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. 2024 Mar 11;1(4):283–293. doi: 10.1021/acsestair.3c00069

Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications

Xuan Liu †,*, Jay R Turner †, Christopher R Oxford †, Jacob McNeill †, Brenna Walsh †, Emmie Le Roy †, Crystal L Weagle †, Emily Stone †, Haihui Zhu †, Wenyu Liu †, Zilin Wei †, Nicole P Hyslop ‡, Jason Giacomo ‡, Ann M Dillner ‡, Abdus Salam §, Al-amin Hossen §, Zubayer Islam §, Ihab Abboud ∥, Clement Akoshile ⊥, Omar Amador-Muñoz #, Nguyen Xuan Anh ∇, Araya Asfaw ○, Rajasekhar Balasubramanian ●, Rachel Ying-Wen Chang □, Craig Coburn ■, Sagnik Dey △, David J Diner ▲, Jinlu Dong ▽, Tareq Farrah ▼, Paterne Gahungu ◇, Rebecca M Garland ◆,◑,◐, Michel Grutter de la Mora #, Sina Hasheminassab ▲, Juanette John ◆, Jhoon Kim ◒, Jong Sung Kim ◓, Kristy Langerman ⊖, Pei-Chen Lee ⦶, Puji Lestari ⬡, Yang Liu ⬢, Tesfaye Mamo ⬠, Mathieu Martins ▼, Olga L Mayol-Bracero ◨, Mogesh Naidoo ◆, Sang Seo Park ◧, Yoav Schechner ⬒, Robyn Schofield ⬓, Sachchida N Tripathi ⬕, Eli Windwer ⬔, Ming-Tsang Wu ☆,★, Qiang Zhang ◮, Michael Brauer ◭, Yinon Rudich ⬔, Randall V Martin †
PMCID: PMC11020157  PMID: 38633206

Abstract

graphic file with name ea3c00069_0005.jpg

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019–2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 μg/m3) in PM2.5 is also found in Dhaka (Bangladesh) along with a high average TEO level (6 μg/m3). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

Keywords: particulate matter, monitoring network, elemental composition, quality assurance, dust, trace elements, health risk assessment, source apportionment

Short abstract

This study describes the methodology and examines the environmental and health implications of the elemental composition of ambient particulate matter measured with a globally distributed network.

1. Introduction

Elemental characterization of ambient particulate matter (PM) provides concentrations of major (crustal) and trace elements, which can be used to estimate two PM components—mineral dust and trace element oxides (TEO).1,2 As the most dominant global aerosol by mass, mineral dust can strongly reduce visibility, perturb climate systems, affect biogeochemistry, and cause adverse health effects.3 Some epidemiological studies find that acute exposure to dust in PM10 (aerodynamic diameter <10 μm) or PM2.5 (aerodynamic diameter <2.5 μm) during dust events as well as long-term exposure is associated with cardiovascular and respiratory events and diseases.4−9 Dust has both natural and anthropogenic sources, such as deserts, unpaved roads, construction, and agricultural activities. Trace elements are often more concentrated in PM2.5 and primarily emitted by anthropogenic sources such as fossil fuel combustion, industries, and traffic. Many of the trace elements (e.g., Pb, As, Cd, Cr) have strong associations with morbidity and mortality.10,11 Concentrations of hazardous trace elements are particularly high in low-income and middle-income countries (LMICs) because of unregulated activities during urbanization and industrialization.12−14 Ground-level observations of atmospheric elements are important to estimate the exposure to dust and trace elements, assess health risks, and investigate emission sources as well as improve atmospheric models. However, few monitoring networks of the PM chemical composition exist in LMICs. Uniform sampling protocols and reliable analyses are also needed to enable comparisons around the world.

