Associations of Air Pollution and Pediatric Asthma in Cleveland, Ohio.

Air pollution has been associated with poor health outcomes and continues to be a risk factor for respiratory health in children. While higher particulate matter (PM) levels are associated with increased frequency of symptoms, lower lung function, and increase airway inflammation from asthma, the precise composition of the particles that are more highly associated with poor health outcomes or healthcare utilization are not fully elucidated. PM is measured quantifiably by current air pollution monitoring systems. To better determine sources of PM and speciation of such sources, a particulate matter (PM) source apportionment study, the Cleveland Multiple Air Pollutant Study (CMAPS), was conducted in Cleveland, Ohio, in 2009-2010, which allowed more refined assessment of associations with health outcomes. This article presents an evaluation of short-term (daily) and long-term associations between motor vehicle and industrial air pollution components and pediatric asthma emergency department (ED) visits by evaluating two sets of air quality data with healthcare utilization for pediatric asthma. Exposure estimates were developed using land use regression models for long-term exposures for nitrogen dioxide (NO2) and coarse (i.e., with aerodynamic diameters between 2.5 and 10 µm) particulate matter (PM) and the US EPA Positive Matrix Factorization receptor model for short-term exposures to fine (<2.5 µm) and coarse PM components. Exposure metrics from these two approaches were used in asthma ED visit prevalence and time series analyses to investigate seasonal-averaged short- and long-term impacts of both motor vehicles and industry emissions. Increased pediatric asthma ED visits were found for LUR coarse PM and NO2 estimates, which were primarily contributed by motor vehicles. Consistent, statistically significant associations with pediatric asthma visits were observed, with short-term exposures to components of fine and coarse iron PM associated with steel production. Our study is the first to combine spatial and time series analysis of ED visits for asthma using the same periods and shows that PM related to motor vehicle emissions and iron/steel production are associated with increased pediatric asthma visits.

Signal Decomposition of Conductivity Sensor Measurements on the Allegheny River, Pennsylvania.

Surface water conductivity measurements were used to evaluate the combined contribution of anions in western Pennsylvania from brines discharged by sources such as oil and gas wastewater treatment, coal-fired power plants, and coal mining activities. Conductivity sensor data were collected in the Allegheny River during a US Environmental Protection Agency and US Fish and Wildlife study that included seven sites covering 256 river km during the fall of 2012. Intermittent discharges, such as oil and gas wastewater, and continuous sources contributing to the conductivity were quantified using constrained and adaptive decomposition of time-series (CADETS) frequency analysis. CADETS was able to quantify the intermittent or short-term component of conductivity at sites where the intermittent fraction was 1 to 22% of the total conductivity. The demonstrated efficacy of the CADETS method for surface water quality analysis suggests it could be widely used to evaluate other water sensor data in rivers with both continuous and intermittent source impacts.

Single-particle SEM-EDX analysis of iron-containing coarse particulate matter in an urban environment: sources and distribution of iron within Cleveland, Ohio.

The physicochemical properties of coarse-mode, iron-containing particles and their temporal and spatial distributions are poorly understood. Single-particle analysis combining X-ray elemental mapping and computer-controlled scanning electron microscopy (CCSEM-EDX) of passively collected particles was used to investigate the physicochemical properties of iron-containing particles in Cleveland, OH, in summer 2008 (Aug-Sept), summer 2009 (July-Aug), and winter 2010 (Feb-March). The most abundant classes of iron-containing particles were iron oxide fly ash, mineral dust, NaCl-containing agglomerates (likely from road salt), and Ca-S containing agglomerates (likely from slag, a byproduct of steel production, or gypsum in road salt). The mass concentrations of anthropogenic fly ash particles were highest in the Flats region (downtown) and decreased with distance away from this region. The concentrations of fly ash in the Flats region were consistent with interannual changes in steel production. These particles were observed to be highly spherical in the Flats region, but less so after transport away from downtown. This change in morphology may be attributed to atmospheric processing. Overall, this work demonstrates that the method of passive collection with single-particle analysis by electron microscopy is a powerful tool to study spatial and temporal gradients in components of coarse particles. These gradients may correlate with human health effects associated with exposure to coarse-mode particulate matter.

