Toward Heart-Healthy and Sustainable Cities: A Policy Statement From the American Heart Association

Nearly 56% of the global population lives in cities, with this number expected to increase to 6.6 billion or >70% of the world's population by 2050. Given that cardiometabolic diseases are the leading causes of morbidity and mortality in people living in urban areas, transforming cities and urban provisioning systems (or urban systems) toward health, equity, and economic productivity can enable the dual attainment of climate and health goals. Seven urban provisioning systems that provide food, energy, mobility-connectivity, housing, green infrastructure, water management, and waste management lie at the core of human health, well-being, and sustainability. These provisioning systems transcend city boundaries (eg, demand for food, water, or energy is met by transboundary supply); thus, transforming the entire system is a larger construct than local urban environments. Poorly designed urban provisioning systems are starkly evident worldwide, resulting in unprecedented exposures to adverse cardiometabolic risk factors, including limited physical activity, lack of access to heart-healthy diets, and reduced access to greenery and beneficial social interactions. Transforming urban systems with a cardiometabolic health-first approach could be accomplished through integrated spatial planning, along with addressing current gaps in key urban provisioning systems. Such an approach will help mitigate undesirable environmental exposures and improve cardiovascular and metabolic health while improving planetary health. The purposes of this American Heart Association policy statement are to present a conceptual framework, summarize the evidence base, and outline policy principles for transforming key urban provisioning systems to heart-health and sustainability outcomes.

Health burden from food systems is highly unequal across income groups

Food consumption contributes to the degradation of air quality in regions where food is produced, creating a contrast between the health burden caused by a specific population through its food consumption and that faced by this same population as a consequence of food production activities. Here we explore this inequality within China's food system by linking air-pollution-related health burden from production to consumption, at high levels of spatial and sectorial granularity. We find that low-income groups bear a 70% higher air-pollution-related health burden from food production than from food consumption, while high-income groups benefit from a 29% lower health burden relative to their food consumption. This discrepancy largely stems from a concentration of low-income residents in food production areas, exposed to higher emissions from agriculture. Comprehensive interventions targeting both production and consumption sides can effectively reduce health damages and concurrently mitigate associated inequalities, while singular interventions exhibit limited efficacy.

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.

Ambient traffic related air pollution in relation to ovarian reserve and oocyte quality in young, healthy oocyte donors

Studies in mice and older, subfertile women have found that air pollution exposure may compromise female reproduction. Our objective was to evaluate the effects of air pollution on ovarian reserve and outcomes of ovarian stimulation among young, healthy females. We included 472 oocyte donors who underwent 781 ovarian stimulation cycles at a fertility clinic in Atlanta, Georgia, USA (2008-2019). Antral follicle count (AFC) was assessed with transvaginal ultrasonography and total and mature oocyte count was assessed following oocyte retrieval. Ovarian sensitivity index (OSI) was calculated as the total number of oocytes divided by total gonadotrophin dose × 1000. Daily ambient exposure to nitric oxide (NOx), carbon monoxide (CO), and particulate matter ≤ 2.5 (PM2.5) was estimated using a fused regional + line-source model for near-surface releases at a 250 m resolution based on residential address. Generalized estimating equations were used to evaluate the associations of an interquartile range (IQR) increase in pollutant exposure with outcomes adjusted for donor characteristics, census-level poverty, and meteorological factors. The median (IQR) age among oocyte donors was 25.0 (5.0) years, and 31% of the donors were racial/ethnic minorities. The median (IQR) exposure to NOx, CO, and PM2.5 in the 3 months prior to stimulation was 37.7 (32.0) ppb, 612 (317) ppb, and 9.8 (2.9) µg/m3, respectively. Ambient air pollution exposure in the 3 months before AFC was not associated with AFC. An IQR increase in PM2.5 in the 3 months before AFC and during stimulation was associated with -7.5% (95% CI -14.1, -0.4) and -6.4% (95% CI -11.0, -1.6) fewer mature oocytes, and a -1.9 (95% CI -3.2, -0.5) and -1.0 (95% CI -1.8, -0.2) lower OSI, respectively. Our results suggest that lowering the current 24-h PM2.5 standard in the US to 25 µg/m3 may still not adequately protect against the reprotoxic effects of short-term PM2.5 exposure.

Associations between source-apportioned PM2.5 and 30-day readmissions in heart failure patients

BACKGROUND

Air pollution exposure is a significant risk factor for morbidity and mortality, especially for those with pre-existing chronic disease. Previous studies highlighted the risks that long-term particulate matter exposure has for readmissions. However, few studies have evaluated source and component specific associations particularly among vulnerable patient populations.

OBJECTIVES

Use electronic health records from 5556 heart failure (HF) patients diagnosed between July 5, 2004 and December 31, 2010 that were part of the EPA CARES resource in conjunction with modeled source-specific fine particulate matter (PM_2.5) to estimate the association between exposure to source and component apportioned PM_2.5 at the time of HF diagnosis and 30-day readmissions.

METHODS

We used zero-inflated mixed effects Poisson models with a random intercept for zip code to model associations while adjusting for age at diagnosis, year of diagnosis, race, sex, smoking status, and neighborhood socioeconomic status. We undertook several sensitivity analyses to explore the impact of geocoding precision and other factors on associations and expressed associations per interquartile range increase in exposures.

RESULTS

We observed associations between 30-day readmissions and an interquartile range increase in gasoline- (16.9% increase; 95% confidence interval = 4.8%, 30.4%) and diesel-derived PM_2.5 (9.9% increase; 95% confidence interval = 1.7%, 18.7%), and the secondary organic carbon component of PM_2.5 (SOC; 20.4% increase; 95% confidence interval = 8.3%, 33.9%). Associations were stable in sensitivity analyses, and most consistently observed among Black study participants, those in lower income areas, and those diagnosed with HF at an earlier age. Concentration-response curves indicated a linear association for diesel and SOC. While there was some non-linearity in the gasoline concentration-response curve, only the linear component was associated with 30-day readmissions.

DISCUSSION

There appear to be source specific associations between PM_2.5 and 30-day readmissions particularly for traffic-related sources, potentially indicating unique toxicity of some sources for readmission risks that should be further explored.

Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates

Annual fine particulate matter (PM_2.5) mass concentrations in the South Coast Air Basin (SoCAB) of California decreased from around 30 μg/m³ to 11 μg/m³ between 2000 and 2013 but rose from 11 μg/m³ to 13 μg/m³ between 2014 and 2018, raising important questions about the effectiveness of ongoing emission control policies. A two-step generalized additive model (GAM)-least squares approach was developed to explore the effects of emissions, large-scale climate events and meteorological factors on daily PM_2.5 mass concentrations from 2000 to 2019 to quantitatively link impacts of emissions and meteorological on PM_2.5 and to assess factors leading to the increase. The GAM had an R² = 0.99 and root mean square error (RMSE) = 0.7 μg/m³ for the annual average PM_2.5 concentrations. The two-step method had an R² = 0.93 and RMSE = 4.07 μg/m³ for the 98th percentile 24-hr average PM_2.5 concentrations. Variations in both emissions and relative humidity were of high importance compared with other included factors. Interactions of NH₃ emissions with NOx and SO₂ emissions, which lead to ammonium nitrate and sulfate aerosol formation, were the most important factors. Meteorological effects on PM_2.5 explained the majority of the daily PM_2.5 fluctuations. Emission changes (increases in SO₂ and PM_2.5) led to increases in predicted PM_2.5 between 2014 and 2018. Predicted future PM_2.5, using projected emissions and meteorological data from model simulations of representative concentration pathway (RCP) scenarios, are around 12 μg/m³ (annual) and 30 μg/m³ (98th percentile daily), which are both close to the current National Ambient Air Quality Standards (NAAQS) for PM_2.5. Meteorological impacts on the predicted PM_2.5 in future years lead to variations of ±2 μg/m³ for the annual average and ±5 μg/m³ for the 98th percentile daily level. Future climate changes lead to a probable year-to-year variation that will let PM_2.5 levels in some years exceed the standard.

Emissions and meteorological impacts on PM2.5 species concentrations in Southern California using generalized additive modeling

The chemical composition of PM_2.5 has a significant impact on human health and air quality, and its accurate knowledge can be used to identify contributing emission sources. Assessing and quantifying the impacts of various factors (e.g., emissions, meteorology, and large-scale climate patterns) on the main PM_2.5 chemical components can give guidance for implementing effective regulations to improve air quality in the future. In this study, we developed generalized additive models (GAMs) to assess how emissions, meteorological factors, and large-scale climate indices affected ammonium, sulfate, nitrate, elemental carbon, and organic carbon from 2002 to 2019 in the South Coast Air Basin (SoCAB). Concentration trends from three sites in the SoCAB are studied. The statistical results showed that GAMs can capture the variability of these species' daily concentrations (R² = 0.6 to 0.7) and annual concentrations (R² = 0.93 to 0.99). Precursor emissions most significantly affect PM_2.5 species production, though meteorological factors like maximum temperature, relative humidity, wind speed, and boundary layer height, also influence PM_2.5 composition. In the future, these meteorological factors will become more significant in affecting PM_2.5 speciation, although emissions will continue to strongly affect formation. Results show that the composition of most PM_2.5 species will decrease in the future except for OC, which will become the largest contributor to PM_2.5.

Abundant nitrogen oxide and weakly acidic environment synergistically promote daytime particulate nitrate pollution

Nitrate is formed through the chemical production of gas-phase nitric acid and subsequent partitioning to the aerosol phase during the daytime. Many studies in the past separated these two aspects, even though they occur simultaneously in the atmosphere. To better understand the nitrate formation mechanism and effectively mitigate its production, it is necessary to consider the synergy between these two mechanisms. For this, we analyze hourly-speciated ambient observations data, with EK&TMA (Empirical Kinetic & Thermodynamic Modeling Approach) map to comprehensively explore the factors controlling nitrate production. Results show that precursor NO₂ concentration and aerosol pH, which are related to anthropogenic activities, are the two major factors for chemical kinetics production and gas/particle thermodynamic partitioning processes respectively. Abundant NO₂ and weakly acidic environments are favorable conditions for daytime particulate nitrate pollution, thus collaborative control of coal source, vehicle source, and dust source is needed to alleviate nitrate pollution.

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses

Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NO_x), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4-69%, 14-65%, and 21-97% of BC, NO_x, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26-46%, 17-63%, and -7-18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NO_x to CO and BC to NO_x emission ratios, which were between 0.21-0.32 μg m^-3 NO_x (μg m^-3 CO)^-1 and 0.013-0.018 μg m^-3 BC (μg m^-3 NO_x)^-1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes.Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

Catalytic CO2 Capture via Ultrasonically Activating Dually Functionalized Carbon Nanotubes

High energy consumption and high cost have been the obstacles for large-scale deployment of all state-of-the-art CO₂ capture technologies. Finding a transformational way to improve mass transfer and reaction kinetics of the CO₂ capture process is timely for reducing carbon footprints. In this work, commercial single-walled carbon nanotubes (CNTs) were activated with nitric acid and urea under ultrasonication and hydrothermal methods, respectively, to prepare N-doped CNTs with the functional group of -COOH, which possesses both basic and acid functionalities. The chemically modified CNTs with a concentration of 300 ppm universally catalyze both CO₂ sorption and desorption of the CO₂ capture process. The increases in the desorption rate achieved with the chemically modified CNTs can reach as high as 503% compared to that of the sorbent without the catalyst. A chemical mechanism underlying the catalytic CO₂ capture is proposed based on the experimental results and further confirmed by density functional theory computations.

Acute Effects of Ambient Air Pollution on Asthma Emergency Department Visits in Ten U.S. States

BACKGROUND

Previous studies of short-term ambient air pollution exposure and asthma morbidity in the United States have been limited to a small number of cities and/or pollutants and with limited consideration of effects across ages.

OBJECTIVES

To estimate acute age group-specific effects of fine and coarse particulate matter (PM), major PM components, and gaseous pollutants on emergency department (ED) visits for asthma during 2005-2014 across the United States.

METHODS

We acquired ED visit and air quality data in regions surrounding 53 speciation sites in 10 states. We used quasi-Poisson log-linear time-series models with unconstrained distributed exposure lags to estimate site-specific acute effects of air pollution on asthma ED visits overall and by age group (1-4, 5-17, 18-49, 50-64, and 65+ y), controlling for meteorology, time trends, and influenza activity. We then used a Bayesian hierarchical model to estimate pooled associations from site-specific associations.

RESULTS

Our analysis included 3.19 million asthma ED visits. We observed positive associations for multiday cumulative exposure to all air pollutants examined [e.g., 8-d exposure to PM2.5: rate ratio of 1.016 with 95% credible interval (CI) of (1.008, 1.025) per 6.3-μg/m3 increase, PM10-2.5: 1.014 (95% CI: 1.007, 1.020) per 9.6-μg/m3 increase, organic carbon: 1.016 (95% CI: 1.009, 1.024) per 2.8-μg/m3 increase, and ozone: 1.008 (95% CI: 0.995, 1.022) per 0.02-ppm increase]. PM2.5 and ozone showed stronger effects at shorter lags, whereas associations of traffic-related pollutants (e.g., elemental carbon and oxides of nitrogen) were generally stronger at longer lags. Most pollutants had more pronounced effects on children (<18 y old) than adults; PM2.5 had strong effects on both children and the elderly (>64 y old); and ozone had stronger effects on adults than children.

