A nonlinear, robust station–season index for quantifying fine–coarse mixture regimes in particulate matter

Abstract

The objective of this study is to develop and evaluate an interpretable, robust station–season index that characterizes fine–coarse particulate-matter mixture regimes using routinely available regulatory monitoring data. Using EPA AQS daily PM2.5 and PM10 observations (2022–2024), we computed the daily fine fraction4 f(t) = PM2.5(t)/PM10(t) and coarse mass PMc(t) = max{PM10(t)−PM2.5(t),0}. Each station–season–year was summarized by robust statistics: the median fine fraction fmed, median PM2.5 PM2.5,med, and a coarse-tail burstiness metric Bc = log(1 + PMc,p90)−log(1 + PMc,med). We then defined a nonlinear robust screening index, SNLR = [log(1 + PM_{2.5,med})]^γ x [f_{med}]^α exp(−β Bc), with fixed parameters α= 0.55, β = 1.6, and γ = 1.2, designed to increase with typical fine-dominant exposure and decrease with stronger coarse-tail influence. Analyses used n= 240 stations with consistent multi-year coverage, stratified by location setting (Suburban; Urban and center city) and land use (Residential; Commercial), with group differences summarized by median contrasts and nonparametric bootstrap confidence intervals. Seasonality dominates: SNLR is lowest in spring (MAM) and highest in summer (JJA) across settings. Land-use contrasts are generally modest, although in DJF within Urban and center city, Residential stations show higher SNLR than Commercial stations. Overall, SNLR provides an interpretable, robustness-oriented tool for comparing fine–coarse mixture regimes using widely available monitoring data, rather than for source attribution or health-outcome inference.