Contributions of non-tailpipe emissions to near-road PM2.5 and PM10: A chemical mass balance study

As the importance of non-tailpipe particles (NTP) over tailpipe emissions from urban traffic has been increasing, there is a need to evaluate NTP contributions to ambient particulate matter (PM) using representative source profiles. The Brake and Tire Wear Study conducted in Los Angeles, California in the winter of 2020 collected 64 PM2.5 and 64 PM10 samples from 32 pairs of downwind-upwind measurements at two near-road locations (I-5 in Anaheim and I-710 in Long Beach). These samples were characterized for inorganic and organic markers and, along with locally-developed brake wear, tire wear, and road dust source profiles, subject to source apportionment using the effective-variance chemical mass balance (EV-CMB) model. Model results highlighted the dominance of resuspended dust in both PM2.5 (23-33%) and PM10 (32-53%). Brake and tire wear contributed more to PM2.5 than tailpipe exhausts (diesel + gasoline) for I-5 (29-30% vs. 19-21%) while they were comparable for I-710 (15-17% vs. 15-19%). For PM10, the brake and tire wear contributions were 2-3 times the exhaust contributions. Different fleet compositions on and near I-5 and I-710 appeared to influence the relative importance of NTP and exhaust sources. The downwind-upwind differences in source contributions were often insignificant, consistent with small and/or nearly equal impacts of adjacent highway traffic emissions on the downwind and upwind sites. The utility of sole markers, such as barium and zinc, to predict brake and tire wear abundances in ambient PM is evaluated.

Evidence of non-tailpipe emission contributions to PM2.5 and PM10 near southern California highways

Particulate Matter (PM) concentrations near highways are influenced by vehicle tailpipe and non-tailpipe emissions, other emission sources, and urban background aerosols. This study collected PM_2.5 and PM₁₀ filter samples near two southern California highways (I-5 and I-710) over two weeks in winter 2020. Samples were analyzed for chemical source markers. Mean PM_2.5 and PM₁₀ concentrations were approximately 10-15 and 30 μg/m³, respectively. Organic matter, mineral dust, and elemental carbon (EC) were the most abundant PM components. EC and polycyclic aromatic hydrocarbons at I-710 were 19-26% and 47% higher than those at the I-5 sites, respectively, likely due to a larger proportion of diesel vehicles. High correlations were found for elements with common sources, such as markers for brake wear (e.g., Fe, Ba, Cu, and Zr) and road dust (e.g., Al, Si, Ca, and Mn). Based on rubber abundances, the contributions of tire tread particles to PM_2.5 and PM₁₀ mass were approximately 8.0% at I-5 and 5.5% at I-710. Two different tire brands showed significantly different Si, Zn, carbon, and natural rubber abundances.

Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis

BACKGROUND

The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory.

OBJECTIVE

The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States.

METHODS

This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation.

RESULTS

Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States.

SIGNIFICANCE

The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.

Nonuniform impacts of COVID-19 lockdown on air quality over the United States

Most of the state governments in United States (U.S.) issued lockdown or business restrictions amid the COVID-19 pandemic in March 2020, which created a unique opportunity to evaluate the air quality response to reduced economic activities. Data acquired from 28 long-term air quality stations across the U.S. revealed widespread but nonuniform reductions of nitrogen dioxide (NO₂) and carbon monoxide (CO) during the first phase of lockdown (March 15-April 25, 2020) relative to a pre-lockdown reference period and historical baselines established in 2017-2019. The reductions, up to 49% for NO₂ and 37% for CO, are statistically significant at two thirds of the sites and tend to increase with local population density. Significant reductions of particulate matter (PM_2.5 and PM₁₀) only occurred in the Northeast and California/Nevada metropolises where NO₂ declined the most, while the changes in ozone (O₃) were mixed and relatively minor. These findings are consistent with lower transportation and utility demands that dominate NO₂ and CO emissions, especially in major urban areas, due to the lockdown. This study provides an insight into potential public health benefits with more aggressive air quality management, which should be factored into strategies to reopen the U.S. and global economy.

PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model

A hybrid environmental receptor model (HERM) that unifies the theory of effective-variance chemical mass balance (EV-CMB) and positive matrix factorization (PMF) models was developed to support the weight-of-evidence approach of air pollution source apportionment. The HERM software is capable of (1) conducting EV-CMB analysis for multiple samples in a single model run; (2) calculating EV-CMB and PMF source contributions, as well as middle grounds between the two (i.e., hybrid mode), using partial source information available for the study region; (3) reporting source contribution uncertainties and sample-/species-specific fitting performance measures; and (4) interfacing with MS Excel for convenient data inputs/outputs and analysis. Initial testing with simulated and real-world PM_2.5 (fine particulate air pollutants with aerodynamic diameter <2.5 μm) data sets show that HERM reproduces EV-CMB results from existing software but with more tolerance to collinearity and better uncertainty estimates. It also shows that partial source information helps reduce rotational ambiguity in PMF, thus producing more accurate partitioning between highly correlated sources. Moreover, source profiles generated from the hybrid mode can be more representative of the study region than those acquired from other locales or calculated by PMF with no source information. Strategies to use HERM for source apportionment are recommended.

Separation of brown carbon from black carbon for IMPROVE and Chemical Speciation Network PM2.5 samples

The replacement of the Desert Research Institute (DRI) model 2001 with model 2015 thermal/optical analyzers (TOAs) results in continuity of the long-term organic carbon (OC) and elemental carbon (EC) database, and it adds optical information with no additional carbon analysis effort. The value of multiwavelength light attenuation is that light absorption due to black carbon (BC) can be separated from that of brown carbon (BrC), with subsequent attribution to known sources such as biomass burning and secondary organic aerosols. There is evidence of filter loading effects for the 25% of all samples with the highest EC concentrations based on the ratio of light attenuation to EC. Loading corrections similar to those used for the seven-wavelength aethalometer need to be investigated. On average, nonurban Interagency Monitoring of PROtected Visual Environments (IMPROVE) samples show higher BrC fractions of short-wavelength absorption than urban Chemical Speciation Network (CSN) samples, owing to greater influence from biomass burning and aged aerosols, as well as to higher primary BC contributions from engine exhaust at urban sites. Sequential samples taken during an Everglades National Park wildfire demonstrate the evolution from flaming to smoldering combustion, with the BrC fraction increasing as smoldering begins to dominate the fire event.

IMPLICATIONS

The inclusion of seven wavelengths in thermal/optical carbon analysis of speciated PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) samples allows contributions from biomass burning and secondary organic aerosols to be estimated. This separation is useful for evaluating control strategy effectiveness, identifying exceptional events, and determining natural visibility conditions.

Atmospheric deposition of particles at a sensitive alpine lake: Size-segregated daily and annual fluxes from passive sampling techniques

Lake Tahoe, a North American alpine lake long appreciated for its clear water and geographic setting, has experienced a trend of declining water clarity due to increasing nutrient and particle inputs. Contributions from atmospheric deposition of particulate matter (PM) could be important, yet they are inadequately quantified. This study established a yearlong deposition monitoring network in the northern Lake Tahoe Basin. Dry deposition was quantified on surrogate surfaces while wet deposition was based on particles suspended in precipitation at 24-hour resolution. The particle size ranges by these passive techniques were 1-64μm and 0.5-20μm in diameter for dry and wet deposition, respectively. Dry deposition of submicrometer (0.5-1μm) particles was also estimated by extrapolation of a lognormal size distribution. Higher daily number deposition fluxes (NDF_dry and NDF_wet) were found at a near-shore site, confirming substantial impacts of commercial and tourist activities. The two more isolated sites indicated a uniform regional background. On average, daily NDF_dry is about one order of magnitude lower than daily NDF_wet. Dry deposition velocities increased rapidly with particle size, as evidenced by collocated measurements of NDF_dry and ambient particle number concentrations, though it seems less so for wet deposition due to different scavenging mechanisms. Despite fewer "wet" days than "dry" days during the monitoring period, wet processes dominated seasonal particle deposition, particularly in winter and spring when most precipitation occurred. Adopting sediment (insoluble, inorganic) particle fraction estimates from the literature, this study reports an annual particle flux of 2.9-5.2×10¹⁰#m^-2yr^-1 for sediment particles with 1-20μm diameter and 6.1-11×10¹⁰#m^-2yr^-1 for those with 0.5-20μm diameter. Implications of these findings to the current knowledge of atmospheric deposition in the Lake Tahoe Total Maximum Daily Load (TMDL) are discussed.