CONCLUSIONS

We reported positive associations between short-term air pollution exposure and increased rates of asthma ED visits. We found that air pollution exposure posed a higher risk for children and older populations. https://doi.org/10.1289/EHP11661.

[EMmim][NTf2 ]-a Novel Ionic Liquid (IL) in Catalytic CO2 Capture and ILs' Applications

Ionic liquids (ILs) have been used for carbon dioxide (CO₂ ) capture, however, which have never been used as catalysts to accelerate CO₂ capture. The record is broken by a uniquely designed IL, [EMmim][NTf₂ ]. The IL can universally catalyze both CO₂ sorption and desorption of all the chemisorption-based technologies. As demonstrated in monoethanolamine (MEA) based CO₂ capture, even with the addition of only 2000 ppm IL catalyst, the rate of CO₂ desorption-the key to reducing the overall CO₂ capture energy consumption or breaking the bottleneck of the state-of-the-art technologies and Paris Agreement implementation-can be increased by 791% at 85 °C, which makes use of low-temperature waste heat and avoids secondary pollution during CO₂ capture feasible. Furthermore, the catalytic CO₂ capture mechanism is experimentally and theoretically revealed.

Chemical looping based ammonia production-A promising pathway for production of the noncarbon fuel

Ammonia, primarily made with Haber-Bosch process developed in 1909 and winning two Nobel prizes, is a promising noncarbon fuel for preventing global warming of 1.5 °C above pre-industrial levels. However, the undesired characteristics of the process, including high carbon footprint, necessitate alternative ammonia synthesis methods, and among them is chemical looping ammonia production (CLAP) that uses nitrogen carrier materials and operates at atmospheric pressure with high product selectivity and energy efficiency. To date, neither a systematic review nor a perspective in nitrogen carriers and CLAP has been reported in the critical area. Thus, this work not only assesses the previous results of CLAP but also provides perspectives towards the future of CLAP. It classifies, characterizes, and holistically analyzes the fundamentally different CLAP pathways and discusses the ways of further improving the CLAP performance with the assistance of plasma technology and artificial intelligence (AI).

Neighborhood characteristics as confounders and effect modifiers for the association between air pollution exposure and subjective cognitive functioning

BACKGROUND

Air pollution has been associated with cognitive function in the elderly. Previous studies have not evaluated the simultaneous effect of neighborhood-level socioeconomic status (N-SES), which can be an essential source of bias.

OBJECTIVES

We explored N-SES as a confounder and effect modifier in a cross-sectional study of air pollution and subjective cognitive function.

METHODS

We included 12,058 participants age 50+ years from the Emory Healthy Aging Study in Metro Atlanta using the Cognitive Function Instrument (CFI) score as our outcome, with higher scores representing worse subjective cognitive function. We estimated 9-year average ambient carbon monoxide (CO), nitrogen oxides (NOx), and fine particulate matter (PM2.5) concentrations at residential addresses using a fusion of dispersion and chemical transport models. We collected census-tract level N-SES indicators and created two composite measures via principal component analysis and k-means clustering. Associations between pollutants and CFI and effect modification by N-SES were estimated via linear regression models adjusted for age, education, race and N-SES.

RESULTS

N-SES confounded the association between air pollution and CFI, independent of individual characteristics. We found significant effect modifications by N-SES for the association between air pollution and CFI (p-values<0.001) suggesting that effects of air pollution differ depending on N-SES. Participants living in areas with low N-SES were most vulnerable to air pollution. In the lowest N-SES urban areas, interquartile range (IQR) increases in CO, NOx, and PM2.5 were associated with 5.4% (95%-confidence interval, -0.2,11.3), 4.9% (-0.4,10.4), and 9.8% (2.2,18.0) changes in CFI, respectively. In lowest N-SES suburban areas, IQR increases in CO, NOx, and PM2.5 were associated with higher changes in CFI, namely 13.0% (0.9,26.5), 13.0% (-0.1,27.8), and 17.3% (2.5,34.2), respectively.

DISCUSSION

N-SES is an important confounder and effect modifier in our study. This finding could have implications for studying health effects of air pollution and identifying susceptible populations.

Separating emissions and meteorological impacts on peak ozone concentrations in Southern California using generalized additive modeling

Ozone levels have been declining in the Los Angeles, CA, USA area for the last four decades, but there was a recent uptick in the 4th highest daily maximum 8-h (MDA8) ozone concentrations from 2014 to 2018 despite continued reductions in the estimated precursor emissions. In this study, we assess the emissions and meteorological impacts on the 4th highest MDA8 ozone concentrations to better understand the factors affecting the observed MDA8 ozone using a two-step generalized additive model (GAM)/least squares approach applied to the South Coast Air Basin (SoCAB) for the 1990 to 2019 period. The GAM model includes emissions, meteorological factors, large-scale climate variables, date, and the interactions between meteorology and emissions. A least squares method was applied to the GAM output to better capture the 4th highest MDA8 ozone. The resulting two-step model had an R² of 0.98 and a slope of 1 between the observed and predicted 4th highest MDA8 ozone. Emissions and the interactions between the maximum temperature and emissions explain most of the variation in the peak MDA8 ozone concentrations. Declining emissions have lowered the 4th highest MDA8 ozone concentration. Meteorology explains the higher than expected 4th-high, ozone levels observed in 2014-2018, indicating that meteorology was a stronger forcer than the continued reductions in emissions during that time period. The model was applied to estimate future ozone levels. Meteorology developed from climate modeling of the representative concentration pathway (RCP) scenarios, and two sets of emissions were used in the application. The modeling results indicated climate trends will push ozone levels slightly higher if no further emissions reductions are realized and that of two emissions trajectories modeled, the more stringent is required to reliably meet the federal ozone standard given annual meteorological variability.

Targeting Atmospheric Oxidants Can Better Reduce Sulfate Aerosol in China: H2O2 Aqueous Oxidation Pathway Dominates Sulfate Formation in Haze

Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO₂ in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol. The importance of ionic strength to sulfate multiphase chemistry has been verified under laboratory conditions, though studies in the actual atmosphere are still limited. By utilizing online observations and developing an improved solute strength-dependent chemical thermodynamics and kinetics model (EF-T&K model, EF is the enhancement factor that represents the effect of ionic strength on an aerosol aqueous-phase reaction), we provided quantitative evidence that the H₂O₂ pathway was enhanced nearly 100 times and dominated sulfate formation for entire years (66%) in Tianjin (a northern city in China). TMI (oxygen catalyzed by transition-metal ions) (14%) and NO₂ (14%) pathways got the second-highest contributions. Machine learning supported the result that aerosol sulfate production was more affected by the H₂O₂ pathway. The collaborative effects of atmospheric oxidants and SO₂ on sulfate aerosol production were further investigated using the improved EF-T&K model. Our findings highlight the effectiveness of adopting target oxidant control as a new direction for sustainable mitigation of sulfate, given the already low SO₂ concentrations in China.