The Surface PARTiculate mAtter Network (SPARTAN, https://www.spartan-network.org/) is a long-term project that measures ground-based speciated PM at globally dispersed sites in densely populated regions.15 This network is designed to expand available global ground-based observations of PM composition and to provide information to evaluate and improve satellite-based estimates of PM2.5. To our knowledge, SPARTAN is the only global monitoring network that measures the elemental composition of PM2.5 and, to a lesser extent, PM10. Samples are collected from SPARTAN sites and analyzed for elemental composition at one central laboratory using consistent protocols, which ensures the comparability of data among the different sites. Beginning in 2019, the elemental measurements of SPARTAN samples have been conducted by Energy-Dispersive X-ray Fluorescence (ED-XRF) spectroscopy, which is also used in the U.S. national PM2.5 Chemical Speciation Network (CSN) and the U.S. Interagency Monitoring of PROtected Visual Environments (IMPROVE) network.16 XRF is widely used to characterize the elemental composition of PM filters mainly because of its non-destructive nature that requires no acid digestion, making the analysis less labor-intensive and allowing additional analysis such as Ion Chromatography.17,18 XRF can also measure the major dust element Si. Prior analysis of SPARTAN filters for 2013–2019 identified large global variations in measured airborne metal concentrations, but this analysis used Inductively Coupled Plasma Mass Spectrometry (ICP-MS) with nitric acid digestion that introduced uncertainty in extraction efficiencies for some crustal elements such as Fe and Al and could not measure Si.19 There is a need to examine more recent filters by using XRF to assess the robustness of prior conclusions and their degree of persistence over time.

Especially for networks that operate over long periods across multiple sites, high-quality and consistent data are needed to interpret the measurements.16 Robust quality assurance (QA) measures are needed, including appropriate calibration, filter acceptance testing, routine analyses of blanks and standards, and appropriate blank subtraction to obtain reliable elemental data. Method detection limits (MDLs) and uncertainties for elemental concentrations are needed to evaluate the data quality.

Both high-quality elemental data and an accurate dust equation that sums dust compounds based on elemental data are essential to accurately estimate dust mass. However, most dust equations fail to account for all major dust compounds such as carbonate or fail to exclude non-dust components of some crustal elements such as K from biomass burning. A recently developed global dust equation with region-specific coefficients tackles these challenges and takes account of dust composition differences across regions,2 which allows calculating and comparing dust mass at globally distributed sites.

The objectives of this paper are to (1) describe the laboratory methods of measuring PM elemental composition with ED-XRF employed in the SPARTAN network, (2) describe QA methods and reported values, and (3) explore this new global PM elemental dataset from SPARTAN. The dataset is analyzed to examine the concentration levels of dust and TEO across globally distributed sites, evaluate health risks caused by hazardous trace elements, and explore emission sources of trace elements at the site with the highest estimated health risk levels.

2. Materials and Methods

2.1. Sampling Overview

Table S1 provides specific location information on 27 SPARTAN sites with available XRF data examined in this study. High population density and poorly sampled regions are two key factors in the selection of SPARTAN sites. Given SPARTAN’s objective to evaluate and enhance PM2.5 estimates derived from satellite retrievals of aerosol optical depth, site locations should have available sun photometers providing aerosol optical depth measurements. Site safety and access to electricity are also considered in the selection process. The chosen SPARTAN sites represent a variety of PM2.5 concentrations and compositions. More details about the early development of SPARTAN are provided by Snider et al.15,20 and Weagle et al.21

Sampling procedures and chemical analysis instrumentation have been updated over the last few years. Most sites retain the original standard sampling protocol, while select sites have more frequent sampling as part of the National Aeronautics and Space Administration (NASA)–Italian Space Agency (ASI) Multi-Angle Imager for Aerosols (MAIA) satellite mission.22 For the standard sampling protocol, AirPhoton (Baltimore, MD) SS5 sampling stations are employed to collect PM2.5 and PM10 on 25 mm Teflon filters (PT25DMCAN-PF03A, Measurement Technology Laboratories) assembled in a cartridge as described by McNeill et al.19 The sampling station uses a sharp-cut cyclone that operates at a target flow rate of 5 L/min to collect PM2.5 samples and 1.5 L/min to collect PM10 samples with a sampling period of 54 days for one filter cartridge. Each cartridge consists of six filters for PM2.5, one for PM10, and one for a field blank. The station collects PM2.5 at staggered 3 h intervals followed by 30 min intervals of PM10 sampling over a 9 day period to generate one 24 h PM2.5 sample covering one diel cycle (Figure S1). The entire 54-day period generates six 24 h PM2.5 samples and one 24 h PM10 sample. The sampling time per day for the Canadian sites is doubled to increase filter mass loading, given that the PM2.5 concentration is typically low at these sites. Cartridges are assembled with preweighed filters in the central laboratory, shipped to each site for sampling, and shipped back to the central laboratory for a series of analyses including elemental analysis.

