Primary Sulfate Is the Dominant Source of Particulate Sulfate during Winter in Fairbanks, Alaska

Abstract

Within and surrounding high-latitude cities, poor air quality disturbs Arctic ecosystems, influences the climate, and harms human health. The Fairbanks North Star Borough has wintertime particulate matter (PM) concentrations that exceed the Environmental Protection Agency's (EPA) threshold for public health. Particulate sulfate (SO₄ ^2-) is the most abundant inorganic species and contributes approximately 20% of the total PM mass in Fairbanks, but air quality models underestimate observed sulfate concentrations. Here we quantify sulfate sources using size-resolved δ³⁴S(SO₄ ^2-), δ¹⁸O(SO₄ ^2-), and Δ¹⁷O(SO₄ ^2-) of particulate sulfate in Fairbanks from January 18th to February 25th, 2022 using a Bayesian isotope mixing model. Primary sulfate contributes 62 ± 12% of the total sulfate mass on average. Most primary sulfate is found in the size bin with a particle diameter < 0.7 μm, which contains 90 ±5% of total sulfate mass and poses the greatest risk to human health. Oxidation by all secondary formation pathways combined contributes 38 ± 12% of total sulfate mass on average, indicating that secondary sulfate formation is inefficient in this cold, dark environment. On average, the dominant secondary sulfate formation pathways are oxidation by H₂O₂ (13 ± 6%), O₃ (8 ± 4%), and NO₂ (8 ± 3%). These findings will inform mitigation strategies to improve air quality and public health in Fairbanks and possibly other high-latitude urban areas during winter.

Figure 1

Time series of sulfur species concentrations (a), δ¹⁸O(b), δ¹⁸O (c), and δ¹⁸O (d) measurements. (a) Ambient concentrations of sulfur species including SO₄^2– (gold), non-HMS S(IV) (blue), and HMS (magenta). The SOR for each sample is plotted with a black dashed line. Isotope observations in (b)–(d) are divided into three size bins: PM_0.7 (gold squares), PM_0.7–2.5 (narrow pink diamonds), and PM_2.5–10 (wide blue diamonds). The error bars represent the propagated errors for each measurement. Daily PM_0.7–2.5 and PM_2.5–10 samples were combined into 10 periods as indicated by the vertical gridlines. A 2 week average of isotopic composition at Poker Flat is shown with gray shading in (b)–(d). The measured δ³⁴S source signature for fuel oil is shown in blue in (d).

Figure 2

Regressions of (a) Δ¹⁷O vs δ¹⁸O, (b) Δ¹⁷O vs δ³⁴S, and (c) δ¹⁸O vs δ³⁴S, where the solid black line is the linear least-squares regression line. The three size bins are depicted by the shape of the marker, as defined in the legend. The color bar shows the sulfur oxidation ratio (SOR) for each sample. Poker Flat measurements are depicted with black triangles. The isotopic composition of fuel oil is shown by a blue line. The gray shaded region shows the full possible range of δ¹⁸O, Δ¹⁷O, and δ³⁴S source signatures with the average source signature for each pathway plotted as a black star.

Figure 3

Time series of the estimated contributions of primary sulfate (navy) and secondary sulfate formation via the NO₂ (green), O₃ (gold), OH (orange), TMI-O₂ (light blue), and H₂O₂ (pink) pathways. Mass concentrations and average fractional contributions for PM_0.7 sulfate are presented in (a) and (b), respectively. Likewise, (c) and (d) show mass concentrations and fractional contributions of PM_0.7–2.5 and PM_2.5–10 sulfate combined as PM_>0.7 μm. The line graphs (a and c) show the estimated mass concentration for each sulfate formation pathway, and the shading represents the 95% confidence interval. The difference in scale for the y-axis for (a) and (c) should be noted. The bar charts (b and d) summarize the median fraction for each pathway and period during the campaign. “Day” and “Pol. Night” correspond to the daytime and nighttime samples collected during the ultrapolluted period between January 30th and February 2nd.