Impact of Circular, Waste-Heat Reuse Pathways on PM2.5-Air Quality, CO2 Emissions, and Human Health in India: Comparison with Material Exchange Potential

India is home to 1.3 billion people who are exposed to some of the highest levels of ambient air pollution in the world. In addition, India is one of the fastest-growing carbon-emitting countries. Here, we assess how two strategies to reuse waste-heat from coal-fired power plants and other large sources would impact PM_2.5-air quality, human health, and CO₂ emissions in 2015 and a future year, 2050, using varying levels of policy adoption (current regulations, proposed single-sector policies, and ambitious single-sector strategies). We find that power plant and industrial waste-heat reuse as input to district heating systems (DHSs), a novel, multisector strategy to reduce local biomass burning for heating emissions, can offset 71.3-85.2% of residential heating demand in communities near a power plant (9.3-12.4% of the nationwide heating demand) with the highest benefits observed during winter months in areas with collocated industrial activity and higher residential heating demands (e.g., New Delhi). Utilizing waste-heat to generate electricity via organic Rankine cycles (ORCs) can generate an additional 22 (11% of total coal-fired generating capacity), 41 (8%), 32 (13%), and 6 (5%) GW of electricity capacity in the 2015, 2050-current regulations, 2050-single-sector, and 2050-ambitious-single-sector scenarios, respectively. Emission estimates utilizing these strategies were input to the GEOS-Chem model, and population-weighted, simulated PM_2.5 showed small improvements in the DHS (0.2-0.4%) and ORC (0.3-3.4%) scenarios, where the minimal DHS PM_2.5-benefit is attributed to the small contribution of biomass burning for heating to nationwide PM_2.5 emissions (much of the biomass burning activity is for cooking). The PM_2.5 reductions lead to ∼130-36,000 mortalities per year avoided among the scenarios, with the largest health benefits observed in the ORC scenarios. Nationwide CO₂ emissions reduced <0.04% by DHSs but showed larger reductions using ORCs (1.9-7.4%). Coal fly-ash as material exchange in cement and brick production was assessed, and capacity exists to completely reutilize unused fly-ash toward cement and brick production in each of the scenarios.

The Oxidative Potential of Fine Particulate Matter and Biological Perturbations in Human Plasma and Saliva Metabolome

Particulate oxidative potential may comprise a key health-relevant parameter of particulate matter (PM) toxicity. To identify biological perturbations associated with particulate oxidative potential and examine the underlying molecular mechanisms, we recruited 54 participants from two dormitories near and far from a congested highway in Atlanta, GA. Fine particulate matter oxidative potential ("FPMOP") levels at the dormitories were measured using dithiothreitol assay. Plasma and saliva samples were collected from participants four times for longitudinal high-resolution metabolic profiling. We conducted metabolome-wide association studies to identify metabolic signals with FPMOP. Leukotriene metabolism and galactose metabolism were top pathways associated with ≥5 FPMOP-related indicators in plasma, while vitamin E metabolism and leukotriene metabolism were found associated with most FPMOP indicators in saliva. We observed different patterns of perturbed pathways significantly associated with water-soluble and -insoluble FPMOPs, respectively. We confirmed five metabolites directly associated with FPMOP, including hypoxanthine, histidine, pyruvate, lactate/glyceraldehyde, and azelaic acid, which were implications of perturbations in acute inflammation, nucleic acid damage and repair, and energy perturbation. The unique metabolic signals were specific to FPMOP, but not PM mass, providing initial indication that FPMOP might constitute a more sensitive, health-relevant measure for elucidating etiologies related to PM_2.5 exposures.

Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA

A number of studies have found differing associations of disease outcomes with PM_2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO₂, NO_x, O₃, and SO₂), total PM_2.5 mass, and speciated PM_2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM_2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM_2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

Global Emissions of Hydrogen Chloride and Particulate Chloride from Continental Sources

Gaseous and particulate chlorine species play an important role in modulating tropospheric oxidation capacity, aerosol water uptake, visibility degradation, and human health. The lack of recent global continental chlorine emissions has hindered modeling studies of the role of chlorine in the atmosphere. Here, we develop a comprehensive global emission inventory of gaseous HCl and particulate Cl- (pCl), including 35 sources categorized in six source sectors based on published up-to-date activity data and emission factors. These emissions are gridded at a spatial resolution of 0.1° × 0.1° for the years 1960 to 2014. The estimated emissions of HCl and pCl in 2014 are 2354 (1661-3201) and 2321 (930-3264) Gg Cl a-1, respectively. Emissions of HCl are mostly from open waste burning (38%), open biomass burning (19%), energy (19%), and residential (13%) sectors, and the major sources classified by fuel type are combustion of waste (43%), biomass (32%), and coal (25%). Emissions of pCl are mostly from biofuel (29%) and open biomass burning processes (44%). The sectoral and spatial distributions of HCl and pCl emissions are very heterogeneous along the study period, and the temporal trends are mainly driven by the changes in emission factors, energy intensity, economy, and population.

Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, and PM2.5 constituents: SO42-, NO3-, NH4+, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R2 ranged from 0.45 to 0.96, depending upon pollutant. Additional analysis found that temporal R2 ranged from 0.84 to 0.99 and spatial R2 values ranged from 0.76 to 0.96 across species. Four-fold cross-validation was performed to assess the model's predictive power, and ranged from 0.40 for PM10 to 0.94 for SO4 with other pollutants falling within this range. Largest improvements were found for PM10 which had substantial bias in the CMAQ fields that varied east-to-west; smallest improvements were for SO4 which was already well simulated. The random forest model results to correct the simulation biases, while largely consistent year-to-year, did show slight variation due in part to changes in the distribution of monitors and changes in CMAQ simulation inputs.

Assessment of Airport-Related Emissions and Their Impact on Air Quality in Atlanta, GA, Using CMAQ and TROPOMI

Impacts of emissions from the Atlanta Hartsfield-Jackson Airport (ATL) on ozone (O₃), ultrafine particulates (UFPs), and fine particulate matter (PM_2.5) are evaluated using the Community Multiscale Air Quality (CMAQ) model and high-resolution satellite observations of NO₂ vertical column densities (VCDs) from TROPOMI. Two airport inventories are compared: an inventory using emissions where landing and take-off (LTO) processes are allocated to the surface (default) and a modified (3D) inventory that has LTO and cruise emissions vertically and horizontally distributed, accounting for aircraft climb and descend rates. The 3D scenario showed reduced bias and error between CMAQ and TROPOMI VCDs compared to the default scenario [i.e., normalized mean bias: -43%/-46% and root mean square error: 1.12/1.21 (1015 molecules/cm²)]. Close agreement of TROPOMI-derived observations to modeled NO₂ VCDs from two power plants with continuous emissions monitors was found. The net effect of aviation-related emissions was an increase in UFP (j mode in CMAQ), PM_2.5 (i + j mode), and O₃ concentrations by up to 6.5 × 10² particles/cm³ (∼38%), 0.7 μg/m³ (∼8%), and 2.7 ppb (∼4%), respectively. Overall, the results show (1) that the spatial allocation of airport emissions has notable effects on air quality modeling results and will be of further importance as airports become a larger part of the total urban emissions and (2) the applicability of high-resolution satellite retrievals to better understand emissions from facilities such as airports.