Scanning electron microanalysis and analytical challenges of mapping elements in urban atmospheric particles.

Elemental mapping with energy-dispersive X-ray spectroscopy (EDX) associated with scanning electron microscopy is highly useful for studying internally mixed atmospheric particles. Presented is a study of individual particles from urban airsheds and the analytical challenges in qualitatively determining the composition and origin of heterogeneous urban-air particles from high-resolution elemental maps. Coarse-mode particles were taken from samples collected in three U.S. cities: Atlanta, Los Angeles, and Seattle. Elemental maps distinguished particles with heterogeneously mixed phases from those with homogeneously mixed phases that also contained inclusions or surface adducts. Elemental mapping at low and high beam energies, along with imaging at an oblique angle helped to classify particles by origin. The impact of particle shape on X-ray microanalysis was demonstrated by having the beam enter the particle at ≥ 52° from normal. Potential misinterpretations of particle composition due to artifacts in the elemental maps were minimized by tilt imaging to reveal particle surface roughness and depth, mapping at low beam energies, noting the position of the EDX detector in the map field, and assessing differences in the mass absorption coefficients of the particle's major elements to anticipate X-ray self-absorption.

Application of EPA unmix and nonparametric wind regression on high time resolution trace elements and speciated mercury in Tampa, Florida aerosol.

Intensive ambient air sampling was conducted in Tampa, FL, during October and November of 2002. Fine particulate matter (PM(2.5)) was collected at 30 min resolution using the Semicontinuous Elements in Aerosol Sampler II (SEAS-II) and analyzed off-line for up to 45 trace elements by high-resolution ICPMS (HR-ICPMS). Divalent reactive gaseous mercury and particulate bound mercury were also measured semicontinuously (2 h). Application of the United States Environmental Protection Agency's (EPA) Unmix receptor model on the 30 min resolution trace metals data set identified eight possible sources: residual oil combustion, lead recycling, coal combustion, a Cd-rich source, biomass burning, marine aerosol, general industrial, and coarse dust contamination. The source contribution estimates from EPA Unmix were then run in a nonparametric wind regression (NWR) model, which convincingly identified plausible source origins. When the 30 min ambient concentrations of trace elements were time integrated (2 h) and combined with speciated mercury concentrations, the model identified only four sources, some of which appeared to be merged source profiles that were identified as separate sources by using the 30 min resolution data. This work demonstrates that source signatures that can be captured at 30 min resolution may be lost when sampling for longer durations.

Primary Sources of Polycyclic Aromatic Hydrocarbons to Streambed Sediment in Great Lakes Tributaries Using Multiple Lines of Evidence.

Polycyclic aromatic hydrocarbons (PAHs) are among the most widespread and potentially toxic contaminants in Great Lakes (USA/Canada) tributaries. The sources of PAHs are numerous and diverse, and identifying the primary source(s) can be difficult. The present study used multiple lines of evidence to determine the likely sources of PAHs to surficial streambed sediments at 71 locations across 26 Great Lakes Basin watersheds. Profile correlations, principal component analysis, positive matrix factorization source-receptor modeling, and mass fractions analysis were used to identify potential PAH sources, and land-use analysis was used to relate streambed sediment PAH concentrations to different land uses. Based on the common conclusion of these analyses, coal-tar-sealed pavement was the most likely source of PAHs to the majority of the locations sampled. The potential PAH-related toxicity of streambed sediments to aquatic organisms was assessed by comparison of concentrations with sediment quality guidelines. The sum concentration of 16 US Environmental Protection Agency priority pollutant PAHs was 7.4-196 000 µg/kg, and the median was 2600 µg/kg. The threshold effect concentration was exceeded at 62% of sampling locations, and the probable effect concentration or the equilibrium partitioning sediment benchmark was exceeded at 41% of sampling locations. These results have important implications for watershed managers tasked with protecting and remediating aquatic habitats in the Great Lakes Basin. Environ Toxicol Chem 2020;39:1392-1408. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Impact of mine waste on airborne respirable particulates in northeastern Oklahoma, United States.