PM2.5 source apportionment with organic markers in the Southeastern Aerosol Research and Characterization (SEARCH) study

Positive matrix factorization (PMF) and effective variance (EV) solutions to the chemical mass balance (CMB) were applied to PM(2.5) (particulate matter with an aerodynamic diameter <2.5 μm) mass and chemically speciated measurements for samples taken from 2008 to 2010 at the Atlanta, Georgia, and Birmingham, Alabama, sites. Commonly measured PM(2.5) mass, elemental, ionic, and thermal carbon fraction concentrations were supplemented with detailed nonpolar organic speciation by thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS). Source contribution estimates were calculated for motor vehicle exhaust, biomass burning, cooking, coal-fired power plants, road dust, vegetative detritus, and secondary sulfates and nitrates for Atlanta. Similar sources were found for Birmingham, with the addition of an industrial source and the separation of biomass burning into open burning and residential wood combustion. EV-CMB results based on conventional species were qualitatively similar to those estimated by PMF-CMB. Secondary ammonium sulfate was the largest contributor, accounting for 27-38% of PM(2.5), followed by biomass burning (21-24%) and motor vehicle exhaust (9-24%) at both sites, with 4-6% of PM(2.5) attributed to coal-fired power plants by EV-CMB. Including organic compounds in the EV-CMB reduced the motor vehicle exhaust and biomass burning contributions at both sites, with a 13-23% deficit for PM(2.5) mass. The PMF-CMB solution showed mixing of sources within the derived factors, both with and without the addition of speciated organics, as is often the case with complex source mixtures such as those at these urban-scale sites. The nonpolar TD-GC/MS compounds can be obtained from existing filter samples and are a useful complement to the elements, ions, and carbon fractions. However, they should be supplemented with other methods, such as TD-GC/MS on derivitized samples, to obtain a wider range of polar compounds such as sterols, sugars, and organic acids. The PMF and EV solutions to the CMB equations are complementary to, rather than replacements for, each other, as comparisons of their results reveal uncertainties that are not otherwise evident.

IMPLICATIONS

Organic markers can be measured on currently acquired PM(2.5) filter samples by thermal methods. These markers can complement element, ion, and carbon fraction measurements from long-term speciation networks. Applying the positive matrix factorization and effective variance solutions for the chemical mass balance equations provides useful information on the accuracy of the source contribution estimates. Nonpolar compounds need to be complemented with polar compounds to better apportion cooking and secondary organic aerosol contributors.

Particulate emission factors for mobile fossil fuel and biomass combustion sources