The SPARTAN sites that have been selected or established since 2021 as part of the MAIA mission are designed to collect PM2.5 samples in the Primary Target Areas to study the health impacts of exposure to different types of airborne particles.22 These sites use the same instrumentation (AirPhoton SS5), Teflon filter, cyclone, and target flow rate of 5 L/min to collect PM2.5 continuously for 24 h from 9 am to 9 am at a mission-defined frequency, currently every 3 days with a planned increase to every 2 days around the time of MAIA launch in 2025. The sites that apply the MAIA sampling protocol are indicated in Table S1.

About 1800 PM2.5 samples and 140 PM10 samples collected during 2019–2023 as well as a few samples from December 2018 have been analyzed by XRF and are used in this study. The specific sampling period, seasons with samples, and number of samples for each site are summarized in Tables S2 and S3.

2.2. Instrumentation and Standards

Pre- and postsampling weighing of filters is performed using an automated weighing system (MTL AH500E). Prior to weighing, each filter is equilibrated for 24 h in an environment where the temperature and relative humidity are controlled to 21.5 ± 1.0 °C and 35.0 ± 1.5%, respectively. Each filter is then weighed three times using a Mettler Toledo XPR6UD5 microbalance with a 0.5 μg readability within the controlled environment. The mean of these three measurements is calculated as pre- or postweights, and the difference between them provides the PM mass.

The Epsilon 4 ED-XRF instrument (Malvern PANalytical) is used to analyze the elemental composition of SPARTAN samples. Details about the Epsilon 4 configuration are provided in Text S1 in the Supporting Information. Spectrum background subtraction (blank correction) is performed to obtain the net intensity of each element, which is converted to mass loading (μg/cm2) based on calibration curves established using a set of standards. Five analytical conditions with different mediums, X-ray filters, X-ray tube voltages and currents, and analysis times are applied to measure the 26 elements reported to SPARTAN as summarized in Table 1.

Table 1. Epsilon 4 ED-XRF Application Used to Analyze SPARTAN Samples.

reported elements medium X-ray filtera voltageb (kV) current (μA) analysis time (s)
Na, Mg, Al, Si, S, Cl helium Ti 9 1666 720
K, Ca, Ti air Al-50 12 1250 540
V, Cr, Mn, Ce air Al-200 20 750 540
Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Pb air Ag 50 300 540
Cd, Sn, Sb air Cu 50 300 540
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a

Al-50 and Al-200 represent aluminum with thicknesses of 50 and 200 μm, respectively.

b

The maximum X-ray tube voltage is 50 kV.

Calibration curves are established with 62 standards to include enough standards for each element preferably covering the mass loading range of SPARTAN samples. Commonly used and commercially available Micromatter (Surrey, Canada) single-element/compound reference materials (RMs) and the U.S. National Institute of Standards and Technology (NIST) standard reference material (SRM) 2783 are included in the standards. However, these RMs insufficiently represent filter material and mass loadings of common ambient PM samples.23 Interlaboratory evaluations show that the single compound and multielement RMs generated recently by the University of California-Davis (UCD) are a useful resource to address this issue.23−25 Therefore, UCD-made RMs are also included in our standards. Background subtraction is performed on all standards using the corresponding blanks. The correlation coefficients of calibration curves determined by linear regression are ≥0.99 for all the elements except Mg with a correlation coefficient of 0.98.

2.3. Quality Assurance

Acceptance testing of filters is performed to ensure filter quality by evaluating the contamination level of elements on new filters from the manufacturer.16 Five test filters from each new filter box (100 filters) are randomly selected and measured for elemental loadings using XRF. If the measurements are all within the acceptance limit, defined as the mean plus 3 times the standard deviation from measurements of 100 laboratory blank (LB) filters from multiple boxes, then the new filter box is accepted for use in field sampling from the aspect of elemental analysis.

Routine quality control (QC) measures are conducted to verify calibration and monitor the long-term stability of the XRF instrument, which includes repeated analyses of one LB, RMs, and representative SPARTAN samples. The RMs include a UCD-made multielement RM and NIST SRM 2783. The representative SPARTAN samples are nine selected samples that typify low, medium, and high loadings in the SPARTAN network. Details about the routine QC activities including frequencies, criteria, and corrective actions that are built on the QC methods used by IMPROVE26 are provided in Table S4 and Text S2 in the Supporting Information. When new calibrations are performed, an additional quality check is performed by conducting replicate measurements of Micromatter stoichiometric standards and ensuring the relative uncertainty is <5%.