Atmospheric dispersion of particles from mine waste is potentially an important route of human exposure to metals in communities close to active and abandoned mining areas. This study assessed sources of mass and metal concentrations in two size fractions of respirable particles using positive matrix factorization (U.S. Environmental Protection Agency [EPA] PMF 3.0). Weekly integrated samples of particulate matter (PM) 10 microm in aerodynamic diameter or less (PM10) and fine PM (PM2.5, or PM <2.5 microm in aerodynamic diameter) were collected at three monitoring sites, varying distances (0.5-20 km) from mine waste piles, for 58 consecutive weeks in a former lead (Pb) and zinc (Zn) mining region. Mean mass concentrations varied significantly across sites for coarse PM (PM10-PM2.5) but not PM2.5 particles. Concentrations of Pb and Zn significantly decreased with increasing distance from the mine waste piles in PM10-PM2.5 (P < 0.0001) and PM2.5 (P < 0.0005) fractions. Source apportionment analyses deduced five sources contributing to PM2.5 (mobile source combustion, secondary sulfates, mine waste, crustal/soil, and a source rich in calcium [Ca]) and three sources for the coarse fraction (mine waste, crustal/soil, and a Ca-rich source). In the PM2.5 fraction, mine waste contributed 1-6% of the overall mass, 40% of Pb, and 63% of Zn. Mine waste impacts were more apparent in the PM10-PM2.5 fraction and contributed 4-39% of total mass, 88% of Pb, and 97% of Zn. Percent contribution of mine waste varied significantly across sites (P < 0.0001) for both size fractions, with highest contributions in the site closest to the mine waste piles. Seasonality, wind direction, and concentrations of the Ca-rich source were also associated with levels of ambient aerosols from the mine waste source. Scanning electron microscopy results indicated that the PMF-identified mine waste source is mainly composed of Zn-Pb agglomerates on crustal particles in the PM10-PM2.5 fraction. In conclusion, the differential impacts of mine waste on respirable particles by size fraction and location should be considered in future exposure evaluations.

Unmix Optimum analysis of PAH sediment sources.

Unmix Optimum (UnmixO) was developed to analyze data, such as sediment PAH data, that were resistant to existing methods of multivariate analysis. Using a geometrical approach, UnmixO uses multiple advanced nonlinear optimization algorithms to find potential sources that obey non-negativity constraints while optimally fitting the data. UnmixO does not require specific knowledge of the uncertainties in the data and will work better for smaller data sets than other multivariate models. UnmixO was able to identify polycyclic aromatic hydrocarbon (PAH) contaminant sources contributing to sediment samples based on sample composition data with good diagnostic values. Results were compared to published EPA Chemical Mass Balance (CMB) sediment results from Lady Bird Lake (LBL) Austin, TX and 40 lakes (40LKS) across the U.S. A Chi-sum approach determined which UnmixO source profile best matched profiles used in CMB sediment studies; two coal tar (CT) sealcoat sources and a mixed combustion source contributed to the sediment PAHs. These results were consistent with CMB results for the LBL and 40LKS studies that estimated CT sealcoats contribute over 80% of PAHs to urban lakes. UnmixO results also showed that CT sealant's contribution to sediments decreased after the City of Austin ban in 2006.

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in small craft harbor (SCH) surficial sediments in Nova Scotia, Canada.

Multiple source apportionment approaches were employed to investigate PAH sources which contribute to small craft harbor (SCH) sediments in Nova Scotia (NS), Canada. A total of 580 sediment samples were analyzed using PAH diagnostic ratios, Unmix Optimum receptor modeling, and by assessment of the composition of the PAH profile. PAH diagnostic ratios suggest PAHs are primarily of pyrogenic (thermal) origin, while UnmixO modeling identifies four individual sources which best describe surficial sediments and suggests contributions from both pyrogenic and petrogenic origins. These include coal combustion, automobile exhaust, and biomass incineration. PAH profile assessment determined an overwhelming contribution of high molecular weight PAHs, which exhibited a strong correlation with total PAH concentrations.

Source apportionment of particulate matter in the U.S. and associations with lung inflammatory markers.

