SPARTAN is composed of ground-based instruments that measure fine-particle concentrations and allow for the determination of some compositional features (i.e. water-soluble ions, black carbon, and major metals). Our primary focus is on determining PM2.5 mass. We subdivide this goal into estimating hourly, 24 h mean, and long-term (annual and seasonal) concentrations. Daily mean PM2.5 is compared and related with total column AOD measurements during daytime satellite overpass times. Coarse aerosol mass, defined as PMc≡ PM10–PM2.5, is measured to assess PM10 concentrations. Coarse mass provides additional information on the particle size distribution of relevance for both aerosol optical properties and health effects. A major consideration for the instrumentation is capability for near-autonomous operation. Cost efficiencies are considered, given the grass-roots nature of this network.

Each SPARTAN site includes a combination of continuous monitoring by nephelometry and mass concentration from sampling on filters. Nephelometer backscatter and total light scatter at three wavelengths provide high temporal resolution and some information on particle size. We constrain nephelometer light scattering with filter-based measurements over multi-day intervals; hence the combination of these measurements yields estimates of hourly PM2.5 values.

All SPARTAN instruments to date have been designed and manufactured by AirPhoton, LLC (www.airphoton.com). Attributes of these instruments include low maintenance, portability, and field readiness. Installation is straightforward; both the nephelometer and air sampler mount directly to a secure support pole. Sections 3.2 and 3.3 summarize the most recent instrument designs, but they will likely be modified as the network matures. Total power consumption is minimal (34 W) and the instruments are being successfully operated in Nigeria using a solar panel and battery. Martins et al. (2015) will provide more detail about the instrument characteristics and performance.

Filter-based measurements are collected using an AirPhoton SS4i automated air sampler. Each station houses a removable filter cartridge inside a weather-resistant Pelican case such that the filter inlet faces downwards. Airflow and back pressure are logged every 15 s onto a memory card with capacity for 2 or more years of data. The eight-slot filter cartridge protects the filters during transport to and from the field and reduces the frequency of site visits. Sampled cartridges are mailed to the central SPARTAN clean-room laboratory at Dalhousie University every 2 months.

Figure 2 shows a diagram of the filter assembly. Each cartridge contains seven pairs of pre-weighed 25 mm 2 µm pore-size PTFE (225-2726, SKC) and capillary membrane (custom grease-coated E8025-MB, SPI) filters sampled actively at 4 L min-1 for the programmed period. An eighth cartridge slot contains a travelling blank. An important aspect of this filter assembly design is the automatic switching between filter pairs. Incoming aerosols pass through a bug screen and a greased (ultra-high vacuum) impactor plate, which traps aerosols larger than 10 µm in diameter. Coarse-mode (PMc) particles are then removed by a capillary (Nuclepore) membrane (8 µm pore diameter, 5 % porosity). The concept of employing capillary filters for size selection has been well established (Heidam, 1981; John et al., 1983; Parker et al., 1977). This stacked filter unit (SFU) arrangement has similarities with the Gent model (Hopke et al., 1997) and the SFU design has been shown to compare well with other aerosol filter systems (Hitzenberger et al., 2004). The 50 % aerosol capture efficiency is at approximately 2.5 µm for the selected flow rate and pore size (Chow, 1995; John et al., 1983). Coarse-mode solid particles are susceptible to particle bounce (John et al., 1983). The manufacturer (SPI) coated the capillary pore membrane surfaces with a thin layer of vacuum grease to enhance their capture efficiency. Fine-mode (PM2.5) aerosols are collected on 2 µm fibre PTFE filter surfaces, which are compatible with a variety of chemical analyses (Chow, 1995).

