U.S. national PM2.5 Chemical Speciation Monitoring Networks-CSN and IMPROVE: description of networks

Absorption photometry of patterned deposits on IMPROVE PTFE filters

The IMPROVE program (Interagency Monitoring of PROtected Visual Environments) tracks long-term trends in the composition and optics of regional haze aerosols in the United States. The absorptance of red (633-nm) light is monitored by filter photometry of 24 h-integrated samples of fine particulate matter (PM 2.5). These are collected every third day at about 150 rural and often remote locations. Systematic reanalyses of archived samples have established the reproducibility of these optical absorption measurements across decades and instrument systems, with a consistent calibration that is traceable back to 2003.IMPROVE samples for nondestructive analyses by photometry, gravimetry, and X-ray fluorescence are all collected on ring-mounted membranes of expanded PTFE (polytetrafluoroethylene). Although attractively inert, low in blank mass, and optically thin, these media yield visibly nonuniform deposits that do not admit direct interpretation according to a naïve "Beer-Lambert" formulation of optical absorption. Most IMPROVE PTFE deposits exhibit a fine-scale "pixelation" shaped by the perforated screen that supports the membrane during sample collection.This paper extends the traditional Beer-Lambert interpretive model to accommodate the patterned deposits generated by IMPROVE and similarly aggressive sampling protocols on PTFE filters. The extended model is then used to assess the bias and epistemic uncertainty in the aerosol absorption coefficient that IMPROVE has historically reported.ImplicationsAmbient fine aerosol particles are widely collected at high sample face velocities on PTFE (Teflon®) membrane filters backed by perforated support screens. The screens' localized blockage of airflow can channel the resulting PM2.5 sample deposits into a pattern of discrete heavily loaded "pixels", where the screen perforations allow flow through the membrane, separated by a lightly loaded background that reflects blockage by the screen. Such non-uniform deposits, collected throughout the United States on FRM filters to monitor NAAQS compliance and on IMPROVE filters to support the Regional Haze Rule, do not meet the requirements of the Beer-Lambert model for optical absorption measurements. This paper extends Beer-Lambert theory to accommodate patterned deposits, and assesses the bias and epistemic uncertainties introduced by historical (mis)reporting based on "Beer's Law". For samples of moderate (and lesser) absorptance, including the bulk of those collected at rural IMPROVE sites to track regional haze, usefully tight bounds are developed for this pixelation bias.

Source-dependent absorption Ångström exponent in the Los Angeles Basin: Multi-time resolution factor analyses of ambient PM2.5 and aerosol optical absorption

Advanced receptor models can leverage the information derived from optical and chemical variables as input by a variety of instruments at different time resolutions to extract the source specific absorption Ångström exponent (AAE) from aerosol absorption. The multilinear engine (ME-2), a Positive Matrix Factorization (PMF) solver, serves as a proficient tool for performing such analyses, thereby overcoming the constraints imposed by the assumptions in current optical source apportionment methods such as the Aethalometer approach since the use of a-priori AAE values introduces additional uncertainty into the results of optical methods. Comprehensive PM2.5 chemical speciation datasets, and aerosol absorption coefficients (babs, λ) at seven wavelengths measured by an Aethalometer (AE33), were used in multi-time source apportionment (MT-PMF). The study focused on two locations in the Los Angeles (LA) Basin: Central LA (CELA, Main St.), an urban area surrounded by major freeways, and Rubidoux (RIVR), a residential area surrounded by local roads. Factor profiles and temporal variations of their contributions were obtained. Additionally, factor displacements (DISP) and profile constraints were applied. Five-factor solutions were obtained at both sites. At CELA, the resolved factors included traffic + crustal matter (traffic+ Cr_M), secondary sulfate + nitrate (SSN), biomass burning (BB), diesel emissions (DIE) and aged sea salt (ASS). Moreover, source-dependent AAE values at CELA were obtained without a-priori assumption, with values of 1.46 for traffic+ Cr_M, 1.45 for DIE and 2.37 for BB. At RIVR, the resolved factors were traffic+ Cr_M (AAE = 1.24), particulate sulfate, particulate nitrate, BB (AAE = 3.00) and aged sea salt. PM2.5 composition differed at both locations. SSN accounted for the largest fraction of the ambient PM2.5 mass concentration, their sum at the CELA site averaged 40 % of the PM2.5 mass while the same species represented 77 % at RIVR.

Increases in PM2.5 levels in Houston are associated with a highly recirculating sea breeze

Local land-sea breezes play an important role in coastal air quality because they circulate air between coastal/urban and marine areas, potentially causing the accumulation of pollutants. This has been observed for the secondary photochemical pollutant ozone. However, particulate matter (PM) also warrants investigation. To understand the complicated interactions between coastal urban air quality and a local land-sea breeze, we analyzed historical monitoring data from Houston, Texas, which is the fourth most populous city in the United States. Using k-means clustering algorithms to analyze wind data from Houston, we successfully identified a sea breeze recirculation cluster. Additionally, we performed positive matrix factorization on PM2.5 (2.5 μm in diameter or smaller) composition data for 2010-2018 from Houston Deer Park #2 monitoring site, 5 km south of the industrialized Houston Ship Channel. The resulting eight factors indicated a variety of anthropogenic, natural, primary and secondary sources. Emphasizing the PM2.5 sources in each of the wind clusters for June, July, and August, we discovered that on southernly wind and sea breeze recirculation days, the PM2.5 concentrations are ∼30% higher than those under other wind patterns. Under southerly wind, 53% of PM2.5 was attributed to long-range transport of soil and 15% to aged and fresh sea salt. In contrast, on days identified as being impacted by a sea breeze, 60% of PM2.5 was attributed to anthropogenic emissions and only 15% to soil sources. Secondary organic aerosol from multiple sources also appeared to be important on sea breeze days.

Fire Smoke Elevated the Carbonaceous PM2.5 Concentration and Mortality Burden in the Contiguous U.S. and Southern Canada

Despite emerging evidence on the health impacts of fine particulate matter (PM2.5) from wildland fire smoke, the specific effects of PM2.5 composition on health outcomes remain uncertain. We developed a three-level, chemical transport model-based framework to estimate daily full-coverage concentrations of smoke-derived carbonaceous PM2.5, specifically Organic Carbon (OC) and Elemental Carbon (EC), at a 1 km2 spatial resolution from 2002 to 2019 across the contiguous U.S. (CONUS) and Southern Canada (SC). Cross-validation demonstrated that the framework performed well at both the daily and monthly levels. Modeling results indicated that increases in wildland fire smoke have offset approximately one-third of the improvements in background air quality. In recent years, wildland fire smoke has become more frequent and carbonaceous PM2.5 concentrations have intensified, especially in the Western CONUS and Southwestern Canada. Smoke exposure is also occurring earlier throughout the year, leading to more population being exposed. We estimated that long-term exposure to fire smoke carbonaceous PM2.5 is responsible for 7,462 and 259 non-accidental deaths annually in the CONUS and SC, respectively, with associated annual monetized damage of 68.4 billion USD for the CONUS and 1.97 billion CAD for SC. The Southeastern CONUS, where prescribed fires are prevalent, contributed most to these health impacts and monetized damages. Given the challenges posed by climate change for managing prescribed and wildland fires, our findings offer critical insights to inform policy development and assess future health burdens associated with fire smoke exposure.

Impact of improper municipal solid waste management on fostering One Health approach in Ethiopia - challenges and opportunities: A systematic review

Improper disposal of solid waste, predominantly illegal dumping, can lead to severe air and water pollution, land degradation, climate change, and health hazards due to the persistence of hazardous materials. As a result, it is threatening public and animal health, environmental sustainability, and economic development. The One Health approach, which acknowledges the interconnectedness of human, animal, and environmental health, offers a comprehensive solution. This systematic review examines the impact of improper municipal solid waste on fostering One Health approaches at the national level of Ethiopia by identifying key challenges and opportunities. Publications were retrieved from peer-reviewed, indexed journal publications, government documents (policies, proclamations, regulations, and guidelines), and credible non-governmental organization publications from selected electronic databases (Google scholar, PubMed, EMBASE, Global Health, Web of Science, etc.), and governmental offices. Despite efforts to advance the One Health approach in Ethiopia through the formation of the National One Health Steering Committee and technical working groups, implementation is hindered by challenges such as poor sectoral integration, insufficient advocacy, financial constraints, and limited research. These challenges contribute to worsening zoonotic and infectious diseases and environmental issues due to inadequate solid waste management. Nonetheless, opportunities exist through One Health integration via holistic programs, interdisciplinary collaboration, community engagement, policy enhancement, institutional capacity building, and public-private partnerships. Therefore, enhancing sectoral integration and increasing advocacy efforts and securing financial support is necessary to back waste management initiatives and related research. Further research is crucial to understand the impact of solid waste management and the potential benefits of the One Health approach in Ethiopia.

Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter

Global geophysical satellite-derived ambient fine particulate matter (PM2.5) inference relies upon a geophysical relationship (η) from a chemical transport model to relate satellite retrievals of aerosol optical depth (AOD) to surface PM2.5. The resolution dependence of simulated η warrants further investigation. In this study, we calculate geophysical PM2.5 with simulated η from the GEOS-Chem model in its high-performance configuration (GCHP) at cubed-sphere resolutions of C360 (∼25 km) and C48 (∼200 km) and satellite AOD at 0.01° (∼1 km). Annual geophysical PM2.5 concentrations inferred from satellite AOD and GCHP simulations at ∼25 km and ∼200 km resolutions exhibit remarkable similarity (R 2 = 0.96, slope = 1.03). This similarity in part reflects opposite resolution responses across components with population-weighted normalized mean difference (PW-NMD) increasing by 5% to 11% for primary species while decreasing by -30% to -5% for secondary species at finer resolution. Despite global similarity, our results also identify larger resolution sensitivities of η over isolated pollution sources and mountainous regions, where spatial contrast of aerosol concentration and composition is better represented at fine resolution. Our results highlight the resolution dependence of representing near-surface concentrations and the vertical distribution of chemically different species with implications for inferring ground-level PM2.5 from columnar AOD.

Sources of personal PM2.5 exposure during pregnancy in the MADRES cohort

BACKGROUND

Personal exposure to fine particulate matter (PM2.5) is impacted by different sources each with different chemical composition. Determining these sources is important for reducing personal exposure and its health risks especially during pregnancy.

OBJECTIVE

Identify main sources and their contributions to the personal PM2.5 exposure in 213 women in the 3rd trimester of pregnancy in Los Angeles, CA.

METHODS

We measured 48-hr integrated personal PM2.5 exposure and analyzed filters for PM2.5 mass, elemental composition, and optical carbon fractions. We used the EPA Positive Matrix Factorization (PMF) model to resolve and quantify the major sources of personal PM2.5 exposure. We then investigated bivariate relationships between sources, time-activity patterns, and environmental exposures in activity spaces and residential neighborhoods to further understand sources.

RESULTS

Mean personal PM2.5 mass concentration was 22.3 (SD = 16.6) μg/m3. Twenty-five species and PM2.5 mass were used in PMF with a final R2 of 0.48. We identified six sources (with major species in profiles and % contribution to PM2.5 mass) as follows: secondhand smoking (SHS) (brown carbon, environmental tobacco smoke; 65.3%), fuel oil (nickel, vanadium; 11.7%), crustal (aluminum, calcium, silicon; 11.5%), fresh sea salt (sodium, chlorine; 4.7%), aged sea salt (sodium, magnesium, sulfur; 4.3%), and traffic (black carbon, zinc; 2.6%). SHS was significantly greater in apartments compared to houses. Crustal source was correlated with more occupants in the household. Aged sea salt increased with temperature and outdoor ozone, while fresh sea salt was highest on days with westerly winds from the Pacific Ocean. Traffic was positively correlated with ambient NO2 and traffic-related NOx at residence. Overall, 76.8% of personal PM2.5 mass came from indoor or personal compared to outdoor sources.

IMPACT

We conducted source apportionment of personal PM2.5 samples in pregnancy in Los Angeles, CA. Among identified sources, secondhand smoking contributed the most to the personal exposure. In addition, traffic, crustal, fuel oil, fresh and aged sea salt sources were also identified as main sources. Traffic sources contained markers of combustion and non-exhaust wear emissions. Crustal source was correlated with more occupants in the household. Aged sea salt source increased with temperature and outdoor ozone and fresh sea salt source was highest on days with westerly winds from the Pacific Ocean.

A case-crossover study of ST-elevation myocardial infarction and organic carbon and source-specific PM2.5 concentrations in Monroe County, New York

Background

Previous work reported increased rates of cardiovascular hospitalizations associated with increased source-specific PM2.5 concentrations in New York State, despite decreased PM2.5 concentrations. We also found increased rates of ST elevation myocardial infarction (STEMI) associated with short-term increases in concentrations of ultrafine particles and other traffic-related pollutants in the 2014-2016 period, but not during 2017-2019 in Rochester. Changes in PM2.5 composition and sources resulting from air quality policies (e.g., Tier 3 light-duty vehicles) may explain the differences. Thus, this study aimed to estimate whether rates of STEMI were associated with organic carbon and source-specific PM2.5 concentrations.

Methods

Using STEMI patients treated at the University of Rochester Medical Center, compositional and source-apportioned PM2.5 concentrations measured in Rochester, a time-stratified case-crossover design, and conditional logistic regression models, we estimated the rate of STEMI associated with increases in mean primary organic carbon (POC), secondary organic carbon (SOC), and source-specific PM2.5 concentrations on lag days 0, 0-3, and 0-6 during 2014-2019.

Results

The associations of an increased rate of STEMI with interquartile range (IQR) increases in spark-ignition emissions (GAS) and diesel (DIE) concentrations in the previous few days were not found from 2014 to 2019. However, IQR increases in GAS concentrations were associated with an increased rate of STEMI on the same day in the 2014-2016 period (Rate ratio [RR] = 1.69; 95% CI = 0.98, 2.94; 1.73 μg/m3). In addition, each IQR increase in mean SOC concentration in the previous 6 days was associated with an increased rate of STEMI, despite imprecision (RR = 1.14; 95% CI = 0.89, 1.45; 0.42 μg/m3).

Conclusion

Increased SOC concentrations may be associated with increased rates of STEMI, while there seems to be a declining trend in adverse effects of GAS on triggering of STEMI. These changes could be attributed to changes in PM2.5 composition and sources following the Tier 3 vehicle introduction.

Spatial variability of pollution source contributions during two (2012-2013 and 2018-2019) sampling campaigns at ten sites in Los Angeles basin

This study assessed the spatial variability of PM2.5 source contributions across ten sites located in the South Coast Air Basin, California. Eight pollution sources and their contributions were obtained using positive matrix factorization (PMF) from the PM2.5 compositional data collected during the two sampling campaigns (2012/13 and 2018/19) of the Multiple Air Toxics Exposure Study (MATES). The identified sources were "gasoline vehicles", "aged sea salt", "biomass burning", "secondary nitrate", "secondary sulfate", "diesel vehicles", "soil/road dust" and "OP-rich". Among them, "gasoline vehicle" was the largest contributor to the PM2.5 mass. The spatial distributions of source contributions to PM2.5 at the sites were characterized by the Pearson correlation coefficients as well as coefficients of determination and divergence. The highest spatial variability was found for the contributions from the "OP-rich" source in both MATES campaigns suggesting varying influences of the wildfires in the Los Angeles Basin. Alternatively, the smallest spatial variabilities were observed for the contributions of the "secondary sulfate" and "aged sea salt" sources resolved for the MATES campaign in 2012/13. The "soil/road dust" contributions of the sites from the 2018/19 campaign were also highly correlated. Compared to the other sites, the source contribution patterns observed for Inland Valley and Rubidoux were the most diverse from the others likely due to their remote locations from the other sites, the major urban area, and the Pacific Ocean.

Long-term source apportionment of PM2.5 across the contiguous United States (2000-2019) using a multilinear engine model

Identifying PM2.5 sources is crucial for effective air quality management and public health. This research used the Multilinear Engine (ME-2) model to analyze PM2.5 from 515 EPA Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE) sites across the U.S. from 2000 to 2019. The U.S. was divided into nine regions for detailed analysis. A total of seven source types (tracers) were resolved across the country: (1) Soil/Dust (Si, Al, Ca and Fe); (2) Vehicle emissions (EC, OC, Cu and Zn); (3) Biomass/wood burning (K); (4) Heavy oil/coal combustion (Ni, V, Cl and As); (5) Secondary sulfate (SO42-); (6) Secondary nitrate (NO3-) and (7) Sea salt (Mg, Na, Cl and SO42-). Furthermore, we extracted and calculated secondary organic aerosols (SOA) based on the secondary sulfate and nitrate factors. Notably, significant reductions in secondary sulfate, nitrate, and heavy oil/coal combustion emissions reflect recent cuts in fossil-fueled power sector emissions. A decline in SOA suggests effective mitigation of their formation conditions or precursors. Despite these improvements, vehicle emissions and biomass burning show no significant decrease, highlighting the need for focused control on these persistent pollution sources for future air quality management.