Background contamination levels from both the laboratory and field are considered in calculating the method detection limit (MDL) by using the greater of the MDL determined from either field blanks (FBs) or LBs. The FB filter is loaded along with the seven sample filters in one of the eight filter slots of a cartridge, which is shipped to the field and installed in the sampling station without air pulled through the FB filter. Following methods used by the IMPROVE network,26 the MDLs based on FBs (MDLFB) and LBs (MDLLB or analytical MDL) are calculated as the 95th percentile minus the median mass loading of 100 FBs and LBs, respectively. Concentrations below MDL including negative values are retained and posted on the SPARTAN website to avoid losing potentially useful data and to allow data users the flexibility to process these values. Both analytical MDLs and final MDLs are reported on the SPARTAN website.

The overall measurement uncertainties for elemental mass concentrations are estimated by combining additive and proportional (mass and volume) uncertainties

2.3. 1

where σ is the overall uncertainty for the elemental concentration (ng/m3), σadditive is the additive uncertainty (ng/cm2), σproportion is the total proportional uncertainty, C is the elemental concentration (ng/m3), S is the known deposition area for 25 mm filters (3.53 cm2), V is the sampled volume (m3), σmass is the proportional mass uncertainty, and σvolume is the proportional volume (flow rate) uncertainty. The additive uncertainty of each element is derived by dividing the MDL by 1.645, which is the critical value of the Z-score in a one-tailed test for a 5% significance level. Deposition area and sampled volume are used to convert the unit of σadditive from mass loading (ng/cm2) to mass concentrations (ng/m3). σmass is estimated as the mean of relative standard deviations from monthly QC reanalyses of the representative SPARTAN samples using only the samples with mean >3 × MDL. σvolume is estimated as the relative standard deviation from 120 flow rate measurements of six flow meters (3.5%).

2.4. Dataset Analysis

We use the XRF method described above at all SPARTAN sites and a global dust equation recently developed by Liu et al.2 to estimate and compare concentration levels of dust and TEO at the global scale. First, ambient elemental mass concentrations (ng/m3) are calculated by using elemental mass loadings (ng/cm2) measured by XRF and sampling flow measurements along with the deposition area on the filter. Dust concentrations are computed from concentrations of major crustal elements using the global-scale mineral dust equation2

2.4. 2

where MAL is a mineral-to-aluminum mass ratio used to account for the dust components of K, Mg, and Na, CF is a correction factor used to incorporate other missing compounds, mainly carbonate, and the constant before each element is the oxide factor that converts elements to oxides. Region-specific MAL and CF values2 are used for different SPARTAN sites (Table S5). The MAL values are derived from dust composition data with negligible influence of non-dust sources, so the global dust equation is not susceptible to K, Mg, and Na from non-dust sources such as biomass burning and sea salt. Specific measurements of K, Mg, and Na are not used in the dust equation. TEO concentrations are calculated by summing all the oxides of measured elements retained after excluding S, Cl, and major crustal elements (Si, Al, Fe, Ca, Ti, Na, Mg, and K)

2.4. 3

where oxide factors summarized by Reff et al.27 are applied to calculate oxide concentrations by assuming common oxide forms of the trace elements.

The SPARTAN elemental dataset measured using this consistent methodology enables evaluation and comparison of health risks from exposure to deleterious trace elements in PM2.5 across the set of globally distributed measurement sites. Both carcinogenic risk (CR) and noncarcinogenic risk quantified by hazard quotient (HQ) are estimated by applying the U.S. Environmental Protection Agency (EPA) health risk assessment model,28 which includes the estimation of exposure concentration and the use of reference toxicity values (Table S6).29 CR is estimated by multiplying exposure concentration and inhalation unit risk, while HQ is the ratio of exposure concentration to inhalation reference concentration (details in Text S3). Summing the HQ for multiple elements yields the hazard index (HI). The mean elemental concentrations of available PM2.5 samples from SPARTAN sites over the study period are used to estimate exposure concentrations. Elements with <50% of samples above MDLs at each site are excluded when calculating CR or HI for that site to ensure more reliable results.

