration were also investigated. Cutpoint at 1.05 µm, 1.03 µm, 1.02 µm, 0.98 µm, 0.97 µm, 0.95 µm were considered along with σg values of 1.1, 1.2, 1.3, 1.4, and 1.5. Based on the previously described methods, the RD values between estimated PM1 sampler and actual PM1 concentrations for all samples were calculated. RD values in the range of –5% to +5% are again assumed acceptable by analogy with PM2.5 systems (Kenny et al., 2000, 2004). The number of samples with RD values exceeding the permissible range are shown in Fig. 6. The contour plots showing the distributions of RD values as a function of cutpoint (d50) and sharpness (σg) are shown in Fig. 7.


Fig. 6. The number of s with relative differences (RD) between the estimated PM1 sampler concentration and actual PM1 concentration exceeding the permissible range.Fig. 6. The number of s with relative differences (RD) between the estimated PM1 sampler concentration and actual PM1 concentration exceeding the permissible range.


Fig. 7. Contour plots showing the distributions of the average RD for PM1 mass values for the variations in cutpoint size (d50) and sharpness (σg) parameters.Fig. 7. Contour plots showing the distributions of the average RD for PM1 mass values for the variations in cutpoint size (d50) and sharpness (σg) parameters.

Fig. 7 shows that there are more parameter combinations that produce unacceptable agreement between the estimated measured mass and the actual masses over the range of possible pollutant conditions. Even for the lowest level of PM pollution, the RD is out of the acceptable range for the lowest sizes and largest σg values. As the pollution level increases, unacceptable average mass recoveries are obtained at the high end of the size cutoff even for low σg values.

When the cutpoint is at 1 µm and σg is in the range of 1.1 to 1.3, RD values varied from –3.95% to 0.92% and are all within the permissible range. When σg was 1.4 or 1.5, the RD values varied from –7.75% to 7.92% and their values for nine or seventeen of the 45 samples exceeded the permissible range, respectively. Generally, for each sample, the RDs increased with increasing σg.

When cutpoint was at 0.95 µm and σg was in the range of 1.1 to 1.5, RD values between estimated PM1 sampler concentration and actual PM1 concentration varied from –11.04% to 0.38%. RD values for nine to thirty-four of 45 samples exceeded the permissible range. When the cutpoint was set at 1.05 µm and σg varied in the same range, RD values ranged from –5.81% to 15.28%. RD values for seven to eight of the 45 samples exceeded the permissible range (Fig. 6).

When cutpoint was 0.97 µm and σg was ranged from 1.1 to 1.5, RD values varied from –9.05% to 3.41%. RD values exceeding the permissible range increased with increasing σg. When σg was in the range of 1.1 to 1.2, no RD values exceeded the permissible range, while RD values exceeding the permissible range ranged up to twenty-five when σg increased to 1.5. When the cutpoint was at 1.03 µm and σg varied in the same range, RD values range from –6.55% to 12.36%. RD values for three to eleven of the 45 samples exceeded the permissible range (Fig. 7).

When cutpoint was set at 0.98 µm and σg is in the range of 1.1 to 1.5, RD values vary from –8.87% to 4.92%. RD values exceeding the permissible range again increased with increasing σg. For σg in the range of 1.1 to 1.2, there were no RD values exceeding the permissible range. However, there were up to twenty-two high RD values when σg increased to 1.5. With a cutpoint of 1.02 µm and σg varied in the same range, the RD values ranged from –6.94% to 10.89%. When σg was at 1.1, no RD values exceeded the permissible range. When σg was in the range of 1.2 to 1.4, only one RD value exceeded the permissible range. When σg increased to 1.5, ten RD values exceeded the permissible range.

From these results for typical urban atmospheric aerosols, a cutpoint tolerance for a PM1 sampler of 1.00 ± 0.02 µm and σg no greater than 1.2 produced RD values for only one sample that exceeded the permissible range. Hence, the cutpoint tolerance for an acceptable PM1 size separator is proposed to be 1.00 ± 0.02 µm with σg no greater than 1.2. Compared with PM2.5 sampler, the PM1 sampler is more sensitive to the variation in cutpoint and only a relatively small range of variation is allowable. For the measurements made in Beijing, 2.5 µm is closer to the minimum between the accumulation and coarse modes than 1 µm (Fig. 3). Thus, shifts in cutpoint of PM2.5 size separator caused smaller differences in observed mass concentrations (Cao et al., 2013).