Regime shift in secondary inorganic aerosol formation and nitrogen deposition in the rural United States

Secondary inorganic aerosols play an important role in air pollution and climate change, and their formation modulates the atmospheric deposition of reactive nitrogen (including oxidized and reduced nitrogen), thus impacting the nitrogen cycle. Large-scale and long-term analyses of secondary inorganic aerosol formation based on model simulations have substantial uncertainties. Here we improve constraints on secondary inorganic aerosol formation using decade-long in situ observations of aerosol composition and gaseous precursors from multiple monitoring networks across the United States. We reveal a shift in the secondary inorganic aerosol formation regime in the rural United States between 2011 and 2020, making rural areas less sensitive to changes in ammonia concentrations and shortening the effective atmospheric lifetime of reduced forms of reactive nitrogen. This leads to potential increases in reactive nitrogen deposition near ammonia emission hotspots, with ecosystem impacts warranting further investigation. Ammonia (NH3), a critical but not directly regulated precursor of fine particulate matter in the United States, has been increasingly scrutinized to improve air quality. Our findings, however, show that controlling NH3 became significantly less effective for mitigating fine particulate matter in the rural United States. We highlight the need for more collocated aerosol and precursor observations for better characterization of secondary inorganic aerosols formation in urban areas.

Atmospheric reduced nitrogen: Sources, transformations, effects, and management

Human activities have increased atmospheric emissions and deposition of oxidized and reduced forms of nitrogen, but emission control programs have largely focused on oxidized nitrogen. As a result, in many regions of the world emissions of oxidized nitrogen are decreasing while emissions of reduced nitrogen are increasing. Emissions of reduced nitrogen largely originate from livestock waste and fertilizer application, with contributions from transportation sources in urban areas. Observations suggest a discrepancy between trends in emissions and deposition of reduced nitrogen in the U.S., likely due to an underestimate in emissions. In the atmosphere, ammonia reacts with oxides of sulfur and nitrogen to form fine particulate matter that impairs health and visibility and affects climate forcings. Recent reductions in emissions of sulfur and nitrogen oxides have limited partitioning with ammonia, decreasing long-range transport. Continuing research is needed to improve understanding of how shifting emissions alter formation of secondary particulates and patterns of transport and deposition of reactive nitrogen. Satellite remote sensing has potential for monitoring atmospheric concentrations and emissions of ammonia, but there remains a need to maintain and strengthen ground-based measurements and continue development of chemical transport models. Elevated nitrogen deposition has decreased plant and soil microbial biodiversity and altered the biogeochemical function of terrestrial, freshwater, and coastal ecosystems. Further study is needed on differential effects of oxidized versus reduced nitrogen and pathways and timescales of ecosystem recovery from elevated nitrogen deposition. Decreases in deposition of reduced nitrogen could alleviate exceedances of critical loads for terrestrial and freshwater indicators in many U.S. areas. The U.S. Environmental Protection Agency should consider using critical loads as a basis for setting standards to protect public welfare and ecosystems. The U.S. and other countries might look to European experience for approaches to control emissions of reduced nitrogen from agricultural and transportation sectors.Implications: In this Critical Review we synthesize research on effects, air emissions, environmental transformations, and management of reduced forms of nitrogen. Emissions of reduced nitrogen affect human health, the structure and function of ecosystems, and climatic forcings. While emissions of oxidized forms of nitrogen are regulated in the U.S., controls on reduced forms are largely absent. Decreases in emissions of sulfur and nitrogen oxides coupled with increases in ammonia are shifting the gas-particle partitioning of ammonia and decreasing long-range atmospheric transport of reduced nitrogen. Effort is needed to understand, monitor, and manage emissions of reduced nitrogen in a changing environment.

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

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.

Increasing Contributions of Temperature-Dependent Oxygenated Organic Aerosol to Summertime Particulate Matter in New York City

As part of the summer 2022 NYC-METS (New York City metropolitan Measurements of Emissions and TransformationS) campaign and the ASCENT (Atmospheric Science and Chemistry mEasurement NeTwork) observational network, speciated particulate matter was measured in real time in Manhattan and Queens, NY, with additional gas-phase measurements. Largely due to observed reductions in inorganic sulfate aerosol components over the 21st century, summertime aerosol composition in NYC has become predominantly organic (80-83%). Organic aerosol source apportionment via positive matrix factorization showed that this is dominated by secondary production as oxygenated organic aerosol (OOA) source factors comprised 73-76% of OA. Primary factors, including cooking-related organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA) comprised minor fractions of OA, only 13-15% and 10-11%, respectively. The two sites presented considerable spatiotemporal variations in OA source factor concentrations despite similar average PM2.5 concentrations. The less- and more-oxidized OOA factors exhibited clear temperature dependences at both sites with increased concentrations and greater degrees of oxidation at higher temperatures, including during a heatwave. With strong temperature sensitivity and minimal changes in summertime concentrations since 2001, secondary OA poses a particular challenge for air quality policy in NYC that will very likely be exacerbated by continued climate change and extreme heat events.

A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3

Urban smoke exposure events from large wildfires have become increasingly common in California and throughout the western United States. The ability to study the impacts of high smoke aerosol exposures from these events on the public is limited by the availability of high-quality, spatially resolved estimates of aerosol concentrations. Methods for assigning aerosol exposure often employ multiple data sets that are time-consuming to create and difficult to reproduce. As these events have gone from occasional to nearly annual in frequency, the need for rapid smoke exposure assessments has increased. The rapidfire (relatively accurate particulate information derived from inputs retrieved easily) R package (version 0.1.3) provides a suite of tools for developing exposure assignments using data sets that are routinely generated and publicly available within a month of the event. Specifically, rapidfire harvests official air quality monitoring, satellite observations, meteorological modeling, operational predictive smoke modeling, and low-cost sensor networks. A machine learning approach, random forest (RF) regression, is used to fuse the different data sets. Using rapidfire, we produced estimates of ground-level 24 h average particulate matter for several large wildfire smoke events in California from 2017-2021. These estimates show excellent agreement with independent measures from filter-based networks.

Aerosol Thermodynamics: Nitrate Loss from Regulatory PM2.5 Filters in California

Fine particulate matter (PM2.5) mass concentrations reported by regulatory networks are declining across the United States. It is well established that ammonium nitrate contributes substantially to the PM2.5 mass in the western United States, and that Teflon filters commonly used by regulatory monitors are subject to negative mass artifacts due to ammonium nitrate volatilization. This study focuses on the San Joaquin Valley (SJV), an environmental justice (EJ) and agricultural region with persistently poor air quality. The SJV is a serious nonattainment area of PM2.5 National Ambient Air Quality Standards (NAAQS) with substantial nitrate mass concentrations. We explicitly model the chemical thermodynamic equilibrium of the ammonium nitrate-nitric acid systems and quantify volatilization across California as a function of the deliquescence point relative humidity (%DRH). Nitrate loss is estimated at all federal reference method (FRM) and federal equivalent method (FEM) monitors from 2001 to 2021. Nearly 20% of PM2.5 mass is lost from filters in the SJV area, especially during winter and fall when particulate nitrate mass is most abundant. All decadal PM2.5 trends calculated from reported measurements in Kern, Tulare, and Fresno counties in the SJV show greater decline in PM2.5 mass when nitrate loss is accounted for, up to a factor of 20 in Kern county. This suggests PM2.5 mass concentrations reported in regulatory networks are biased low relative to the actual atmospheric burden, notably in an EJ area that lags behind most of the country's air quality improvements.

Common and distinct pollution sources identified from ambient PM2.5 concentrations in two sites of Los Angeles Basin from 2005 to 2019

The effects of air quality control policies implemented in California from 2005 to 2019 targeting sources contributing to ambient PM2.5 concentrations, were assessed at two sampling sites in the Los Angeles Basin (N. Main Street and Rubidoux). The spatial and temporal variations of pollution source contributions obtained from dispersion-normalized positive matrix factorization, (DN-PMF) were interpreted with respect to site specific locations. Secondary nitrate and secondary sulfate were the major contributors to the ambient PM2.5 mass concentrations at both sites with substantial concentration decreases after 2008 that were likely due to the implementation of California specific programs including stricter NOx emissions control on motor vehicles. Biomass burning emissions also decreased over the study period at both sampling sites except for one event in December 2005 when strong winter storms and multiple floods led to unusually low atmospheric temperatures and likely increased residential wood burning. The large number of wildfires, trans-Pacific transport of mineral dust and regional dust transported by strong Santa Ana winds and agriculturally generated dust in Rubidoux contributed to poor air quality. Severe storms and devastating wildfires were also linked to the elevated pyrolyzed organic carbon (OP-rich) concentrations. The two distinct region-specific sources, describing fuel combustion in LA, were "residual oil" and "traffic", while separate "gasoline" and "diesel" vehicles sources were identified in Rubidoux. California emissions standards program which required replacement of conventional cars with electric or hybrid vehicles and standards for gasoline and diesel fuels, led to lower "traffic" contributions. Gasoline vehicle emissions after 2017 in Rubidoux also decreased. "Diesel" concentrations declined between 2007 and 2011 because of the recession from late 2007 to early 2009 and the Federal Heavy-Duty Diesel Rule.