The response of streams in the Adirondack region of New York to projected changes in sulfur and nitrogen deposition under changing climate

Modeling studies project that in the future surface waters in the northeast US will continue to recover from acidification over decades following reductions in atmospheric sulfur dioxide and nitrogen oxides emissions. However, these studies generally assume stationary climatic conditions over the simulation period and ignore the linkages between soil and surface water recovery from acid deposition and changing climate, despite fundamental impacts to watershed processes and comparable time scales for both phenomena. In this study, the integrated biogeochemical model PnET-BGC was applied to two montane forest watersheds in the Adirondack region of New York, USA to evaluate the recovery of surface waters from historical acidification in response to possible future changes in climate and atmospheric sulfur and nitrogen deposition. Statistically downscaled climate scenarios on average project warmer temperatures and greater precipitation for the Adirondack by the end of the century. Model simulations suggest under constant climate, acid-sensitive Buck Creek would gain 12.8 μeq L^-1 of acid neutralizing capacity (ANC) by 2100 from large reductions in deposition, whereas acid insensitive Archer Creek is projected to gain 7.9 μeq L^-1 of ANC. However, climate change could limit those improvements in acid-base status. Under climate change, a negative offset relative to the ANC increases with no climate change are projected for both streams by 2100. In acid-insensitive Archer Creek the negative offset (-8.5 μeq L^-1) was large enough that ANC is projected to decrease by -0.6 μeq L^-1, whereas in acid-sensitive Buck Creek, the negative offset (-0.4 μeq L^-1) resulted in a slight decline of the projected future ANC increase to 12.4 μeq L^-1. Calculated target loads for 2150 for both sites decreased when future climate change was considered in model simulations, which suggests further reductions in acid deposition may be necessary to restore ecosystem structure and function under a changing climate.

Novel Method for Ozone Isopleth Construction and Diagnosis for the Ozone Control Strategy of Chinese Cities

Ozone (O₃) isopleths describe the nonlinear responses of O₃ concentrations to changes in nitrogen oxides (NO_X) and volatile organic compounds (VOCs) and thus are pivotal to the determination of O₃ control requirements. In this study, we innovatively use the Community Multiscale Air Quality model with the high-order decoupled direct method (CMAQ-HDDM) to simulate O₃ pollution of China in 2017 and derive O₃ isopleths for individual cities. Our simulation covering the entire China Mainland suggests severe O₃ pollution as 97% of the residents experienced at least 1 day, in 2017, in excess of Chinese Level-II Ambient Air Quality Standards for O₃ as 160 μg·m^-3 (81.5 ppbV equally). The O₃ responses to emissions of precursors vary widely across individual cities. Densely populated metropolitan areas such as Jing-Jin-Ji, Yangtze River Delta, and Pearl River Delta are following NO_X-saturated regimes, where a small amount of NO_X reduction increases O₃. Ambient O₃ pollution in the eastern region generally is limited by VOCs, while in the west by NO_X. The city-specific O₃ isopleths generated in this study are instrumental in forming hybrid and differentiated strategies for O₃ abatement in China.

Satellite Monitoring for Air Quality and Health

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM_2.5) and nitrogen dioxide (NO₂). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA

Prescribed burning (PB) is a prominent source of PM_2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM_2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM_2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM_2.5 concentrations. Simulated PM_2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM_2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM_2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R² of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM_2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM_2.5 fields to provide better estimates of hourly exposures to smoke from PB.Implications: Prescribed burning emissions are a major cause of elevated PM_2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM_2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.

Evaluation of PM2.5 air pollution sources and cardiovascular health

Long-term air pollution exposure, notably fine particulate matter, is a global contributor to morbidity and mortality and a known risk factor for coronary artery disease (CAD) and myocardial infarctions (MI). Knowledge of impacts related to source-apportioned PM2.5 is limited. New modeling methods allow researchers to estimate source-specific long-term impacts on the prevalence of CAD and MI. The Catheterization Genetics (CATHGEN) cohort consists of patients who underwent a cardiac catheterization at Duke University Medical Center between 2002 and 2010. Severity of coronary blockage was determined by coronary angiography and converted into a binary indicator of clinical CAD. History of MI was extracted from medical records. Annual averages of source specific PM2.5 were estimated using an improved gas-constrained source apportionment model for North Carolina from 2002 to 2010. We tested six sources of PM2.5 mass for associations with CAD and MI using mixed effects multivariable logistic regression with a random intercept for county and multiple adjustments. PM2.5 fractions of ammonium bisulfate and ammonium nitrate were associated with increased prevalence of CAD (odds ratio [OR] 1.20; 95% CI = 1.11, 1.22 and OR 1.18; 95% CI = 1.05, 1.32, respectively). PM2.5 from ammonium bisulfate and ammonium nitrate were also associated with increased prevalence of MI (OR 1.20; 95% CI = 1.10, 1.29 and OR 1.35; 95% CI = 1.20, 1.53, respectively). Greater PM2.5 concentrations of ammonium bisulfate and ammonium nitrate are associated with greater MI and CAD prevalence. The association with bisulfate suggests aerosol acidity may play a role. Our findings suggest analyses of source specific PM2.5 mass can reveal novel associations.