Size-fractionated particulate matter (PM) samples were collected from six U.S. cities and chemically analyzed as part of the Multiple Air Pollutant Study. Particles were administered to cultured lung cells and the production of three different proinflammatory markers was measured to explore the association between the health effect markers and PM. Ultrafine, fine, and coarse PM samples were collected between December 2003 and May 2004 over a 4-wk period in each city. Filters were pooled for each city and the PM samples were extracted then analyzed for trace metals, ions, and elemental carbon. Particle extracts were applied to cultured human primary airway epithelial cells, and the secreted levels of interleukin-8 (IL-8), heme oxygenase-1, and cyclooxygenase-2 were measured 1 and 24 h following exposure. Fine PM sources were quantified by the chemical mass balance (CMB) model. The relationship between toxicological measures, PM sources, and individual species were evaluated using linear regression. Ultrafine and fine PM mass were associated with increases in IL-8 (r(2) = .80 for ultrafine and r(2) = .52 for fine). Sources of fine PM and their relative contributions varied across the sampling sites and a strong linear association was observed between IL-8 and secondary sulfate from coal combustion (r(2) = .79). Ultrafine vanadium, lead, copper, and sulfate were also associated with increases in IL-8. Increases in inflammatory markers were not observed for coarse PM mass and source markers. These findings suggest that certain PM size fractions and sources are associated with markers of lung injury or inflammation.

The impact of commercially treated oil and gas produced water discharges on bromide concentrations and modeled brominated trihalomethane disinfection byproducts at two downstream municipal drinking water plants in the upper Allegheny River, Pennsylvania, USA.

In 2010, a dramatic increase in the levels of total trihalomethane (THM) and the relative proportion of brominated species was observed in finished water at several Pennsylvania water utilities (PDW) using the Allegheny River as their raw water supply. An increase in bromide (Br(-)) concentrations in the Allegheny River was implicated to be the cause of the elevated water disinfection byproducts. This study focused on quantifying the contribution of Br(-) from a commercial wastewater treatment facility (CWTF) that solely treats wastes from oil and gas producers and discharges into the upper reaches of the Allegheny River, and impacts on two downstream PDWs. In 2012, automated daily integrated samples were collected on the Allegheny River at six sites during three seasonal two-week sampling campaigns to characterize Br(-) concentrations and river dispersion characteristics during periods of high and low river discharges. The CWTF discharges resulted in significant increases in Br(-) compared to upstream baseline values in PDW raw drinking water intakes during periods of low river discharge. During high river discharge, the assimilative dilution capacity of the river resulted in lower absolute halide concentrations, but significant elevations Br(-) concentrations were still observed at the nearest downstream PDW intake over baseline river levels. On days with active CWTF effluent discharge the magnitude of bromide impact increased by 39 ppb (53%) and 7 ppb (22%) for low and high river discharge campaigns, respectively. Despite a declining trend in Allegheny River Br(-) (2009-2014), significant impacts from CWTF and coal-fired power plant discharges to Br(-) concentrations during the low river discharge regime at downstream PDW intakes was observed, resulting in small modeled increases in total THM (3%), and estimated positive shifts (41-47%) to more toxic brominated THM analogs. The lack of available coincident measurements of THM, precursors, and physical parameters limited the interpretation of historical trends.

Chemical composition and source apportionment of size fractionated particulate matter in Cleveland, Ohio, USA.

The Cleveland airshed comprises a complex mixture of industrial source emissions that contribute to periods of non-attainment for fine particulate matter (PM2.5) and are associated with increased adverse health outcomes in the exposed population. Specific PM sources responsible for health effects however are not fully understood. Size-fractionated PM (coarse, fine, and ultrafine) samples were collected using a ChemVol sampler at an urban site (G.T. Craig (GTC)) and rural site (Chippewa Lake (CLM)) from July 2009 to June 2010, and then chemically analyzed. The resulting speciated PM data were apportioned by EPA positive matrix factorization to identify emission sources for each size fraction and location. For comparisons with the ChemVol results, PM samples were also collected with sequential dichotomous and passive samplers, and evaluated for source contributions to each sampling site. The ChemVol results showed that annual average concentrations of PM, elemental carbon, and inorganic elements in the coarse fraction at GTC were ∼2, ∼7, and ∼3 times higher than those at CLM, respectively, while the smaller size fractions at both sites showed similar annual average concentrations. Seasonal variations of secondary aerosols (e.g., high NO3- level in winter and high SO42- level in summer) were observed at both sites. Source apportionment results demonstrated that the PM samples at GTC and CLM were enriched with local industrial sources (e.g., steel plant and coal-fired power plant) but their contributions were influenced by meteorological conditions and the emission source's operation conditions. Taken together the year-long PM collection and data analysis provides valuable insights into the characteristics and sources of PM impacting the Cleveland airshed in both the urban center and the rural upwind background locations. These data will be used to classify the PM samples for toxicology studies to determine which PM sources, species, and size fractions are of greatest health concern.