Atmospheric elemental carbon pollution and its regional health disparities in China

Previous studies have reported that atmospheric elemental carbon (EC) may pose potentially elevated toxicity when compared to total ambient fine particulate matter (PM2.5). However, most research on EC has been conducted in the US and Europe, whereas China experiences significantly higher EC pollution levels. Investigating the health impact of EC exposure in China presents considerable challenges due to the absence of a monitoring network to document long-term EC levels. Despite extensive studies on total PM2.5 in China over the past decade and a significant decrease in its concentration, changes in EC levels and the associated mortality burden remain largely unknown. In our study, we employed a combination of satellite remote sensing, available ground observations, machine learning techniques, and atmospheric big data to predict ground EC concentrations across China for the period 2005-2018, achieving a spatial resolution of 10 km. Our findings reveal that the national average annual mean EC concentration has remained relatively stable since 2005, even as total PM2.5 levels have substantially decreased. Furthermore, we calculated the all-cause non-accidental deaths attributed to long-term EC exposure in China using baseline mortality data and pooled mortality risk from a cohort study. This analysis unveiled significant regional disparities in the mortality burden resulting from long-term EC exposure in China. These variations can be attributed to varying levels of effectiveness in EC regulations across different regions. Specifically, our study highlights that these regulations have been effective in mitigating EC-related health risks in first-tier cities. However, in regions characterized by a high concentration of coal-power plants and industrial facilities, additional efforts are necessary to control emissions. This observation underscores the importance of tailoring environmental policies and interventions to address the specific challenges posed by varying emission sources and regional contexts.

Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study

BACKGROUND

Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA.

METHODS

In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health.

FINDINGS

Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke.

INTERPRETATION

Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades.

FUNDING

National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

Dust Under the Radar: Rethinking How to Evaluate the Impacts of Dust Events on Air Quality in the United States

Dust is an important and complex constituent of the atmospheric system, having significant impacts on the environment, climate, air quality, and human health. Although dust events are common across many regions of the United States, their impacts are not often prioritized in air quality mitigation strategies. We argue that there are at least three factors that result in underestimation of the social and environmental impact of dust events, making them receive less attention. These include (a) sparse monitoring stations with irregular spatial distribution in dust-influenced regions, (b) inconsistency with dust sampling methods, and (c) sampling frequency and schedules, which can lead to missed dust events or underestimation of dust particle concentrations. Without addressing these three factors, it is challenging to characterize and understand the full air quality impacts of dust events in the United States. This paper highlights the need for additional monitoring to measure these events so that we can more fully evaluate and understand their impacts, as they are predicted to increase with climate change.

Mortality risk from United States coal electricity generation

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM2.5" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.

PM2.5 pollution exceeding Indian standard over a semi-urban region at eastern IGP: Chemistry, meteorological impact, and long-range transport

A year-long study (January-December 2019) on the chemical characterization and meteorological impact on PM2.5 was conducted over a semi-urban station, Shyamnagar, in the easternmost part of the Indo-Gangetic Plains (IGP). PM2.5 concentrations (Mean = 81.69 ± 66.27 μgm-3; 7.10-272.74 μgm-3), the total carbonaceous aerosols (TCA) (Mean = 22.85 ± 24.95 μgm-3; 0.77-102.97 μgm-3) along with differential carbonaceous components like organic carbon (OC) (Mean = 11.28 ± 12.48 μgm-3; 0.48-53.01 μgm-3) and elemental carbon (EC) (Mean = 4.83 ± 5.28 μgm-3; 0.1-22.13 μgm-3) exhibited prominent seasonal variability with the highest concentrations during winter, followed by post-monsoon, pre-monsoon and lowest during monsoon. A similar seasonal variation was observed for the total water-soluble ionic species (Mean = 31.91 ± 20.12 μgm-3; 0.1-126.73 μgm-3). We observed that under the least favorable conditions (low ventilation coefficient), high PM2.5 pollution (exceeding Indian standard) was associated with a high increase in secondary components of PM2.5. Eastern, central and western parts of IGP, as well as Nepal, were the major long-distant source regions whereas the northern part of West Bengal and parts of Bangladesh were the major regional source region for high PM2.5 pollution over Shyamnagar. The ratios like char-EC/soot-EC, non-sea-K+/EC and non-sea-SO42-/EC strongly indicated the dominance of fossil fuel burning over biomass burning. Compared with other studies, we observed that the PM2.5 pollution over this semi-urban region was comparable (and even higher in some cases) with other parts of IGP. The high exceedance of PM2.5 over the Indian standard in Shyamnagar strongly demands an immediate initiation of systematic and regular based air pollution monitoring over semi-urban/non-urban regions in India, especially IGP, in addition to the polluted cities.

Evaluating IMPROVE PM2.5 element measurements

The Interagency Monitoring of PROtected Visual Environments (IMPROVE) network has collected airborne particulate matter (PM) samples at locations throughout the United States since 1988 and provided chemical speciation measurements on the samples using several techniques including X-ray fluorescence (XRF). New XRF instruments for measuring PM elemental content of IMPROVE samples were introduced in 2011. To evaluate the performance of these new instruments relative to the old instruments, archived sample from three IMPROVE monitoring sites were retrieved and analyzed on the new instruments. The agreement between the two instruments varied by element. Comparisons of the results were very good (slopes within 10% of unity) for most elements regularly measured well above the detection limits (sulfur, chlorine, potassium, titanium, vanadium, manganese, iron, copper, zinc, selenium, lead). Different particle compositions at the three sites highlighted different measurement interferences. High sea salt concentrations at the coastal site emphasized corrections applied in the old systems to light elements - sodium and magnesium - and resulted in poor agreement for these elements. Comparisons of the XRF measurements with collocated sulfate measurements by ion chromatography suggest that sulfur measurements from the new instruments are more precise but slight underestimates. Comparing elemental ratios to expected ratios for soil-derived PM demonstrate the new instruments are better at resolving the aluminum and silicon peaks.Implications: The presented work represents a comprehensive analysis of the method change enacted within the Interagency Monitoring of PROtected Visual Environments (IMPROVE) air monitoring network. This work describes the implications of the last change in elemental quantification methodology. The most important point for data users performing longitudinal analyses is that light elements (e.g., sodium - sulfur) were affected; the old instrumentation overestimated these elements while the current measurements are slightly underestimated. The authors recommend these results to be taken into consideration when interpreting sea salt and crustal sources of atmospheric dust.

Fugitive gypsum dust deposition on a neighboring wildlife refuge, Antioch Dunes, California, USA

Fugitive dust emissions play an important role in urban air quality. Much research on fugitive dust's effects has focused on human health and societal impacts, with limited work investigating effects on other species. The endangered Apodemia mormo langei butterfly is endemic to the Antioch Dunes, a small area on the south bank of the San Joaquin River in northern California, largely protected as a National Wildlife Refuge. Between the two protected portions of the dunes is a gypsum processing facility. Deposition of gypsum dust may adversely affect endangered insects, especially in their vulnerable larval life stage. Persistent westerly winds blow from the western section of the refuge, across the industrial facility, to the eastern protected dune area. Ambient particulate matter (PM) was collected at 30 sites in both sections of the refuge using passive samplers deployed at times matching the butterfly life cycle. The prevailing wind maintained upwind-downwind sampling orientation throughout the study. PM samples were analyzed for total mass, and elemental composition via X-ray fluorescence. Downwind concentrations of gypsum-related elements were between 4 (strontium) and 12 (sulfur) times higher than upwind loadings, suggesting deposition of PM from the gypsum facility. The effect of fugitive emissions was strongest at the industrial facility's fenceline, closest to a conveyor belt that loads gypsum. Combined with documented reductions in insect larval longevity when exposed to gypsum dust, the results suggest that gypsum deposition may be affecting the ecosystem and endangered species in the downwind unit of the Antioch Dunes National Wildlife Refuge.Implications: Fugitive dust has impacts not only on humans, but on other organisms. The Antioch Dunes National Wildlife Refuge (ADNWR) in California, set aside to protect the endangered Apodemia mormo langei butterfly, consists of two land units separated by a gypsum processing facility in between them. In this study, we demonstrate fugitive gypsum dust deposition on the downwind unit of the ADNWR, which may impact the endangered butterfly and its ecosystem.

Source-Receptor Relationships Between Precursor Emissions and O3 and PM2.5 Air Pollution Impacts

Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), secondarily formed PM2.5, and ozone (O3) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM2.5 and seasonal average maximum daily average 8 h (MDA8) O3 for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM2.5 chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM2.5. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O3 and PM2.5. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.