Significant contrasts in aerosol acidity between China and the United States

Aerosol acidity governs several key processes in aerosol physics and chemistry, thus affecting aerosol mass and composition and ultimately climate and human health. Previous studies have reported aerosol pH values separately in China and the United States (USA), implying different aerosol acidity between these two countries. However, there is debate about whether mass concentration or chemical composition is the more important driver of differences in aerosol acidity. A full picture of the pH difference and the underlying mechanisms responsible is hindered by the scarcity of simultaneous measurements of particle composition and gaseous species, especially in China. Here we conduct a comprehensive assessment of aerosol acidity in China and the USA using extended ground-level measurements and regional chemical transport model simulations. We show that aerosols in China are significantly less acidic than in the USA, with pH values 1-2 units higher. Based on a proposed multivariable Taylor series method and a series of sensitivity tests, we identify major factors leading to the pH difference. Compared to the USA, China has much higher aerosol mass concentrations (gas + particle, by a factor of 8.4 on average) and a higher fraction of total ammonia (gas + particle) in the aerosol composition. Our assessment shows that the differences in mass concentrations and chemical composition play equally important roles in driving the aerosol pH difference between China and the USA - increasing the aerosol mass concentrations (by a factor of 8.4) but keeping the relative component contributions the same in the USA as the level in China increases the aerosol pH by ~1.0 units and further shifting the chemical composition from US conditions to China's that are richer in ammonia increases the aerosol pH by ~0.9 units. Therefore, China being both more polluted than the USA and richer in ammonia explains the aerosol pH difference. The difference in aerosol acidity highlighted in the present study implies potential differences in formation mechanisms, physicochemical properties, and toxicity of aerosol particles in these two countries.

Low-Molecular-Weight Carboxylic Acids in the Southeastern U.S.: Formation, Partitioning, and Implications for Organic Aerosol Aging

While carboxylic acids are important components in both particle and gas phases in the atmosphere, their sources and partitioning are not fully understood. In this study, we present real-time measurements of both particle- and gas-phase concentrations for five of the most common and abundant low-molecular-weight carboxylic acids (LMWCA) in a rural region in the southeastern U.S. in Fall 2016. Through comparison with secondary organic aerosol (SOA) tracers, we find that isoprene was the most important local precursor for all five LMWCA but via different pathways. We propose that monocarboxylic acids (formic and acetic acids) were mainly formed through gas-phase photochemical reactions, while dicarboxylic acids (oxalic, malonic, and succinic acids) were predominantly from aqueous processing. Unexpectedly high concentrations of particle-phase formic and acetic acids (in the form of formate and acetate, respectively) were observed and likely the components of long-range transport organic aerosol (OA), decoupled from their gas-phase counterparts. In addition, an extraordinarily strong correlation (R² = 0.90) was observed between a particulate LMWCA and aged SOA, which we tentatively attribute to boundary layer dynamics.

Estimating US Background Ozone Using Data Fusion

US background (US-B) ozone (O3) is the O3 that would be present in the absence of US anthropogenic (US-A) emissions. US-B O3 varies by location and season and can make up a large, sometimes dominant, portion of total O3. Typically, US-B O3 is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O3. We develop and apply a method to fuse observed O3 with US-B O3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O3. Trends in O3 bias are explored across different simulation years and varying model scales. We found that the CTM US-B O3 estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O3 was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O3. Across the four estimates, we found annual mean CTM-simulated US-B O3 ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O3 ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.

Increased air pollution exposure among the Chinese population during the national quarantine in 2020

The COVID-19 quarantine in China is thought to have reduced ambient air pollution. The overall exposure of the population also depends, however, on indoor air quality and human mobility and activities. Here, by integrating real-time mobility data and a questionnaire survey on time-activity patterns during the pandemic, we show that despite a decrease in ambient PM_2.5 during the quarantine, the total population-weighted exposure to PM_2.5 considering both indoor and outdoor environments increased by 5.7 μg m^-3 (95% confidence interval, 1.2-11.0 μg m^-3). The increase in population-weighted exposure was mainly driven by a nationwide urban-to-rural population migration before the Spring Festival coupled with the freezing of the migration backward due to the quarantine, which increased household energy consumption and the fraction of people exposed to rural household air pollution indoors. Our analysis reveals an increased inequality of air pollution exposure during the quarantine and highlights the importance of household air pollution for population health in China.

Four Decades of United States Mobile Source Pollutants: Spatial-Temporal Trends Assessed by Ground-Based Monitors, Air Quality Models, and Satellites

On-road emissions sources degrade air quality, and these sources have been highly regulated. Epidemiological and environmental justice studies often use road proximity as a proxy for traffic-related air pollution (TRAP) exposure, and other studies employ air quality models or satellite observations. To assess these metrics' abilities to reproduce observed near-road concentration gradients and changes over time, we apply a hierarchical linear regression to ground-based observations, long-term air quality model simulations using Community Multiscale Air Quality (CMAQ), and satellite products. Across 1980-2019, observed TRAP concentrations decreased, and road proximity was positively correlated with TRAP. For all pollutants, concentrations decreased fastest at locations with higher road proximity, resulting in "flatter" concentration fields in recent years. This flattening unfolded at a relatively constant rate for NO_x, whereas the flattening of CO concentration fields has slowed. CMAQ largely captures observed spatial-temporal NO₂ trends across 2002-2010 but overstates the relationships between CO and elemental carbon fine particulate matter (EC) road proximity. Satellite NO_x measures overstate concentration reductions near roads. We show how this perspective provides evidence that California's on-road vehicle regulations led to substantial decreases in NO₂, NO_x, and EC in California, with other states that adopted California's light-duty automobile standards showing mixed benefits over states that did not adopt these standards.

Increased air pollution exposure among the Chinese population during the national quarantine in 2020

The COVID-19 quarantine in China is thought to have reduced ambient air pollution. The overall exposure of the population also depends, however, on indoor air quality and human mobility and activities. Here, by integrating real-time mobility data and a questionnaire survey on time-activity patterns during the pandemic, we show that despite a decrease in ambient PM_2.5 during the quarantine, the total population-weighted exposure to PM_2.5 considering both indoor and outdoor environments increased by 5.7 μg m^-3 (95% confidence interval, 1.2-11.0 μg m^-3). The increase in population-weighted exposure was mainly driven by a nationwide urban-to-rural population migration before the Spring Festival coupled with the freezing of the migration backward due to the quarantine, which increased household energy consumption and the fraction of people exposed to rural household air pollution indoors. Our analysis reveals an increased inequality of air pollution exposure during the quarantine and highlights the importance of household air pollution for population health in China.

Individual and population level protection from particulate matter exposure by wearing facemasks

Because of the severe air pollution in northern China, facemasks have gained popularity in this area in recent years. Although the results of previous studies have shown the effectiveness of wearing facemasks for intercepting particles, the individual differences and the overall health benefits of wearing facemasks have not been comprehensively documented. In this study, using both model and personal tests under various conditions, we test eight major brands of facemasks for their removal efficiencies (REs) of particulate matter (PM) in six size ranges (from 0.3 μm to >10 μm). The results are used to assess the overall exposure reduction at the individual and population levels in Beijing. We find significant differences in REs among PM sizes, facemask brands, pollution levels, and genders. Combining the information on the usage of various brands, facemask wearing rates, and PM_2.5 concentrations in the ambient and indoor air in this area, we evaluate the overall effect of facemask wearing on PM_2.5 exposure reduction. It is quantitatively demonstrated that because people spend most time indoors, facemask protection is limited. For facemask wearers, the overall exposure can be reduced by less than 20%, whereas the reduction rate is as low as 2.4 ± 1.6% for the entire adult populations even in the year with the highest level of pollution with an annual mean PM_2.5 concentration of 102 ± 98 μg∙m^-3. As a strategy of self-protection from long-term exposure to particulate matter, wearing facemasks outdoors is inferior to the installation of indoor air purifiers.