We explore potential emission sources of PM2.5 trace elements at the site with the highest estimated health risks based on measured elemental data. Correlation analysis is conducted first to examine relationships among the elements by calculating nonparametric Spearman’s correlation coefficients. Elements that are used in the health risk assessment or have source specificity are selected to perform correlation analysis. Elements with <50% of samples above MDLs at the specific site are excluded. Principal component analysis (PCA)30 is thereafter applied to qualitatively explore putative emission sources of trace elements by extracting principal components (PCs) that represent most of the variance from normalized elemental data (see details in Text S4).

3. Results and Discussion

3.1. Quality Assurance

Figure S2 provides an example of acceptance testing where mass loadings of all elements on each test filter from six new filter boxes are within acceptance limits, and therefore these new filter boxes are considered “clean” in terms of elemental contamination level. The monthly measurements of a UCD-made multielement RM, NIST SRM 2783, and representative SPARTAN samples within acceptance limits provide an indicator of instrument stability (Figures S3–S5). These repeated measurements of the UCD-made multielement RM over time also demonstrate high long-term precision (relative standard deviation within 6%) and high accuracy (relative bias within ±5%) by comparing with the certified or reference mass loading. The consistent positive or negative bias observed for some elements can be attributed to the use of multiple types of standards in the calibration. SRM 2783 is difficult to keep flat, a requirement for it to receive consistent radiation during each analysis, because its filter membrane does not have a support ring,24 resulting in lower long-term precision than the UCD-made multielement RM.

The field-blank-based MDL is generally higher than the lab-blank-based MDL (Table S7), reflecting the greater range of conditions to which the field blank is exposed. The relative difference between the two MDL estimates is small for most elements but significant for sulfur, which is not yet well understood. About 90–100% of the samples are above MDL for major (crustal) elements except for Mg, while about 10–90% are above MDL for trace elements. The overall uncertainty integrating both additive and proportional uncertainties is reported for each measured elemental concentration on the SPARTAN website. The total proportional uncertainty is estimated to be 4–11% for elements that have at least one reanalyzed sample with a mean >3 × MDL (Table S8). For elements without an estimated σproportion (Cr, Ni, Cu, Cd, Sn, and Sb), σadditive can approximate the overall expected uncertainty for most samples because the concentrations of these elements are usually low. In addition to this “bottom-up” approach, a “top-down” approach using collocated sampling31,32 will be applied to provide more comprehensive estimates of uncertainties when sufficient collocation data are available in the future.

3.2. Dust and TEO Levels

Tables S9 and S10 summarize elemental concentration levels (mean ± standard deviation) in PM2.5 and PM10 samples from the SPARTAN sites, respectively. To understand the magnitude of elemental uncertainties, we use measured elemental concentrations for the Dhaka site as an example and compare standard deviations of the measured concentrations with uncertainties for the means. The uncertainty is much lower than the standard deviation by about one order of magnitude for most elements (Figure S6), indicating that the measurement uncertainty is negligible. For most sites, the uncertainty is significantly lower than the standard deviation for most elements (Table S11).

The mass concentration levels of dust and TEO calculated for PM2.5 and PM10 samples from SPARTAN sites are listed in Tables S2 and S3, respectively. The two tables also provide PM2.5 and PM10 concentrations along with corresponding air quality levels by comparing with the World Health Organization’s annual Air Quality Guideline and Interim Targets.33Figure 1 shows the mean relative contributions of dust and TEO to PM2.5 samples across globally distributed SPARTAN sites. Dust contributes about 20–40% to PM2.5 for sites located in deserts or otherwise impacted by dust events (Abu Dhabi, UAE; Ilorin, Nigeria; Fajardo, Puerto Rico, US; Rehovot, Israel; Haifa, Israel; Addis Ababa, Ethiopia). The Abu Dhabi and Ilorin sites have the highest mean PM2.5-dust concentrations of ∼10 μg/m3 as well as the highest mean PM10-dust concentrations of ∼50 μg/m3. At the Kanpur (India) site, both natural dust (from the neighboring Thar Desert) and anthropogenic dust (e.g., road and agricultural dust)34−36 contribute to a similarly high mean PM2.5-dust concentration (9 μg/m3, 21%) as that observed at desert sites. The Dhaka (Bangladesh) site, located in a humid region, also exhibits a moderately high mean concentration of dust (6 μg/m3) that is likely driven by anthropogenic sources such as road dust or construction dust.37 Nonetheless, the relative dust contribution to PM2.5 in Dhaka is 16%, lower than that of desert sites because of the more significant PM2.5 contribution from non-dust species in Dhaka. The highest mean TEO concentration in PM2.5 is found in Dhaka (6 μg/m3) followed by Hanoi, Vietnam (1 μg/m3), while the TEO concentration at other sites is <1 μg/m3. The relative TEO contribution to PM2.5 varies from <1% at most sites (e.g., Abu Dhabi, Rehovot, and Fajardo) to 4% in Hanoi and reaches up to 16% in Dhaka. Prior analysis of SPARTAN filters for 2013–2019 using ICP-MS similarly identified high trace element concentrations at the same Dhaka and Hanoi sites, but mineral dust concentrations were uncertain due to weak acid digestion and lack of Si measurements, and the oxide forms of trace elements were not considered.19 Our analysis finds that the TEO concentration at Dhaka not only persists but is even higher when oxide forms are included. This high average TEO level at Dhaka is further discussed in Section 3.4.