Assessing Unequal Airborne Exposure to Lead Associated With Race in the USA

Recent research applied the United States Environmental Protection Agency's Chemical Speciation Network and Interagency Monitoring of Protected Visual Environments monitoring stations and observed that mean concentrations of atmospheric lead (Pb) in highly segregated counties are a factor of 5 higher than in well-integrated counties and argument is made that regulation of existing airborne Pb emissions will reduce children's Pb exposure. We argue that one of the main sources of children's current Pb exposure is from resuspension of legacy Pb in soil dust and that the racial disparity of Pb exposure is associated with Pb-contaminated community soils.

Bayesian Inference Approach to Quantify Primary and Secondary Organic Carbon in Fine Particulate Matter Using Major Species Measurements

The determination of primary organic carbon (POC) and secondary organic carbon (SOC) in fine particulate matter using ambient measurements is essential in atmospheric chemistry. A novel Bayesian inference (BI) approach is proposed to achieve such quantification using only major component measurement data and tested in two case studies. One case study composes of filter-based daily compositional data made in the Pearl River Delta region, China, during 2012, while the other uses online measurement data recorded at the Dianshan Lake monitoring site in Shanghai in wintertime 2019. Source-specific organic trace measurement data are available in both the cases so that positive matrix factorization (PMF) analysis is performed, where PMF-resolved POC and SOC are used as the best available reference values for model evaluation. Meanwhile, traditional techniques, i.e., minimum ratio value, minimum R squared, and multiple linear regression, are also employed and evaluated. For both the cases, the BI models have shown significant advantages in accurately estimating POC and SOC amounts over conventional methods. Further analysis suggests that using sulfate as the SOC tracer in BI model gives the best model performance. This methodological advance provides an improved and practical tool to derive POC and SOC levels for addressing PM-related environmental impacts.

Assessment of trace elements directly from archived total suspended particulate filters by laser ablation ICP-MS: A case study of South Carolina

Background

Exposure to particulate air pollution is one of the greatest environmental risk factors for adverse human health outcomes. However, the constituents that may be responsible for such adverse health effects have not been fully studied.

Methods

Total suspended particulates filters collected every 6 days in 2011 from three South Carolina locations were used in this case study. An inductively coupled plasma mass spectrometer interfaced with a laser ablation system (LA-ICP-MS) was used to directly analyze 41 inorganic elemental species on air pollution filters. Then, machine learning and multivariate statistical methods was employed to identify combinatorial patterns in the data and classify sites based on their elemental composition.

Results

Forty-one elements were assessed and 33 were used in subsequent analysis. Correlations between United States Environmental Protection Agency (US EPA)'s chemical analysis dataset and data from the current study revealed significant associations between 7/15 elements with enough variation for comparison (r between 0.28 to 0.66, p<0.05). Subsequent multivariate analyses revealed four distinct patterns in the distribution of elements by sample location throughout the year.

Conclusion

The different airborne elements may need to be assessed to understand combinations of elements which occur together over space and/or time. Such information can be helpful in planning effective counter measures and strategies to control particulate air pollution.

Inequitable Exposures to U.S. Coal Power Plant-Related PM2.5: 22 Years and Counting

BACKGROUND

Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-à-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups.

OBJECTIVES

We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) associated with coal power plant SO 2 emissions. We linked exposure reductions with three specific actions taken at individual power plants: scrubber installations, reduced operations, and retirements. We assessed how emissions changes in different locations have influenced exposure inequities, extending previous source-specific environmental justice analyses by accounting for location-specific differences in racial/ethnic population distributions.

METHODS

We developed a data set of annual PM 2.5 source impacts ("coal PM 2.5 ") associated with SO 2 emissions at each of 1,237 U.S. coal-fired power plants across 1999-2020. We linked population-weighted exposure with information about each coal unit's operational and emissions-control status. We calculate changes in both relative and absolute exposure differences across demographic groups.

RESULTS

Nationwide population-weighted coal PM 2.5 declined from 1. 96 μ g / m 3 in 1999 to 0.06   μ g / m 3 in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to SO 2 scrubber installations, and after 2010 most of the decrease is attributable to retirements. Black populations in the South and North Central United States and Native American populations in the western United States were inequitably exposed early in the study period. Although inequities decreased with falling emissions, facilities in states across the North Central United States continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West.

DISCUSSION

We show that air quality controls, operational adjustments, and retirements since 1999 led to reduced exposure to coal power plant related PM 2.5 . Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to PM 2.5 associated with facilities in the North Central and western United States. https://doi.org/10.1289/EHP11605.

Mobile Sources Are Still an Important Source of Secondary Organic Aerosol and Fine Particulate Matter in the Los Angeles Region

Secondary organic aerosol (SOA) is a significant component of atmospheric fine particulate matter. Mobile sources have historically been a major source of SOA precursors in urban environments, but decades of regulations have reduced their emissions. Less regulated sources, such as volatile chemical products (VCPs), are of growing importance. We analyzed ambient and emissions data to assess the contribution of mobile sources to SOA formation in Los Angeles during the period of 2009-2019. During this period, air quality in the Los Angeles region has improved, but organic aerosol (OA) concentrations did not decrease as much as primary pollutants. This appears to be largely due to SOA, whose mass fraction in OA increased over this period. In 2010, about half of the freshly formed SOA measured in Pasadena, CA appears to be formed from hydrocarbon (non-oxygenated) precursors. Chemical mass balance analysis indicates that these hydrocarbon SOA precursors (including intermediate volatility organic compounds) can largely be explained by emissions from mobile sources in 2010. Our analysis indicates that continued reduction in emissions from mobile sources should lead to additional significant decreases in atmospheric SOA and PM_2.5 mass in the Los Angeles region.

Quantifying residual elemental carbon by thermal-optical analysis using an extended IMPROVE_A protocol with higher maximum temperature

Thermal-optical analysis (TOA) has long been used to quantify organic carbon (OC) and elemental carbon (EC) on quartz-fiber filter samples collected in national ambient air monitoring networks. In the routine analysis of samples from the Chemical Speciation Network (CSN), we observed a considerable fraction of filter punches that remain gray or black in color after TOA was completed, suggesting the presence of EC that was not fully evolved at the highest temperature specified by the IMPROVE_A protocol (840°C). In this work, we explored the operational conditions necessary to evolve and quantify such residual EC. First, four heavily loaded CSN samples were analyzed to evaluate modifications to the IMPROVE_A protocol. We found that adding a higher temperature step at 930°C more effectively evolved the residual EC than did lengthening the duration of the 840°C step. Compared with the standard IMPROVE_A results, the modified protocol evolved additional EC of 1.08 to 4.45 µg cm^-2 in mass, or 0.12 to 0.50 µg m^-3 in concentration. This excess EC accounts for 27.1% to 45.3% of the total EC and 7.6% to 25.1% of the total carbon by standard IMPROVE_A. We then analyzed over 2600 samples from CSN using the extended IMPROVE_A protocol with higher maximum temperature (930°C). A total of 168 samples (6.4% of the total samples analyzed) contained measurable EC at the 930°C step. The average fraction of the evolvable residual EC mass in total EC is 5.7%, and up to 28% for samples with high total EC mass loading (i.e., 95th percentile and above).Implications: Our results suggest that CSN EC measured by the standard IMPROVE_A protocol should be considered a lower limit, and that a higher maximum heating temperature can be used to better quantify EC from CSN sites impacted by fresh urban emissions.

Variabilities of δ13C and carbonaceous components in ambient PM2.5 in Northeast India: Insights into sources and atmospheric processes

A year-long sampling campaign of ambient PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 mm) at a regional station in the North-Eastern Region (NER) of India was performed to understand the sources and formation of carbonaceous aerosols. Mass concentration, carbon fractions (organic and elemental carbon), and stable carbon isotope ratio (δ¹³C) of PM_2.5 were measured and studied along with cluster analysis and Potential Source Contribution Function (PSCF) modelling. PM_2.5 mass concentration was observed to be highest during winter and post-monsoon seasons when the meteorological conditions were relatively stable compared to other seasons. Organic carbon (OC) concentration was more than two times higher in the post-monsoon and winter seasons than in the pre-monsoon and monsoon seasons. Air mass back trajectory cluster analysis showed the dominance of local and regional air masses during winter and post-monsoon periods. In contrast, long-range transported air masses influenced the background site in pre-monsoon and monsoon. Air mass data and PSCF analysis indicated that aerosols during winter and post-monsoon are dominated by freshly generated emissions from local sources along with the influence from regional transport of polluted aerosols. On the contrary, the long-range transported air masses containing aged aerosols were dominant during pre-monsoon. No significant variability was observed in the range of δ¹³C values (-28.2‰ to -26.4‰) during the sampled seasons. The δ¹³C of aerosols indicates major sources to be combustion of biomass/biofuels (C3 plant origin), biogenic aerosols, and secondary aerosols. The δ¹³C variability and cluster/PSCF modelling suggest that aged aerosols (along with enhanced photo-oxidation derived secondary aerosols) influenced the final δ¹³C during the pre-monsoon. On the other hand, lower δ¹³C in winter and post-monsoon is attributed to the freshly emitted aerosols from biomass/biofuels.