Temporal changes in short-term associations between cardiorespiratory emergency department visits and PM2.5 in Los Angeles, 2005 to 2016

BACKGROUND

Emissions control programs targeting certain air pollution sources may alter PM_2.5 composition, as well as the risk of adverse health outcomes associated with PM_2.5.

OBJECTIVES

We examined temporal changes in the risk of emergency department (ED) visits for cardiovascular diseases (CVDs) and asthma associated with short-term increases in ambient PM_2.5 concentrations in Los Angeles, California.

METHODS

Poisson log-linear models with unconstrained distributed exposure lags were used to estimate the risk of CVD and asthma ED visits associated with short-term increases in daily PM_2.5 concentrations, controlling for temporal and meteorological confounders. The models were run separately for three predefined time periods, which were selected based on the implementation of multiple emissions control programs (EARLY: 2005-2008; MIDDLE: 2009-2012; LATE: 2013-2016). Two-pollutant models with individual PM_2.5 components and the remaining PM_2.5 mass were also considered to assess the influence of changes in PM_2.5 composition on changes in the risk of CVD and asthma ED visits associated with PM_2.5 over time.

RESULTS

The relative risk of CVD ED visits associated with a 10 μg/m³ increase in 4-day PM_2.5 concentration (lag 0-3) was higher in the LATE period (rate ratio = 1.020, 95% confidence interval = [1.010, 1.030]) compared to the EARLY period (1.003, [0.996, 1.010]). In contrast, for asthma, relative risk estimates were largest in the EARLY period (1.018, [1.006, 1.029]), but smaller in the following periods. Similar temporal differences in relative risk estimates for CVD and asthma were observed among different age and season groups. No single component was identified as an obvious contributor to the changing risk estimates over time, and some components exhibited different temporal patterns in risk estimates from PM_2.5 total mass, such as a decreased risk of CVD ED visits associated with sulfate over time.

CONCLUSIONS

Temporal changes in the risk of CVD and asthma ED visits associated with short-term increases in ambient PM_2.5 concentrations were observed. These changes could be related to changes in PM_2.5 composition (e.g., an increasing fraction of organic carbon and a decreasing fraction of sulfate in PM_2.5). Other factors such as improvements in healthcare and differential exposure misclassification might also contribute to the changes.

Using High-Temporal-Resolution Ambient Data to Investigate Gas-Particle Partitioning of Ammonium over Different Seasons

Ammonium is one of the dominant inorganic water-soluble ions in fine particulate matter (PM_2.5). In this study, source apportionment and thermodynamic equilibrium models were used to analyze the relationship between pH and the partitioning of ammonium (ε(NH₄⁺)) using hourly ambient samples collected from Tianjin, China. We found a "Reversed-S curve" between pH and ε(NH₄⁺) from the ambient hourly aerosol dataset when the theoretical ε(NO₃^-)^* (an index identified in this work) was within specific ranges. A Boltzmann function was then used to fit the Reversed-S curve. For the summer data set, when ε(NO₃^-)^* was between 0.7 and 0.8, the fitted R² was 0.88. Through thermodynamic analysis, we found that the values of k[H⁺]² (k = 3.08 × 10⁴ L² mol^-2) and ε(NO₃^-)^* can influence the pH-ε(NH₄⁺) curve. Under certain situations, the values of k[H⁺]² and ε(NO₃^-)^* are similar to each other, and ε(NH₄⁺) is sensitive to pH, suggesting that ε(NO₃^-)^* plays an important role in affecting the ε(NH₄⁺). During summer, winter, and spring seasons, when the relative humidity was greater than 0.36 and ε(NO₃^-)^* was between 0.8 and 0.95, there was an obvious Reversed-S curve, with R² = 0.60. The theoretical k[H⁺]² and ε(NO₃^-)^* developed in this work can be used to analyze the gas-particle partitioning of ammonia-ammonium and nitrate-nitric acid in the ambient atmosphere. Also, it is the first time that we created the joint source-NH₃/HNO₃ maps to integrate sources, aerosol pH and liquid water content, and ions (altogether in one map), which can provide useful information for designing effective strategies to control particulate matter pollution.

A multiphase CMAQ version 5.0 adjoint

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US.

Fine Particle Iron in Soils and Road Dust Is Modulated by Coal-Fired Power Plant Sulfur

Transition metal ions, such as water-soluble iron (WS-Fe), are toxic components of fine particles (PM_2.5). In Atlanta, from 1998 to 2013, a previous study found that WS-Fe was the PM_2.5 species most associated with adverse cardiovascular outcomes. We examined this data set to investigate the sources of WS-Fe and the effects of air quality regulations on ambient levels of WS-Fe. We find that insoluble forms of iron in mineral and road dust combined with sulfate from coal-fired electrical generating units were converted into soluble forms by sulfate-driven acid dissolution. Sulfate produced both the highly acidic aerosol (summer pH 1.5-2) and liquid water required for the aqueous phase acid dissolution, but variability in WS-Fe was mainly driven by particle liquid water. These processes were more pronounced in summer when particles were most acidic, whereas in winter the relative importance of WS-Fe from combustion emissions increased. Although WS-Fe constituted a minute fraction of PM_2.5 mass (0.15%), the WS-Fe-PM_2.5 mass correlation was high (r = 0.67) and may be explained by these formation routes, which, in part, could account for observed associations between PM_2.5 mass and adverse health seen in past studies. Similar processes are expected in many regions, implying that these unexpected benefits from coal-burning reduction may be widespread.