Figure 1.

Figure 1

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Mass fraction of dust and trace element oxides (TEO) in PM2.5 based on mean concentrations at SPARTAN sites. Inset values are the mean PM2.5 concentrations (μg/m3). Inset asterisks indicate sites that do not have samples from all four seasons.

3.3. Health Risk Assessment

Figure 2 shows the contributions of trace elements (As, Pb, Cd, Co, and Ni) to carcinogenic risk (CR, defined in Section 2.4) for adults across SPARTAN sites, with ≥50% of samples above MDLs for at least three examined trace elements. The highest total CR of 7.7 × 10–5 for adults (about 77 cancer cases per 1000000 adults) occurs in Dhaka followed by Kanpur and Hanoi. The CR caused only by As in Dhaka, Kanpur, and Hanoi exceeds the benchmark38,39 of 1 × 10–5 and is higher than the sum of CR caused by the remaining elements, suggesting a concerning level of atmospheric As at these sites. Estimates based on global simulation from Zhang et al.40 indicate similarly high CR caused by atmospheric As in several regions, including Asian countries such as India and Bangladesh. Previous studies find that As pollution in groundwater is a serious problem in South and Southeast Asia,41−43 while our measured data indicate that exposure to As through inhalation of PM2.5 may also be of concern in these regions. In addition to As, the CR caused by Co or Cd alone also exceeds 1 × 10–5 in Dhaka. As and Co are major contributors to the CR for most examined SPARTAN sites, while Pb contributes significantly to the CR mainly in Dhaka and Ni to the CR in Singapore. Specific CR estimates for each element for both adults and children at different sites are provided in Table S12. The CR value for adults is 4 times as high as that for children because of the difference in exposure duration used for the CR calculation (Text S3). Dhaka is the only site where the combined CR exceeds the benchmark of 1 × 10–5 for both adults and children.

Figure 2.

Figure 2

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Absolute (left) and relative (right) contributions of trace elements to carcinogenic risk (CR) for adults estimated using mean elemental concentrations in PM2.5 samples from SPARTAN sites, with ≥50% of samples above MDLs for at least three examined trace elements. The black dashed line represents the 1 × 10–5 cancer benchmark for adults. Sites are sorted by the total CR. Sites with shading lines do not contain samples from all four seasons.

Figure 3 displays the contributions of trace elements (As, Cd, Co, Ni, Mn, and V) to the hazard index (HI, defined in Section 2.4) across SPARTAN sites with ≥50% of samples above MDLs for at least three examined trace elements. The inhalation reference concentration of Pb is not available to assess its hazard quotient (HQ, defined in Section 2.4). Specific HQ estimates for each element at different sites are provided in Table S13. Dhaka has the highest HI of 6.8, followed by Kanpur, Hanoi, Singapore, Beijing, and Kaohsiung all exceeding the threshold39 of unity. Similar to CR, the non-carcinogenic risk caused by As, Cd, or Mn alone exceeds the benchmark at the Dhaka site, which suggests that multiple large emission sources may exist and have significant adverse health impacts in Dhaka. Kanpur also has a high noncarcinogenic risk caused solely by As exceeding the threshold. The relative contributions of different elements to the HI vary significantly across different sites. The highest absolute and relative contributions of V and Ni to HI are observed at the Singapore site. Given that V and Ni are often used as indicators for residual oil combustion and Singapore Strait has the highest density of emissions originating from shipping globally,44 the high contribution of V and Ni to HI implies a considerable health impact of shipping emissions in Singapore.