Future year (2028) source apportionment modeling to support Regional Haze Rule planning in the western U.S

The Western Regional Air Partnership (WRAP) has developed a modeling platform to simulate the formation of haze-causing particles that impact federally-protected lands in the western United States. To assist state air quality planners in determining which emission sources are likely candidates for future mitigation, several source apportionment scenarios were evaluated, and two sets of results for the year 2028 are presented here: 1) a "high-level important regional sources" version, with broad emission categories (i.e. U.S. anthropogenic, international anthropogenic, natural, and fires), and 2) a "low-level anthropogenic emission sources within individual states" version, which refines the U.S. anthropogenic contribution to specific emission sectors within individual WRAP region states. Eight examples are discussed, which reflect the variation in source apportionment results at national parks, wilderness areas, and wildlife refuges in the western U.S. and suggest which emission sectors are candidates for mitigation to improve future visibility. In 2028, the contribution of domestic anthropogenic emissions at the eight sites ranges from 17% to 58%, with significant impacts from oil and gas production, fossil fuel electric generation, and federally-regulated mobile sources. The contribution from international anthropogenic sources can also be considerable, and ranges from 17% to 43%. Most sectors that are emitting sulfur dioxide (SO₂) and nitrogen oxides (NO_x), which are the two most likely particle precursors to be curtailed in the states' Regional Haze plans, are declining. For example, in the 13 contiguous WRAP region states, NO_x emissions from on-road mobile sources and electric generating units (EGUs) declined by 738 kton/yr (29% decrease) and 65 kton/yr (31% decrease), respectively, in 2028 as compared to current emission estimates, and SO₂ emissions from EGUs declined by 42 kton/yr (29% decrease). NO_x emissions from oil and gas development also declined by 25 kton/yr (9% decrease) but rose for SO₂ emissions by 12 kton/yr (20% increase).Implications: The goal of the Regional Haze Rule (RHR) is to improve visibility at federally-protected areas, and to eventually arrive at natural conditions by the year 2064. Source apportionment tools within regional air quality models are useful for identifying which emission regions and sectors are contributing to haze-causing particles and can indicate to air quality planners where additional emission controls may be warranted.

Assessment of progress toward regional haze rule visibility goals using United States anthropogenic emissions rate of progress

The U.S. EPA developed the Regional Haze Rule to address Section 7491 of the 1977 Clean Air Act Amendments to prevent any future and remedy any existing visibility impairment due to manmade air pollution at Federal Class I areas (CIAs). The rule addresses this national goal by requiring states to show they are making progress toward estimated natural conditions by 2064 for the 20% anthropogenically Most Impaired Days (MID). For the MID, days that have high haze contributions from wildfires and windblown dust tend to be excluded using haze contributions from Carbon and crustal material as surrogates. To show progress toward natural conditions in 2064, a Uniform Rate of Progress Glidepath is defined as a straight line from measured 2000-2004 IMPROVE MID Baseline to natural conditions in 2064. Photochemical modeling is used to project the observed IMPROVE 2014-2018 MID visibility to 2028 that is compared to the Glidepath at 2028 to determine whether the MID visibility at a CIA is on a path toward natural visibility conditions in 2064. This paper discusses an alternative approach for showing progress toward no manmade impairment by using modeling results to generate a U.S. Anthropogenic Emissions Rate of Progress (RoP). The CAMx photochemical grid model was run for a current year (representing 2014-2018), 2028 future year and a 2002 past year and source apportionment was used to isolate the contributions of U.S. anthropogenic emissions to PM concentrations and visibility extinction. A RoP slope line is drawn from the 2002 visibility extinction due to U.S. anthropogenic emissions to zero in 2064 and the CAMx 2028 visibility for U.S. anthropogenic emissions is compared with the RoP slope line at 2028 to determine whether visibility due to U.S. anthropogenic emissions is on a path toward no U.S. manmade impairment in 2064.Implications: The U.S. EPA Regional Haze Rule guidance to show progress toward no U.S. manmade visibility impairment at Class I Areas by 2064 backs into the U.S. manmade impairment contribution by using total atmospheric haze based on measured PM concentrations and subtracting uncertain estimates of routine natural and episodic (i.e. wildfires and windblown dust) natural conditions. The guidance also recommends accounting for visibility contributions due to international anthropogenic and prescribed fire emissions that are also uncertain. This paper presents an alternative approach that models the contributions of U.S. anthropogenic emissions to visibility for past, current and future years using source apportionment to show that U.S. anthropogenic emissions visibility impairment at Class I areas are on a path toward no contribution in 2064. Many U.S. anthropogenic emissions (e.g. power plants with continuous emissions monitoring systems) are better known and characterized than international, fire and natural emissions so the alternative approach should provide a better assessment of whether U.S. anthropogenic emissions are on a path toward no manmade impairment in 2064 than using trends in the measured visibility most impaired days that rely on uncertain estimates of haze due to wildfire, windblown dust, and international emissions and uncertain estimates of natural conditions in 2064.

Unequal airborne exposure to toxic metals associated with race, ethnicity, and segregation in the USA

Persons of color have been exposed to a disproportionate burden of air pollution across the United States for decades. Yet, the inequality in exposure to known toxic elements of air pollution is unclear. Here, we find that populations living in racially segregated communities are exposed to a form of fine particulate matter with over three times higher mass proportions of known toxic and carcinogenic metals. While concentrations of total fine particulate matter are two times higher in racially segregated communities, concentrations of metals from anthropogenic sources are nearly ten times higher. Populations living in racially segregated communities have been disproportionately exposed to these environmental stressors throughout the past decade. We find evidence, however, that these disproportionate exposures may be abated though targeted regulatory action. For example, recent regulations on marine fuel oil not only reduced vanadium concentrations in coastal cities, but also sharply lessened differences in vanadium exposure by segregation.

Phase state of organic aerosols may limit temperature-driven thermodynamic repartitioning following outdoor-to-indoor transport

Ambient aerosols often experience temperature and humidity gradients following outdoor-to-indoor transport, causing organic aerosols (OA) to either gain or lose mass via gas-particle repartitioning. Recent models have sought to quantify these effects using equilibrium partitioning thermodynamics. However, evidence suggests some indoor OA may possess glassy or semisolid phase states with higher viscosities than liquid OA. Characteristic partitioning timescales of higher-viscosity particles are significantly longer than for liquid particles, which may either fully or partially inhibit repartitioning. For outdoor OA experiencing a temperature change during transport indoors, the ultimate repartitioning state depends on the relationship between the gas-particle partitioning rate coefficient (k_gp) of semivolatile organics and the indoor particle loss rate coefficient (l_p). That is, thermodynamic equilibrium partitioning may occur when semivolatile k_gp ≫ l_p, no repartitioning when semivolatile k_gp ≪ l_p, and partial repartitioning when their magnitudes are similar. Longer indoor particle lifetimes, higher particle number, and larger particle sizes all raise k_gp (driving repartitioning towards equilibrium). For simulated U.S. residences, equilibrium condensation was likely reached in humid climate zones during warm meteorological conditions. In colder regions, the degree of evaporative repartitioning depended on whether organics could repartition before the particle phase state adjusts to indoor conditions, which is uncertain. When an appreciable temperature gradient exists, this study not only confirmed that all outdoor-originating OA that is liquid indoors will reach thermodynamic equilibrium, but also concluded that a plurality (46% for this domain) of such OA that is semisolid may also achieve thermodynamic equilibrium during its indoor lifetime.

Review of Filters for Air Sampling and Chemical Analysis in Mining Workplaces

This review considers the use of filters to sample air in mining workplace environments for dust concentration measurement and subsequent analysis of hazardous contaminants, especially respirable crystalline silica (RCS) on filters compatible with wearable personal dust monitors (PDM). The review summarizes filter vendors, sizes, costs, chemical and physical properties, and information available on filter modeling, laboratory testing, and field performance. Filter media testing and selection should consider the characteristics required for mass by gravimetry in addition to RCS quantification by Fourier-transform infrared (FTIR) or Raman spectroscopic analysis. For mass determination, the filters need to have high filtration efficiency (≥99% for the most penetrable particle sizes) and a reasonable pressure drop (up to 16.7 kPa) to accommodate high dust loading. Additional requirements include: negligible uptake of water vapor and gaseous volatile compounds; adequate particle adhesion as a function of particle loading; sufficient particle loading capacity to form a stable particle deposit layer during sampling in wet and dusty environments; mechanical strength to withstand vibrations and pressure drops across the filter; and appropriate filter mass compatible with the tapered element oscillating microbalance. FTIR and Raman measurements require filters to be free of spectral interference. Furthermore, because the irradiated area does not completely cover the sample deposit, particles should be uniformly deposited on the filter.