Connecting Air Quality with Emotional Well-Being and Neighborhood Infrastructure in a US City

Cities in the United States have announced initiatives to become more sustainable, healthy, resilient, livable, and environmentally friendly. However, indicators for measuring all outcomes related to these targets and the synergies between them have not been well defined or studied. One such relationship is the linkage between air quality with emotional well-being (EWB) and neighborhood infrastructure. Here, regulatory monitoring, low-cost sensors (LCSs), and air quality modeling were combined to assess exposures to PM2.5 and traffic-related NOx in 6 Minneapolis, MN, neighborhoods of varying infrastructure parameters (median household income, urban vs suburban, and access to light rail). Residents of the study neighborhoods concurrently took real-time EWB assessments using a smart phone application, Daynamica, to gauge happiness, tiredness, stress, sadness, and pain. Both LCS PM2.5 observations and mobile-source-simulated NOx were calibrated using regulatory observations in Minneapolis. No statistically significant (α = 0.05) PM2.5 differences were found between urban poor and urban middle-income neighborhoods, but average mobile-source NOx was statistically significantly (α = 0.05) higher in the 4 urban neighborhoods than in the 2 suburban neighborhoods. Close proximity to light rail had no observable impact on average observed PM2.5 or simulated mobile-source NOx. Home-based exposure assessments found that PM2.5 was negatively correlated with positive emotions such as happiness and to net affect (the sum of positive and negative emotion scores) and positively correlated (ie, a higher PM2.5 concentration led to higher scores) for negative emotions such as tiredness, stress, sadness, and pain. Simulated mobile-source NOx, assessed from both home-based exposures and in situ exposures, had a near-zero relationship with all EWB indicators. This was attributed to low NOx levels throughout the study neighborhoods and at locations were the EWB-assessed activities took place, both owing to low on-road mobile-source NOx impacts. Although none of the air quality and EWB responses were determined to be statistically significant (α = 0.05), due in part to the relatively small sample size, the results are suggestive of linkages between air quality and a variety of EWB outcomes.

Evaluating a multipollutant metric for use in characterizing traffic-related air pollution exposures within near-road environments

Accurately characterizing human exposures to traffic-related air pollutants (TRAPs) is critical to public health protection. However, quantifying exposure to this single source is challenging, given its extremely heterogeneous chemical composition. Efforts using single-species tracers of TRAP are, thus, lacking in their ability to accurately reflect exposures to this complex mixture. There have been recent discussions centered on adopting a multipollutant perspective for sources with many emitted pollutants to maximize the benefits of control expenditures as well as to minimize population and ecosystem exposure. As part of a larger study aimed to assess a complete emission-to-exposure pathway of primary traffic pollution and understand exposure of individuals in the near-road environment, an intensive field campaign measured TRAPs and related data (e.g., meteorology, traffic counts, and regional air pollutant levels) in Atlanta along one of the busiest highway corridors in the US. Given the dynamic nature of the near-road environment, a multipollutant exposure metric, the Integrated Mobile Source Indicator (IMSI), which was generated based on emissions-based ratios, was calculated and compared to traditional single-species methods for assessing exposure to mobile source emissions. The current analysis examined how both traditional and non-traditional metrics vary spatially and temporally in the near-road environment, how they compare with each other, and whether they have the potential to offer more accurate means of assigning exposures to primary traffic emissions. The results indicate that compared to the traditional single pollutant specie, the multipollutant IMSI metric provided a more spatially stable method for assessing exposure, though variations occurred based on location with varying results among the six sites within a kilometer of the highway.

Near-road Vehicle Emissions Air Quality Monitoring for Exposure Modeling

Exposure to vehicular emissions has been linked to numerous adverse health effects. In response to the arising concerns, near-road monitoring is conducted to better characterize the impact of mobile source emissions on air quality and exposure in the near-road environment. An intensive measurement campaign measured traffic-related air pollutants (TRAPs) and related data (e.g., meteorology, traffic, regional air pollutant levels) in Atlanta, along one of the busiest highway corridors in the US. Given the complexity of the near-road environment, the study aimed to compare two near-road monitors, located in close proximity to each other, to assess how observed similarities and differences between measurements at these two sites inform the siting of other near-road monitoring stations. TRAP measurements, including carbon monoxide (CO) and nitrogen dioxide (NO₂), are analyzed at two roadside monitors in Atlanta, GA located within 325m of each other. Both meteorological and traffic conditions were monitored to assess the temporal impact of these factors on traffic-related pollutant concentrations. The meteorological factors drove the diurnal variability of primary pollutant concentration more than traffic count. In spite of their proximity, while the CO and NO₂ concentrations were correlated with similar diurnal variations, pollutant concentrations at the two closely sited monitors differed, likely due to the differences in the siting characteristics reducing the dispersion of the primary emissions out of the near-road environment. Overall, the near-road TRAP concentrations at all sites were not as elevated as seen in prior studies, supporting that decreased vehicle emissions have led to significant reductions in TRAP levels, even along major interstates. Further, the differences in the observed levels show that use of single near-road observations will not capture pollutant levels representative of the local near-road environment and that additional approaches (e.g., air quality models) are needed to characterize exposures.

CO2 hydrogenation to high-value products via heterogeneous catalysis

Recently, carbon dioxide capture and conversion, along with hydrogen from renewable resources, provide an alternative approach to synthesis of useful fuels and chemicals. People are increasingly interested in developing innovative carbon dioxide hydrogenation catalysts, and the pace of progress in this area is accelerating. Accordingly, this perspective presents current state of the art and outlook in synthesis of light olefins, dimethyl ether, liquid fuels, and alcohols through two leading hydrogenation mechanisms: methanol reaction and Fischer-Tropsch based carbon dioxide hydrogenation. The future research directions for developing new heterogeneous catalysts with transformational technologies, including 3D printing and artificial intelligence, are provided.

Carbon dioxide (CO₂) capture and conversion provide an alternative approach to synthesis of useful fuels and chemicals. Here, Ye et al. give a comprehensive perspective on the current state of the art and outlook of CO₂ catalytic hydrogenation to the synthesis of light olefins, dimethyl ether, liquid fuels, and alcohols.

Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014

Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO₂, SO_2, O₃, PM_2.5, PM₁₀, NO₃⁻, NH₄⁺, EC, OC, SO₄²⁻) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R² values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R² values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R² values of 0.4 or more for all pollutants except SO₂.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

Current and Future Responses of Aerosol pH and Composition in the U.S. to Declining SO2 Emissions and Increasing NH3 Emissions

Aerosol pH can affect gas-particle partitioning of semivolatile species, secondary aerosol formation, aerosol water uptake and growth, acid deposition, and, potentially, aerosol toxicity. Despite its importance, aerosol pH projected in the near future has not been addressed explicitly while investigating the response of aerosol concentrations to emission regulations. In this study, we apply CMAQ to simulate aerosol pH in 2011 and 2050 across the continental U.S. We also assess the influence of two major emission trends, declining SO₂ emissions and rising NH₃ emissions, with a set of sensitivity simulations. Our results show that the aerosols will remain acidic with average pH typically ranging from 0.5 to 3.5 in 2050. Further reducing domestic SO₂ emissions does not significantly decrease aerosol acidity, even if SO₂ emissions were reduced to preindustrial levels because of the nonlinear response of SO₄^2- concentration to SO₂ emissions, and the semivolatile NH₃-NH₄⁺ buffering effect. Aerosol pH response to NH₃ emission increase will remain minor. Consequently, future fine particulate matter control efficiency will not be undercut by additional nitrate aerosol formation even if SO₂ emissions from industry and electricity generation are aggressively controlled, although areas will see some substitution leading to nitrate increases if NO_x emissions are not reduced.