PM emission factors (EFs) for gasoline- and diesel-fueled vehicles and biomass combustion were measured in several recent studies. In the Gas/Diesel Split Study (GD-Split), PM(2.5) EFs for heavy-duty diesel vehicles (HDDV) ranged from 0.2 to ~2 g/mile and increased with vehicle age. EFs for HDDV estimated with the U.S. EPA MOBILE 6.2 and California Air Resources Board (ARB) EMFAC2007 models correlated well with measured values. PM(2.5) EFs measured for gasoline vehicles were ~two orders of magnitude lower than those for HDDV and did not correlate with model estimates. In the Kansas City Study, PM(2.5) EFs for gasoline-powered vehicles (e.g., passenger cars and light trucks) were generally <0.03 g/mile and were higher in winter than summer. EMFAC2007 reported higher PM(2.5) EFs than MOBILE 6.2 during winter, but not during summer, and neither model captured the variability of the measured EFs. Total PM EFs for heavy-duty diesel military vehicles ranged from 0.18±0.03 and 1.20±0.12 g/kg fuel, corresponding to 0.3 and 2 g/mile, respectively. These values are comparable to those of on-road HDDV. EFs for biomass burning measured during the Fire Laboratory at Missoula Experiment (FLAME) were compared with EFs from the ARB Emission Estimation System (EES) model. The highest PM(2.5) EFs (76.8±37.5 g/kg) were measured for wet (>50% moisture content) Ponderosa Pine needles. EFs were generally <20 g/kg when moisture content was <20%. The EES model agreed with measured EFs for fuels with low moisture content but underestimated measured EFs for fuel with moisture content >40%. Average EFs for dry chamise, rice straw, and dry grass were within a factor of three of values adopted by ARB in California's San Joaquin Valley (SJV). Discrepancies between measured and modeled emission factors suggest that there may be important uncertainties in current PM(2.5) emission inventories.

Evaluation of Regional-Scale Receptor Modeling

The ability of receptor models to estimate regional contributions to fine particulate matter (PM_2.5) was assessed with synthetic, speciated datasets at Brigantine National Wildlife Refuge (BRIG) in New Jersey and Great Smoky Mountains National Park (GRSM) in Tennessee. Synthetic PM_2.5 chemical concentrations were generated for the summer of 2002 using the Community Multiscale Air Quality (CMAQ) model and chemically speciated PM_2.5 source profiles from the U.S. Environmental Protection Agency (EPA)'s SPECIATE and Desert Research Institute's source profile databases. CMAQ estimated the "true" contributions of seven regions in the eastern United States to chemical species concentrations and individual source contributions to primary PM_2.5 at both sites. A seven-factor solution by the positive matrix factorization (PMF) receptor model explained approximately 99% of the variability in the data at both sites. At BRIG, PMF captured the first four major contributing sources (including a secondary sul-fate factor), although diesel and gasoline vehicle contributions were not separated. However, at GRSM, the resolved factors did not correspond well to major PM_2.5 sources. There were no correlations between PMF factors and regional contributions to sulfate at either site. Unmix produced five- and seven-factor solutions, including a secondary sulfate factor, at both sites. Some PMF factors were combined or missing in the Unmix factors. The trajectory mass balance regression (TMBR) model apportioned sulfate concentrations to the seven source regions using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) trajectories based on Meteorological Model Version 5 (MM5) and Eta Data Simulation System (EDAS) meteorological input. The largest estimated sulfate contributions at both sites were from the local regions; this agreed qualitatively with the true regional apportionments. Estimated regional contributions depended on the starting elevation of the trajectories and on the meteorological input data.

Toward Effective Source Apportionment Using Positive Matrix Factorization: Experiments with Simulated PM2.5 Data

To elucidate the relationship between factors resolved by the positive matrix factorization (PMF) receptor model and actual emission sources and to refine the PMF modeling strategy, speciated PM_2.5 (particulate matter with aerodynamic diameter <2.5 μm) data generated from a state-of-the-art chemical transport model for two rural sites in the eastern United States are subjected to PMF analysis. In addition to χ-² and R ² used to infer the quality of fitting, the interpretability of PMF factors with respect to known primary and secondary sources is evaluated using a root mean square difference analysis. For the most part, factors are found to represent imperfect combinations of sources, and the optimal number of factors should be just adequate to explain the input data (e.g., R ² > 0.95). Retaining more factors in the model does not help resolve minor sources, unless temporal resolution of the data is increased, thus allowing more information to be used by the model. If guided with a priori knowledge of source markers and/or special events, rotation of factors leads to more interpretable PMF factors. The choice of uncertainty weighting coefficients greatly influences the PMF modeling results, but it cannot usually be determined for simulated or real-world data. A simple test is recommended to check whether the weighting coefficients are suitable. However, uncertainties in the data divert PMF solutions even when the optimal weighting coefficients and number of factors are in place.