A Global-Scale Mineral Dust Equation

A robust method to estimate mineral dust mass in ambient particulate matter (PM) is essential, as the dust fraction cannot be directly measured but is needed to understand dust impacts on the environment and human health. In this study, a global-scale dust equation is developed that builds on the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) network's "soil" formula that is based on five measured elements (Al, Si, Ca, Fe, and Ti). We incorporate K, Mg, and Na into the equation using the mineral-to-aluminum (MAL) mass ratio of (K2O + MgO + Na2O)/Al2O3 and apply a correction factor (CF) to account for other missing compounds. We obtain region-specific MAL ratios and CFs by investigating the variation in dust composition across desert regions. To calculate reference dust mass for equation evaluation, we use total-mineral-mass (summing all oxides of crustal elements) and residual-mass (subtracting non-dust species from total PM) approaches. For desert dust in source regions, the normalized mean bias (NMB) of the global equation (within ±1%) is significantly smaller than the NMB of the IMPROVE equation (-6% to 10%). For PM2.5 with high dust content measured by the IMPROVE network, the global equation estimates dust mass well (NMB within ±5%) at most sites. For desert dust transported to non-source regions, the global equation still performs well (NMB within ±2%). The global equation can also represent paved road, unpaved road, and agricultural soil dust (NMB within ±5%). This global equation provides a promising approach for calculating dust mass based on elemental analysis of dust.

Changing Emissions Results in Changed PM2.5 Composition and Health Impacts

In the period of 2005 to 2016, multiple air pollution control regulations have entered into effect in the United States at both the Federal and state level. In addition, economic changes have also occurred primarily in the electricity generation sector that substantially changed the emissions from this sector. This combination of policy implementations and economics has led to substantial reductions in PM2.5, its major constituents, and source specific PM2.5 concentrations across the New York State, particularly those of sulfate, nitrate, and primary organic carbon. However, secondary organic carbon and spark-ignition vehicular emission contributions have increased. Related studies of changes in health outcomes, the excess rates of emergency department visits and hospitalizations for a variety of cardiovascular and respiratory diseases and respiratory infections have increased per unit mass of PM2.5. It appears that the increased toxicity per unit mass was due to the reduction in low toxicity constituents such that the remaining mass had greater impacts on public health.

Improving surface PM2.5 forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi-satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multi chemical transport model (CTM) (GEOS-Chem, WRF-Chem and CMAQ) outputs and ground observations. In part I of the study, a multi-model ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non-rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125 - 300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non-rural areas, referred to as "extended ground truth" or EGT, for today. Lastly, we applied the KF technique to update the bias in the forecast for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three models for both today and next day show the best performance. Together, the two-part study develops a multi-model and multi-AOD bias correction technique that has the potential to improve PM2.5 forecasts in both rural and non-rural areas in near real time, and be readily implemented at state levels.

Biomass Burning Over the United States East Coast and Western North Atlantic Ocean: Implications for Clouds and Air Quality

Biomass burning (BB) aerosol events were characterized over the U.S. East Coast and Bermuda over the western North Atlantic Ocean (WNAO) between 2005 and 2018 using a combination of ground-based observations, satellite data, and model outputs. Days with BB influence in an atmospheric column (BB days) were identified using criteria biased toward larger fire events based on anomalously high AERONET aerosol optical depth (AOD) and MERRA-2 black carbon (BC) column density. BB days are present year-round with more in June-August (JJA) over the northern part of the East Coast, in contrast to more frequent events in March-May (MAM) over the southeast U.S. and Bermuda. BB source regions in MAM are southern Mexico and by the Yucatan, Central America, and the southeast U.S. JJA source regions are western parts of North America. Less than half of the BB days coincide with anomalously high PM2.5 levels in the surface layer, according to data from 14 IMPROVE sites over the East Coast. Profiles of aerosol extinction suggest that BB particles can be found in the boundary layer and into the upper troposphere with the potential to interact with clouds. Higher cloud drop number concentration and lower drop effective radius are observed during BB days. In addition, lower liquid water path is found during these days, especially when BB particles are present in the boundary layer. While patterns are suggestive of cloud-BB aerosol interactions over the East Coast and the WNAO, additional studies are needed for confirmation.

Are standardized diesel exhaust particles (DEP) representative of ambient particles in air pollution toxicological studies?

In this study, we investigated the chemical characteristics of standardized diesel exhaust particles (DEP) and compared them to those of read-world particulate matter (PM) collected in different urban settings to evaluate the extent to which standardized DEPs can represent ambient particles for use in toxicological studies. Standard reference material SRM-2975 was obtained from the National Institute of Standards and Technology (NIST) and was chemically analyzed for the content of elemental carbon (EC), organic carbon (OC), polycyclic aromatic hydrocarbons (PAHs), inorganic ions, and several metals and trace elements. The analysis on the filter-collected DEP sample revealed very high levels of EC (i.e., ~397 ng/μg PM) which were comparable to the OC content (~405 ng/μg PM). This is in contrast with the carbonaceous content in the emitted particles from typical filter-equipped diesel-powered vehicles, in which low levels of EC emissions were observed. Furthermore, the EC mass fraction of the DEP sample did not match the observed levels in the ambient PM of multiple US urban areas, including Los Angeles (8%), Houston (~14%), Pittsburgh (~12%), and New York (~17%). Our results illustrated the lack of several high molecular weight carcinogenic PAHs in the DEP samples, unlike our measurements in major freeways of Los Angeles. Negligible levels of inorganic ions were observed in the sample and the DEP did not contain toxic secondary organic aerosols (SOAs) formed through synchronized reactions in the atmosphere. Lastly, the analysis of redox-active metals and trace elements demonstrated that the levels of many species including vehicle emission tracers (e.g., Ba, Ti, Mn, Fe) on Los Angeles roadways were almost 20 times greater than those in the DEP sample. Based on the abovementioned inconsistencies between the chemical composition of the DEP sample and those of real-world PM measured and recorded in different conditions, we conclude that the standardized DEPs are not suitable representatives of traffic emissions nor typical ambient PM to be used in toxicological studies.

Source apportionment of PM2.5 at IMPROVE monitoring sites within and outside of marine vessel fuel sulfur emissions control areas

Fuel sulfur emissions control areas have been established in a few marine coastal regions to reduce environmental impacts from combustion of high sulfur residual fuel oil (RFO). For example, in August of 2012, the U.S. began enforcing fuel sulfur limits on certain large commercial marine vessels up to 200 nautical miles (nm) of its coasts as part of a North American Emissions Control Area (NA-ECA), and in January of 2014, the U.S. began enforcing fuel sulfur limits on these vessels within up to 50 nm of Puerto Rico and the U.S. Virgin Islands as part of the U.S. Caribbean Sea ECA (USCAR-ECA). This work evaluates ECA effectiveness at reducing PM_2.5 from combustion of RFO by using both spatial analysis, comparing PM_2.5 source apportionment at IMPROVE monitoring sites largely impacted by air from either inside or outside of an ECA, along with temporal analysis, comparing RFO combustion impacts pre and post ECA enforcement at sites largely impacted by air from inside an ECA. Source apportionment was performed using Positive Matrix Factorization (PMF) on chemically speciated PM_2.5 data from 2009 to 2018. Results for 7 coastal U.S. IMPROVE sites influenced by marine air masses within the NA-ECA showed an annual mean reduction of PM_2.5 from RFO combustion of 79.0% (range, 60.2% to 91.5%) when comparing impacts from the pre-ECA (RFO average 2.7% S) period of 2009-2011 to the ECA 0.1% fuel S period of 2015-2018. In contrast, the Virgin Islands, Big Bend, and Baengnyeong Island South Korea IMPROVE sites were impacted by RFO combustion and were largely or wholly influenced by air masses from outside of an emissions control area. These sites saw a statistically significant 14.0% increase, a 21.0% decrease, or no statistically significant change, respectively, when comparing time periods pre and post ECA enforcement.Implications: This study performs source apportionment on PM_2.5 monitoring data to identify 10 sites impacted by residual fuel oil combustion, mainly from marine vessel fuel use. The paper then evaluates the effects of enforcement of marine vessel fuel sulfur emissions control areas at reducing ambient impacts of this source on PM_2.5. This study uses both temporal analysis of the source apportionment results, comparing source impacts before and after enforcement of marine vessel fuel sulfur emissions control areas, and spatial analysis, comparing source impacts between sites largely impacted or not impacted by airmasses originating inside of marine vessel fuel sulfur emissions control areas.