Methods for estimating uncertainty in PMF solutions: examples with ambient air and water quality data and guidance on reporting PMF results.

The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. To demonstrate the utility of the EE methods, results are presented for three data sets: (1) speciated PM2.5 data from a chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species in water quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high-resolution aerosol mass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results from sensitivity tests of EE diagnostics using additional and fewer factors, and recommendations for reporting PMF results. BS-DISP and BS are found useful in understanding the uncertainty of factor profiles; they also suggest if the data are over-fitted by specifying too many factors. DISP diagnostics were consistently robust, indicating its use for understanding rotational uncertainty and as a first step in assessing a solution's viability. The uncertainty of each factor's identifying species is shown to be a useful gauge for evaluating multiple solutions, e.g., with a different number of factors.

Application of ICP-OES for evaluating energy extraction and production wastewater discharge impacts on surface waters in Western Pennsylvania.

Oil and gas extraction and coal-fired electrical power generating stations produce wastewaters that are treated and discharged to rivers in Western Pennsylvania with public drinking water system (PDWS) intakes. Inductively coupled plasma optical emission spectroscopy (ICP-OES) was used to quantify inorganic species in wastewater and river samples using a method based on EPA Method 200.7 rev4.4. A total of 53 emission lines from 30 elements (Al, As, B, Ba, Ca, Cd, Ce, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, S, Sb, Se, Si, Sn, Sr, Ti, Tl, V, and Zn) were investigated. Samples were prepared by microwave-assisted acid digestion using a mixture of 2% HNO3 and 0.5% HCl. Lower interferences and better detection characteristics resulted in selection of alternative wavelengths for Al, As, Sb, Mg, Mo, and Na. Radial view measurements offered accurate determinations of Al, Ba, K, Li, Na, and Sr in high-brine samples. Spike recovery studies and analyses of reference materials showed 80-105% recoveries for most analytes. This method was used to quantify species in samples with high to low brine concentrations with method detection limits a factor of 2 below the maximum contaminant limit concentrations of national drinking water standards. Elements B, Ca, K, Li, Mg, Na, and Sr were identified as potential tracers for the sources impacting PDWS intakes. Usability of the ICP-OES derived data for factor analytic model applications was also demonstrated.

Source apportionment of Phoenix PM2.5 aerosol with the Unmix receptor model.

The multivariate receptor model Unmix has been used to analyze a 3-yr PM2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories:gasoline engines (33 +/- 4%), diesel engines (16 +/- 2%), secondary SO4(2-) (19 +/- 2%), crustal/soil (22 +/- 2%), and vegetative burning (10 +/- 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.

Field method comparison between passive air samplers and continuous monitors for Vocs and NO2 in El Paso, Texas.