Figure 3.

Figure 3

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Absolute (left) and relative (right) contributions of trace elements to the hazard index (HI) estimated using mean elemental concentrations in PM2.5 samples from SPARTAN sites with ≥50% of samples above MDLs for at least three examined trace elements (As, Cd, Co, Ni, Mn, and V). The dashed line represents the threshold HI of 1. Sites are sorted by the total HI. Sites with shading lines do not have samples from all four seasons.

3.4. Investigation into TEO Sources in Dhaka

The high TEO concentration observed at the Dhaka site with the highest CR and HI caused by trace elements across SPARTAN warrants further investigation. The Dhaka site is in a busy area with mixed residential, commercial, and industrial uses, where many emission sources could contribute to its high TEO concentration. The breakdown of oxides of different trace elements in PM2.5 samples from Dhaka indicates oxides of Zn (3.8 μg/m3) and Pb (1.1 μg/m3) are major contributors to this high TEO concentration (Figure S7). This PM2.5-Pb level substantially exceeds the U.S. National Ambient Air Quality Standard (NAAQS) for Pb of 0.15 μg/m3 in total suspended particles as a 3 month average,45 suggesting a significant Pb pollution issue at Dhaka. High concentrations of Zn (6.0 μg/m3) and Pb (5.4 μg/m3) in PM2.5 were also observed at a site located ∼4 km north of the SPARTAN site in Dhaka from December 2012 to February 2013.46

We explore potential emission sources of trace elements in PM2.5 at the Dhaka site. Figure 4 shows correlation coefficients among different elements for PM2.5 samples from the Dhaka site. High positive correlations (r > 0.9, P < 0.0001) are observed among Pb, Sb, Se, and As, and among crustal elements including Al, Si, Ca, Ti, and Fe. The strong correlation of As with Pb, Sb, and Se suggests that the high airborne As level at the Dhaka site is likely primarily driven by anthropogenic sources rather than natural dust sources. Significant correlations (r > 0.8, P < 0.0001) exist among Zn, Cd, and Co, while V is only significantly correlated with Ni (r = 0.6, P < 0.01), suggesting its unique emission source. In contrast to other trace elements, Cr correlates well with crustal elements (r > 0.7, P < 0.0001).

Figure 4.

Figure 4

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Correlation matrix for elemental concentrations of PM2.5 samples from the Dhaka site. Elements are sorted by their atomic mass. The number of asterisks indicates the significance level (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001). The color indicates the correlation coefficient.

The PCA results for the Dhaka site (Table S14) are consistent with the patterns observed in the correlation analysis. Interpretation of each principal component (PC) is provided in Text S5 in the Supporting Information. The PCA results interpreted with existing literature and an understanding of potential sources around the site location indicate that coal-fired brick kiln industries47−49 and unregulated lead-acid battery recycling13,50 likely contribute to the elevated levels of Pb and As found in Dhaka, while traffic51,52 and the growing industries of informal e-waste recycling53,54 may be responsible for the elevated concentrations of Zn, Cd, and Co in Dhaka. Given the lack of surface wind data and a microscale emission inventory, further investigation of potential sources is constrained. More samples from future sampling will help us to better understand trace element levels and the emission sources in Dhaka.