The AirPhoton IN100 nephelometer is a continuous sampling, optically based device measuring total particulate scatter bsp at red (632 nm), green (532 nm), and blue (450 nm) wavelengths over the angular range 7 to 170∘. The AirPhoton nephelometer records backscatter (bbks) information between 92 and 170∘. Light-emitting diodes supply the light source. Total scatter is related to total aerosol concentration, whereas backscatter provides information on aerosol size distribution. The forward and backscattering measurements are made independently. Correction for angular truncation is in development. Internal sensors measure the incoming air stream for ambient relative humidity, temperature, and pressure. The nephelometer is a separate module from the air sampler and mounts to a support stand. The inlet is a 10 cm length of copper 1/4′′ tubing ending with a plastic bug screen. Inlet wall losses for particles below 2.5 µm are expected to be less than 2 % (Liu et al., 2011). Light-scatter and backscatter are logged every 15 s on a 2 GB SD card in units of inverse megametres (Mm-1). Ambient air temperature, humidity, and pressure are also recorded at the same frequency on the memory card. The nephelometer is not heated nor is any size cut introduced, and the absence of a dryer also reduces concerns about evaporation of semi-volatile components. The ambient nature of the measured aerosol scatter makes these results consistent with aerosol scatter observed by satellite.

The nephelometer light scattering by particulates, bsp, is reported as 1 h averages, bsp,1h. Hourly dry aerosol scatter component, bsp,dry-1h, is calculated as bsp,dry-1h=bsp,1hRH<RHmaxfm(RH). The term RHmax signifies the exclusion of bsp values for which the hourly averaged humidity exceeds a threshold, initially taken as 80 %, to reduce uncertainty in the effects of aerosol water given the uncertain nature of aerosol composition. The hygroscopic volume correction factor fv (RH) accounts for the uptake of water in aerosols. We initially use the humidity correction factor fv(RH)=1+κ⋅RH/(100-RH). The volume growth factor can often be within experimental error (Kreidenweis et al., 2008) and where the hygroscopicity parameter κ depends on aerosol composition. For pure compounds, κ is 0 (insoluble and hydrophobic compounds), 0.15 (aged organics), 0.5–0.7 (ammonium sulphate and nitrate), and 1.2 (sea salt) (Hersey et al., 2013; Kreidenweis et al., 2008). Based on our studies in Beijing and the United States, we have found κ=0.2 represents a variety of aerosol mixtures. This value is similar to that obtained for urban aerosols (Padró et al., 2012). Future work will refine the fv(RH) calculation for specific site locations via measured composition and its associated hygroscopicity.

Hourly nephelometer scatter, as measured by the nephelometer, is approximately proportional to PM2.5 mass (Chow et al., 2006); however, absolute mass predictions depend on aerosol composition. We therefore relate relative fluctuations in dry aerosol scatter from Eq. (1) anchored to an absolute filter mass (PM‾2.5,dry,9d): PM2.5,dry-1h=PM‾2.5,dry,9dbsp,dry-1hbsp,dry,9d‾. The “dry” subscript refers to the low humidity conditions at which filters are weighed (Sect. ). Quantities with bars above them are the 9 day means.

Measurement uncertainties can be obtained through analyses of blank and replicates. Direct sources of measurement uncertainty are due to absolute PM2.5 weighing (1 µg m-3), nephelometer scatter (1 Mm-1), and AOD at visible wavelengths (0.01). We assessed method uncertainties, i.e. the application of Eq. (2), by statistical sub-sampling of data and using federal equivalence method (FEM) instruments for comparison. The method of sampling a filter for 24 h spread over 9 days introduces a relative uncertainty of 13 % compared with sampling over an entire 9 day interval (cf. Sect. A1.3). Equation (2) was evaluated in a simulated test using 24 h PM2.5 measurements and nephelometer scatter and compared with hourly tapered element oscillating microbalance (TEOM) PM2.5. The resultant prediction accuracy was 1 µg m-3 + 17 % × [PM2.5] at three North American sites and for Beijing (cf. Appendix A1.5). Uncertainties from chemical extractions are listed in Sect. 3.2.2.

The evaluation of the SPARTAN network is an ongoing task. Martins et al. (2015) describe and evaluate the AirPhoton instrumentation in detail. Appendix A2 describes an initial pilot study from university sites in Beijing, Halifax, and Atlanta. Appendix A2.5 describes a Harvard Impactor being circulated across sites for inter-comparison. We have begun a nephelometer and PM2.5 composition inter-comparison at Mammoth Cave, Kentucky, between SPARTAN and IMPROVE. Subsequent measurements at the EPA South Dekalb supersite near Atlanta, Georgia, will compare with hourly federal reference method beta attenuation monitor (FRM-BAM) PM2.5 measurements. Comparisons at NOAA and GAW stations would also be instructive. Information gleaned from these assessments is being and will continue to be used to refine instrumentation and protocols.