Analyses for Hypothetical PSDs

Vanderpool et al. (2001) summarized previous ambient size distribution measurements, and numerically constructed three idealized, bimodal particle size distributions, i.e., fine, typical, and coarse ambient aerosol distributions. The three idealized distributions have been prescribed in 40 CFR Part 53 for evaluation of candidate PM2.5 size separators (U.S. EPA, 2013). PM2.5/PM10 ratios measured in this study across four seasons and different pollution levels vary from 0.52 to 0.87 and can represent the fine (PM2.5/PM10 ratio = 0.94), and the typical (PM2.5/PM10 ratio = 0.55) ambient aerosol distributions (Vanderpool et al., 2001; U.S. EPA, 2013). Our PM2.5/PM10 ratios are much greater than the idealized coarse ambient aerosol distributions (PM2.5/PM10 ratio = 0.27), which indicates that our measured PSDs cannot represent coarse aerosol distributions.

In order to evaluate the performance parameters of a PM1 size separator for the three idealized distributions, especially for the coarse aerosol distributions, RD values between estimated PM1 sampler concentration and actual PM1 concentration for the three idealized particle size distributions were also calculated.

The RD values between estimated PM1 sampler concentration and PM1 actual concentration for the three idealized, bimodal particle size distributions are illustrated in Fig. 8. When the cutpoint was at 0.95 µm and σg ≥ 1.2, RD values for fine ambient aerosol distributions exceeded the permissible range. When cutpoint was at 0.95 µm and σg ≥ 1.4, or cutpoint was at 0.97 µm and σg = 1.5, the RD values for the typical ambient aerosol distribution exceeded the permissible range. When the cutpoint was at 0.95 µm and σg = 1.5, the RD value for coarse aerosol distributions exceeded the permissible range. The other combinations of d50 and σg, RD values were within the permissible range. Thus, the proposed performance parameters of PM1 sampler for typical urban atmospheric aerosol in last section, i.e., d50 = 1.00 ± 0.02 µm, σg ≤ 1.2 is suitable for the three idealized ambient aerosol distributions.


Fig. 8. Relative differences (%) between estimated PM1 sampler concentration and PM1 actual concentration under three idealized, bimodal particle size distributions (a) Fine distribution, (b) Typical distribution, and (c) Coarse distribution.Fig. 8. Relative differences (%) between estimated PM1 sampler concentration and PM1 actual concentration under three idealized, bimodal particle size distributions (a) Fine distribution, (b) Typical distribution, and (c) Coarse distribution.Fig. 8. Relative differences (%) between estimated PM1 sampler concentration and PM1 actual concentration under three idealized, bimodal particle size distributions (a) Fine distribution, (b) Typical distribution, and (c) Coarse distribution.


CONCLUSIONS


Based on the interaction between performance characteristics for PM2.5 or PM1 size separators and actually measured typical urban PSDs in four seasons covering all pollution levels and three idealized ambient PSDs, the RD between estimated PM2.5 or PM1 sampler concentrations and actual concentrations have been systematically investigated. For the PM2.5 size separator, if the cutpoint tolerance is 2.5 ± 0.2 µm and σg is ≤ 1.3, the RD values for most typical urban atmospheric aerosol conditions are within the permissible range (± 5%).

For the PM1 size separator, tolerances for the cutpoint and σg are proposed to be 1.0 ± 0.02 µm and ≤ 1.2, respectively. Those parameters would provide acceptable performance for most typical urban atmospheric aerosol conditions and the idealized ambient aerosol distribution.


ACKNOWLEDGEMENT


This work was supported by the National Nature Science Foundation of China (Grant No. 41575119, 41275121), the program of Beijing Municipal Science & Technology Commission (No. Z161100000716004), the National Key Basic Research and Development Program of China (No. 2014CB931802).



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5.9
2020CiteScore
 
 
81st percentile
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2020 Impact Factor: 3.063
5-Year Impact Factor: 2.857

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