3.5. Outlook

In summary, we described the methodology of PM elemental characterization with X-ray fluorescence for the SPARTAN network. Samples are collected from globally distributed urban cities and analyzed at one central laboratory using consistent protocols, which ensure the comparability of data across different sites. Reference materials that mimic the filter material and mass loadings of typical PM samples are applied to calibrate the instrument. Acceptance testing is conducted to ensure filter quality, and routine quality control measures are implemented to monitor instrument stability. Background levels from both the laboratory and field are considered to calculate method detection limits. Additive and proportional uncertainties are estimated to provide the overall measurement uncertainties. Uncertainty is generally substantially lower than the standard deviation of measured concentrations, indicating that for most sites and elements, measurement uncertainty is negligible. We applied this globally distributed PM elemental dataset to compare concentration levels of dust and trace element oxides (TEO) and health risks caused by hazardous trace elements across the SPARTAN sites. The average dust concentration contributes up to 40% to PM2.5 for sites located in arid regions. The Dhaka site located in a humid region features both a moderate dust level (6 μg/m3, 16%) and the highest TEO level (6 μg/m3, 16%) of PM2.5 contributed mainly by oxides of Zn and Pb. Dhaka has the highest total carcinogenic risk (∼77 cancer cases per 1000000 adults) and the highest hazard index (6.8) exceeding the thresholds. High carcinogenic risk level caused only by As (>1 cancer case per 100000 adults) in Dhaka, Kanpur, and Hanoi suggests significant airborne particulate As pollution. Growing industries of informal recycling of lead-acid batteries and e-waste as well as coal-fired brick kiln industries are identified as likely sources contributing to the elevated concentrations of trace elements (Pb, As, Zn, Cd, and Co) in Dhaka.

More samples representative of all four seasons are needed to better estimate dust and TEO levels across different sites and enable time-series analyses. Both health risk assessments and source apportionment analyses in this work are intended to provide qualitative information about health risk levels and emission sources at SPARTAN sites. As the elemental dataset grows in the future, more complete measurements of exposure concentrations can be used to estimate health risks. Uncertainties exist in the exposure parameters and reference toxicity values used for the risk assessment. The bioavailability of trace elements may be useful to further evaluate health risks.55 Further investigation of the oxidation state of trace elements would better constrain their mass concentrations. Given the sample collection schedule of periodic sampling over 9 days onto a single filter, considerable smearing across factors is likely present in the PCA analysis. In the future, other models such as Positive Matrix Factorization (PMF)56 can be used to perform source apportionment based on the data of all PM components and associated uncertainties at sites applying the MAIA protocol with increased sampling frequency and a larger sample size. Despite the limitations, the concerning health risk level caused by hazardous trace elements observed at the Dhaka, Kanpur, and Hanoi sites demonstrates the need for urgent attention to survey elemental composition to identify regions with elevated trace elements and inform air quality management strategies.

Acknowledgments

This work was supported by the Clean Air Fund and the National Science Foundation (Grant 2020673), with additional contributions from NASA and the US Agency for International Development via the MAIA project at the Jet Propulsion Laboratory, California Institute of Technology. The work of S.H. and D.J.D. was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Y.R. acknowledges support by research grants from the Israel Science Foundation (grant #928/21) and by Horizon Europe Framework Program (EASVOLEE, No. 101095457). We acknowledge technical support from the Sherbrooke site at the Université de Sherbrooke, Département de geomatique appliquée, especially the technical assistance provided by Patrick Menard and support from the site principal investigator Norman T. O’Neill. We acknowledge the technical support provided by Vadim Holodovsky and Tamar Klein at the Haifa site. We are grateful to the dynamic team of numerous SPARTAN site operators for their careful collection of samples. We thank Maya Arnott, Kyla Fung, Michelle Kaibara, Maya Mehrotra, Manasi Pawar, Yuxuan Ren, Guinter Vogg, and Emma Walter for contributing to the laboratory analyses of collected samples.

Data Availability Statement

PM mass and elemental composition data from SPARTAN are freely available at https://www.spartan-network.org/.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsestair.3c00069.

  • Epsilon 4 configuration, definition of acceptance limits in QC criteria, health risk assessment method, PCA method, interpretation of PCA results for the Dhaka site, 9 day sampling period schedule, acceptance testing example, QC plots, comparison between standard deviations and uncertainties for the Dhaka site, breakdown of TEO concentrations at the Dhaka site, location of SPARTAN sites, sampling information, concentrations of PM2.5 and PM10 as well as dust and TEO in PM2.5 and PM10, QC activities, MAL and CF values, reference toxicity values, MDLs and uncertainties, elemental concentrations in PM2.5 and PM10, overall elemental uncertainties of PM2.5 samples, carcinogenic risks and hazard quotients, and PCA results for the Dhaka site (PDF)

The authors declare no competing financial interest.

Supplementary Material

ea3c00069_si_001.pdf (1.2MB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ea3c00069_si_001.pdf (1.2MB, pdf)

Data Availability Statement

PM mass and elemental composition data from SPARTAN are freely available at https://www.spartan-network.org/.

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