Particulate carbon measurements in California's San Joaquin Valley

Aerosol carbon sampling methods and biases were evaluated during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) and Fresno Supersite programs. PM2.5 sampling was conducted using Desert Research Institute (DRI) sequential filter samplers (SFS) from December 1999 through February 2001 at two urban sites (Fresno and Bakersfield), one regional transport site (Angiola), and two boundary sites (Bethel Island and Sierra Nevada Foothills) during CRPAQS in the San Joaquin Valley (SJV). Additional filter-based sampling was done in Fresno as part of the US Environmental Protection Agency (EPA) Supersites program. Organic carbon (OC) and elemental carbon (EC) concentrations were higher during winter (December-February) than summer (June-August) and this trend was most pronounced at Fresno and Bakersfield. OC and EC displayed similar diurnal trends during winter and summer at Fresno and during winter at Angiola. The diurnal pattern at Angiola reflected the transport of secondary pollutants to the site. Collocated measurements of OC and EC on undenuded quartz-fiber filters were made at Fresno with the DRI SFS and the Andersen FRM and RAAS samplers. All average differences in OC between samplers were less than their respective measurement uncertainties. Positive and negative OC biases were evaluated at Fresno using the Andersen RAAS sampler with carbon-denuded and undenuded channels with Teflon-membrane and quartz-fiber filter pairs. Differences between the denuded particle OC and that obtained by subtracting the quartz-behind-Teflon or quartz-behind-quartz OC from the undenuded quartz-fiber front filter were less than twice their measurement uncertainties in most cases. Particulate OC in the denuded channel agreed most closely with the difference between undenuded front and backup quartz-fiber OC.

Comparison of PM2.5 carbon measurement methods in Hong Kong, China

Samples from Hong Kong, China, were analyzed for organic carbon (OC), elemental carbon (EC), and total carbon (TC) by three thermal protocols (low-temperature IMPROVE and high-temperature STN and NIOSH) and two optical monitoring methods: reflectance and transmittance. Good agreement (+/-10%) for TC among the three protocols was observed for sample loadings of 1-55 microg m(-3). The two protocols using a reflectance pyrolysis correction showed best agreement for EC, with <20% differences found for approximately 80% of the samples. Hong Kong has a large diesel fleet, and for some heavily loaded samples the light transmittance was too low for quantitative detection, resulting in large uncertainties in the OC/EC split based on transmittance. Hong Kong experienced OC levels similar to those at US sites, but has much higher EC concentrations. OC/EC ratios range from 2 to 5 at two US sites and from 0.2 to 1.2 at three Hong Kong sites.

PM2.5 chemical composition in Hong Kong: urban and regional variations

Chemically speciated PM2.5 measurements were made at roadside, urban, and rural background sites in Hong Kong for 1 year during 2000/2001 to determine the spatial and temporal variations of PM2.5 mass and chemical composition in this highly populated region. Annual average PM2.5 concentrations at the urban and rural sites were 34.1 and 23.7 microg m(-3), respectively, approximately 50-100% higher than the United States' annual average National Ambient Air Quality Standard (NAAQS) of 15 microg m(-3). Daily PM2.5 concentrations exceeded the U.S. 24-h NAAQS of 65 microg m(-3) on 19 days, reaching 131+/-8 microg m(-3) at the roadside site on 02/28/2001. Carbonaceous aerosol is the largest contributor to PM2.5 mass (explaining 52-75% of PM2.5 mass at the two urban sites and 32% at the background site), followed by ammonium sulfate (ranging from 23% to 37% at the two urban sites and 51% at the background site). Ammonium sulfate and crustal concentrations showed more uniform spatial distributions, while the largest urban-rural contrasts found in carbonaceous aerosol (likely due to emissions from on-road gasoline and diesel vehicles). Marine influences accounted for 7% of the mass at the background site (more than twice as much as at the two urban sites). Ternary diagrams are utilized to illustrate the different spatial patterns.