The ratio of ground-level PM2.5 to AOD is fundamental in inferring PM2.5 from satellite observations of AOD. We introduce initial measurements of this ratio to provide an example of the type of information SPARTAN can provide. The ratio η, as defined by van Donkelaar et al. (2010), is the ratio of 24 h PM2.5 to AOD at satellite overpass time whereas PM2.5,24h is the daily average of the hourly values obtained in Eq. (2). We define AOD10-14h as the ground-measured AERONET AOD averaged from 10:00 to 14:00 LT to include a range of common satellite overpass times and interpolated via the Ångström exponents to the wavelength (550 nm) typically reported for satellite retrievals. η=PM2.5,24hAOD10-14h The top panels of Fig. 3 show daily-varying values of η in Beijing, China, for selected months in 2013–2014. Daily PM2.5 ranged from 7 to 228 µg m-3 whereas AOD10-14h ranged from 0.05 to 3.8 during the measured sampling periods (middle panels). We observe that the PM2.5 / AOD ratio exhibits dramatic daily variation of more than 1 order of magnitude as well, ranging from below 50 µg m-3 to above 900 µg m-3. We calculated the contribution of AOD10-14h and PM2.5,24h to the variation of the dependent variable η as the relative contribution to the coefficient of multiple determination (R2), based on the product of the correlation coefficient (ryx(j)) and standardized regression coefficients (aj) for each variable j. In Beijing the contributions to η of PM2.5,24h and 1/AOD10-14h are 0.07 and 0.51, respectively. The larger contribution from AOD10-14h indicates the importance of accounting for aerosol aloft.

We offer further insight into the variation in η by decomposing it into three terms: η=bsp,10-14hAOD10-14h︸T1bsp,24hbsp,10-14h︸T2PM2.5,24hbsp,24h︸T3. Term 1 (T1) is related to height, H, for which aerosol scatter would be constant above ground level to obtain the measured AOD and can be thought of as the inverse effective scale height if the total column AOD were distributed vertically according to bsp(z) ∼ e-z/H. The second term (T2) accounts for the diurnal variation in near-ground scattering during typical satellite overpass time (bsp,10-14h) versus over the entire 24 h day (bsp,24h). Term 2 requires only measurements from the nephelometer. The third term (T3) is the inverse of the mass scattering efficiency, which is a function of aerosol size and composition. All nephelometer scatter and AERONET AOD measurements are interpolated to 550 nm via the nephelometer Ångström exponents to match the wavelengths typically reported for satellite AOD. Hourly scatter values for which RH > 80 % (Eq. 1) or bsp,532 > 1300 Mm-1 (nonlinear regime; Appendix A2) are omitted. The product of the three terms in Eq. (4) will cancel to yield Eq. (3).

Figure 3 also shows a time series for these three terms during two sampling intervals. We interpret the time series by determining the contribution of total variance in η for Eq. (4) with respect to T1, T2, and T3. Term 1, related to effective scale height, has the largest contribution to the variance in η (0.4). Term 2, related to the diurnal variation in atmospheric scattering, has a smaller, though similar, contribution (0.34). Term 3, related to the mass scattering efficiency, does not contribute significantly to the variance in η (contribution = 0.03). Given that hourly PM2.5, as defined in Eq. (2), depends on bsp, we also calculated ηBAM as inferred with a second AERONET sun photometer in Beijing and external hourly PM2.5 measurement using a beta attenuation monitor on the roof of the US Embassy, 8 km southeast of Tsinghua University. The contributions for the three terms to the variance in η retain the same essential features, with contributions for T1 = 0.52, for T2 = 0.2, and for T3 ∼ 0. The majority of the daily variance in η in Beijing is therefore explained by the effective scale height of aerosol scattering and more specifically by the relative ground-to-column scattering. Diurnal cycles have some influence on total variance whereas mass scattering efficiency exhibits little influence on the variance in η. Future work will examine these relationships at other sites, temporally, in detail.