This study evaluates the performance of Model 3300 Ogawa Passive Nitrogen Dioxide (NO2) Samplers and 3M 3520 Organic Vapor Monitors (OVMs) by comparing integrated passive sampling concentrations to averaged hourly NO2 and volatile organic compound (VOC) measurements at two sites in El Paso, TX. Sampling periods were three time intervals (3-day weekend, 4-day weekday, and 7-day weekly) for three consecutive weeks. OVM concentrations were corrected for ambient pressure to account for higher elevation. Precise results (< 5% relative standard deviation, RSD) were found for NO2 measurements from collocated Ogawa samplers. Reproducibility was lower from duplicate OVMs for BTEX (benzene, toluene, ethylbenzene, and xylene isomers) VOCs (> or = 77% RSD for 2-day samples) with better precision for longer sampling periods. Comparison of Ogawa NO2 samplers with chemiluminescence measurements averaged over the same time period suggested potential calibration problems with the chemiluminescence analyzer. For BTEX species, generally good agreement was obtained between OVMs and automated-gas chromatograph (auto-GC) measurements. The OVMs successfully tracked increasing levels of VOCs recorded by the auto-GCs. However, except for toluene, OVM BTEX measurements generally exceeded their continuous counterparts with a mean bias of 5-10%. Although interpretation of the study results was limited due to small sample sizes, diffusion barrier influences caused by shelters that housed OVMs and differences in sampling heights between OVMs and auto-GC inlet may explain the overestimation.

Source apportionment of ambient fine particulate matter in Dearborn, Michigan, using hourly resolved PM chemical composition data.

High time-resolution aerosol sampling was conducted for one month during July-August 2007 in Dearborn, MI, a non-attainment area for fine particulate matter (PM2.5) National Ambient Air Quality Standards (NAAQS). Measurements of more than 30 PM2.5 species were made using a suite of semi-continuous sampling and monitoring instruments. Dynamic variations in the sub-hourly concentrations of source 'marker' elements were observed when discrete plumes from local sources impacted the sampling site. Hourly averaged PM2.5 composition data for 639 samples were used to identify and apportion PM2.5 emission sources using the multivariate receptor modeling techniques EPA Positive Matrix Factorization (PMF) v4.2 and EPA Unmix v6.0. Source contribution estimates from PMF and Unmix were then evaluated using the Sustained Wind Instance Method (SWIM), which identified plausible source origins. Ten sources were identified by both PMF and Unmix: (1) secondary sulfate, (2) secondary nitrate characterized by a significant diurnal trend, (3) iron and steel production, (4) a potassium-rich factor attributable to iron/steel slag waste processing, (5) a cadmium-rich factor attributable to incineration, (6) an oil refinery characterized by La/Ce>1 specific to south wind, (7) oil combustion, (8) coal combustion, (9) motor vehicles, and (10) road dust enriched with organic carbon. While both models apportioned secondary sulfate, oil refinery, and oil combustion PM2.5 masses closely, the mobile and industrial source apportionments differed. Analyses were also carried out to help infer time-of-day variations in the contributions of local sources.

Bayesian hierarchical modeling of cardiac response to particulate matter exposure.

Studies have linked increased levels of particulate air pollution to decreased autonomic control, as measured by heart rate variability (HRV), particularly in susceptible populations such as the elderly. In this study, we use data obtained from the 1998 USEPA epidemiology-exposure longitudinal panel study of elderly adults in a Baltimore retirement home to examine the relationship between HRV and PM2.5 personal exposure. We consider PM2.5 personal exposure in the aggregate and personal exposure to the components of PM2.5 as estimated in two ways using receptor models. We develop a Bayesian hierarchical model for HRV as a function of personal exposure to PM2.5, which integrates HRV measurements and data obtained from personal, indoor and outdoor PM2.5 monitoring and meteorological data. We found a strong relationship between decreased HRV (HF, LF, r-MSSD and SDNN) and total personal exposure to PM2.5 at a lag of 1 day. Using personal exposure monitoring (PEM) apportionment results, we examined the relative importance of ambient and non-ambient personal PM2.5 exposure to HRV and found the effect of internal non-ambient sources of PM2.5 on HRV to be minimal. Using the PEM apportionment data, a consistent effect of soil at short time scales (lag 0) was found across all five HRV measures, and an effect of sulfate on HRV was seen for HF and r-MSSD at the moving average of lags 0 and 1 days. Modeling of ambient site apportionment data indicated effects of nitrate on HRV at lags of 1 day, and moving averages of days 0 and 1 and days 0-2 for all but the ratio LF/HF. Sulfate had an effect on HRV at a lag of 1 day for four HRV measures (HF, LF, r-MSSD, SDNN) and for LF/HF at a moving average of days 0-2.