Ambient PM2.5 organic and elemental carbon in New York City: Changing source contributions during a decade of large emission reductions

Fine particle (PM_2.5) exposure is a public health issue affecting millions of people worldwide. In New York State, significant emission reductions occurred during the past decades due to fuel switching, increased renewable energy, and transformations in buildings and transportation. Between 2002 and 2018, anthropogenic emissions of CO, NO_x, SO₂, VOCs, and primary PM_2.5 declined by 58%, 61%, 89%, 47%, and 29%, respectively, in New York and three adjoining states. Ambient PM_2.5 mass concentrations decreased but contributions of source types to changes in PM_2.5 elemental carbon (EC) and organic carbon (OC) are incompletely understood. Receptor modeling was used to estimate changing source contributions to EC and OC in New York City (NYC) between 2007 and 2019. Source identification was facilitated by incorporating measurements of CO, NO, NO₂, O₃, SO_2, and speciated hydrocarbons (1,3-butadiene, n-butane, isobutane, n-pentane, isopentane, isoprene, benzene, toluene, xylenes, acetaldehyde, and formaldehyde). Hydrocarbon species identified mobile-source emissions, evaporative emissions, biogenics, and photochemical secondary organic aerosol. At three study locations, predicted reductions of TC (OC + EC) summed over all source types were 1.3 ± 0.2 μg m^-3, compared with a measured TC reduction of 1.5 ± 0.2 μg m^-3. Declining sulfate concentrations and cleaner mobile sources together reduced the predicted average TC by a combined 1 μg m^-3. Smaller changes occurred in other source contributions, e.g., 0.15 ± 0.01 μg m^-3 reduction likely in response to NYC regulations related to heating fuel oil. Biomass burning PM_2.5 increased between 2007 and 2011, then declined between 2015 and 2019. Reductions contrast with a non-significant increase of 0.05 μg m^-3 in photochemical TC. Further opportunities to decrease PM_2.5 concentrations include wood burning and photochemical-related OC. Continued temporal analysis and source apportionment will be needed to track changes in air quality and source contributions as jurisdictions expand renewable energy and energy efficiency goals.Implications: Large emission reductions that occurred in the eastern U.S. between 2002 and 2019 lowered average fine particle concentrations in New York City by a factor of two. Secondary organic aerosol concentrations declined as sulfate decreased but increased non-significantly with rising ozone. Cleaner mobile-source emissions lowered elemental and organic carbon concentrations. Opportunities for further reductions of PM_2.5 concentrations include biomass burning and photochemical secondary aerosol.

Predicting Spatial Variations in Multiple Measures of Oxidative Burden for Outdoor Fine Particulate Air Pollution across Canada

Fine particulate air pollution (PM_2.5) is a leading contributor to the overall global burden of disease. Traditionally, outdoor PM_2.5 has been characterized using mass concentrations which treat all particles as equally harmful. Oxidative potential (OP) (per μg) and oxidative burden (OB) (per m³) are complementary metrics that estimate the ability of PM_2.5 to cause oxidative stress, which is an important mechanism in air pollution health effects. Here, we provide the first national estimates of spatial variations in multiple measures (glutathione, ascorbate, and dithiothreitol depletion) of annual median outdoor PM_2.5 OB across Canada. To do this, we combined a large database of ground-level OB measurements collected monthly prospectively across Canada for 2 years (2016-2018) with PM_2.5 components estimated using a chemical transport model (GEOS-Chem) and satellite aerosol observations. Our predicted ground-level OB values of all three methods were consistent with ground-level observations (cross-validation R² = 0.63-0.74). We found that forested regions and urban areas had the highest OB, predicted primarily by black carbon and organic carbon from wildfires and transportation sources. Importantly, the dominant components associated with OB were different than those contributing to PM_2.5 mass concentrations (secondary inorganic aerosol); thus, OB metrics may better indicate harmful components and sources on health than the bulk PM_2.5 mass, reinforcing that OB estimates can complement the existing PM_2.5 data in future national-level epidemiological studies.

Brownness of Organic Aerosol over the United States: Evidence for Seasonal Biomass Burning and Photobleaching Effects

Light-absorptivity of organic aerosol may play an important role in visibility and climate forcing, but it has not been assessed as extensively as black carbon (BC) aerosol. Based on multiwavelength thermal/optical analysis and spectral mass balance, this study quantifies BC for the U.S. Interagency Monitoring of Protected Visual Environments (IMPROVE) network while developing a brownness index (γ_Br) for non-BC organic carbon (OC*) to illustrate the spatiotemporal trends of light-absorbing brown carbon (BrC) content. OC* light absorption efficiencies range from 0 to 3.1 m² gC^-1 at 405 nm, corresponding to the lowest and highest BrC content of 0 and 100%, respectively. BC, OC*, and γ_Br explain >97% of the variability of measured spectral light absorption (405-980 nm) across 158 IMPROVE sites. Network-average OC* light absorptions at 405 nm are 50 and 28% those for BC over rural and urban areas, respectively. Larger organic fractions of light absorption occur in winter, partially due to higher organic brownness. Winter γ_Br exhibits a dramatic regional/urban-rural contrast consistent with anthropogenic BrC emissions from residential wood combustion. The spatial differences diminish to uniformly low γ_Br in summer, suggesting effective BrC photobleaching over the midlatitudes. An empirical relationship between BC, ambient temperature, and γ_Br is established, which can facilitate the incorporation of organic aerosol absorptivity into climate and visibility models that currently assume either zero or static organic light absorption efficiencies.

Improving Predictions of Indoor Aerosol Concentrations of Outdoor Origin by Considering the Phase Change of Semivolatile Material Driven by Temperature and Mass-Loading Gradients

Outdoor aerosols experience environmental changes as they are transported indoors, including outdoor-to-indoor temperature and mass-loading gradients, which can reduce or enhance their indoor concentrations due to repartitioning driven by changes in thermodynamic equilibrium states. However, the complexity required to model repartitioning typically hinders its inclusion in studies predicting indoor exposure to ambient aerosols. To facilitate exposure predictions, this work used an explicit thermodynamic indoor aerosol model to simulate outdoor-to-indoor aerosol repartitioning typical for residential and office buildings across the 16 U.S. climate zones over an annual time horizon. Results demonstrate that neglecting repartitioning when predicting indoor concentrations can produce errors of up to 80-100% for hydrocarbon-like organic aerosol, 40-60% for total organic aerosol, 400% for ammonium nitrate, and 60% (typically 3 μg/m³) for the total PM_2.5 aerosol. Underpredictions were more likely for buildings in hotter than colder regions, and for residences than offices, since both cooler indoor air and more meaningful residential organic aerosol concentrations encourage condensation of semivolatile organics. Furthermore, a method for computing correction factors to more easily account for thermodynamic repartitioning is provided. Applying these correction factors to mechanical-only aerosol predictions significantly reduced errors to <0.5 μg/m³ for the total indoor PM_2.5 while bypassing explicit thermodynamic simulations.

Spatiotemporal trends of PM2.5 and its major chemical components at urban sites in Canada

To evaluate the effectiveness of emission control regulations designed for reducing air pollution, chemically resolved PM_2.5 data have been collected across Canada through the National Air Pollution Surveillance network in the past decade. 24-hr time integrated PM_2.5 collected at seven urban and two rural sites during 2010-2016 were analyzed to characterize geographical and seasonal patterns and associated potential causes. Site-specific seven-year mean gravimetric PM_2.5 mass concentrations ranged from 5.7 to 9.6 µg/m³. Seven-year mean concentrations of SO₄^2-, NO₃^-, NH₄⁺, organic carbon (OC), and elemental carbon (EC) were in the range of 0.68 to 1.6, 0.21 to 1.5, 0.27 to 0.71, 1.1 to 1.9, and 0.37 to 0.71 µg /m³, accounting for 10.8%-18.1%, 3.7%-16.7%, 4.7%-7.4%, 18.4%-21.0%, and 6.4%-10.6%, respectively, of gravimetric PM_2.5 mass. PM_2.5 and its five major chemical components showed higher concentrations in southeastern Canada and lower values in Atlantic Canada, with the seven-year mean ratios between the two regions being on the order of 1.7 for PM_2.5 and 1.8-7.1 for its chemical components. When comparing the concentrations between urban and rural sites within the same region, those of SO₄^2- and NH₄⁺ were comparable, while those of NO₃^-, OC, and EC were around 20%, 40%-50%, and 70%-80%, respectively, higher at urban than rural sites, indicating the regional scale impacts of SO₄^2- and NH₄⁺ and effects of local sources on OC and EC. Monthly variations generally showed summertime peaks for SO₄^2- and wintertime peaks for NO₃^-, but those of NH₄⁺, OC, and EC exhibited different seasonality at different locations.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM_2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM_2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM_2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM_2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

Improving Estimates of PM2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm

Improved characterization of ambient PM2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign (2011) are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM2.5 concentrations (R2 value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 μg m-3, and the normalized mean bias (NMB) was lowered from -46.0 to 4.6%). The HSRL-CH method showed statistics (R2=0.75, RMSE=8.6 μgm-3, and NMB=24.0%), which were better than the CMAQ prediction of PM2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM2.5 concentration and chemical composition where HSRL data are available.