The time periods selected for Fig. 3 represent two separate protocol periods for air filter sampling in Beijing. February–April 2013 was part of the initial pilot study with filters exchanged every 24 h. The December 2013–January 2014 period was part of the “beta” testing of the 9 day sampling period. It is noteworthy that the relationship of η to the three terms in Eq. (4) remains comparable for both time periods despite the extended filter sampling protocol in the latter period.

We have begun to examine factors affecting the global variation in η in order to explore how satellite AOD relates to PM2.5 in different regions of the world. Table 2 contains mean values of η and related measurements across SPARTAN sites. Mean PM2.5 concentrations varied from 3.2 µg m-3 (Dalhousie) to 102 µg m-3 (IIT Kanpur), whereas mean AOD across sites varied from 0.09 (Dalhousie) to 0.8 (Dhaka). Spatial variation of η is weaker than spatial variation in PM2.5 or the temporal variation in η in Beijing. There is a tendency for η to increase with PM2.5; the contribution to the spatial variance in η is larger for PM2.5 (contribution = 0.71) than for AOD10-14h (contribution = -0.08). We again used Eq. (4) to understand the factors affecting η. Satellite-coincident ground-level atmospheric scattering AOD ratios contribute significantly to the ratio η (T1; contribution = 0.59), as does the mass extinction efficiency (T3; contribution = 0.46); however, the diurnal variation contributes little (T2; contribution = -0.22). The sub-Saharan site of Ilorin had the lowest values of η and the highest AOD10-14h / bsp,10-14h ratio, perhaps reflecting the larger effective aerosol scale height (T1) that may arise from transported dust aloft, and influence from coarse particles, as indicated by a low PM2.5 / PMc ratio. We measured the lowest AOD10-14h / bsp,10-14h ratio at the Bandung site, which could be influenced by local volcanic emissions. We found that locations with enhanced PM2.5 generally have lower AOD10-14h / bsp,10-14h ratios (T1), implying lower scale height with a larger fraction of aerosol scattering near the surface. Dhaka, however, had a similar ratio (i.e. only 40 % higher) compared with Halifax as well as similar η values (4 % higher) despite 10-fold higher PM2.5 levels, implying a pronounced aerosol scattering layer above Dhaka. Coarse PM also plays a role in Dhaka as apparent from the low PM2.5 / PMc ratio. We caution that these results are preliminary, but they demonstrate the potential to understand the relationship between PM2.5 and AOD at a variety of sites around the world.

Table 2 also contains an initial comparison of the measured values of η versus the simulated values from the GEOS-Chem simulation that van Donkelaar et al. (2010) used to produce global satellite-based PM2.5 estimates. We include in this comparison measurements from the only two locations worldwide (Taiwan and Mexico City) that we found with nearly collocated (within 3 km) AOD and PM2.5 measurements. Comparison of mean PM2.5 and AOD reveals that in most locations, measured ratios were within range of GEOS-Chem estimates, though several are above this range, including in Bandung, Kanpur, Manila, and Halifax. The Bandung site data were well above the GEOS-Chem ratio; however, a volcanic eruption during sampling likely played some role. Future work will conduct a more rigorous comparison with identical modelled time series.

Additional information from SPARTAN measurements is being prepared for detailed analysis. Already we see that sulfate concentrations varied by more than 1 order of magnitude across sites. Nitrate concentrations in Kanpur and Beijing were 1 order of magnitude higher than elsewhere. Cations offer additional information about sea salt and fine dust. The Ångström exponent and the backscatter fraction measured by the nephelometer offer the prospect of retrieving aerosol size following Kaku et al. (2014).

Our initial measurements indicate that the vertical profile of aerosol scattering, which we represent by an effective aerosol scale height, is the most important factor affecting temporal and spatial variation in PM2.5 / AOD. Spatial variation is also strongly affected by the mass scattering efficiency, which implies that efforts to apply satellite AOD to estimate long-term PM2.5 concentrations must be attentive to processes affecting aerosol size and composition. Longer time series from our ongoing measurements will test the robustness of these initial conclusions.