Catalytic role of formaldehyde in particulate matter formation.

Formaldehyde (HCHO), the simplest and most abundant carbonyl in the atmosphere, contributes to particulate matter (PM) formation via two in-cloud processing pathways. First, in a catalytic pathway, HCHO reacts with hydrogen peroxide (H2O2) to form hydroxymethyl hydroperoxide (HMHP), which rapidly oxidizes dissolved sulfur dioxide (SO2,aq) to sulfate, regenerating HCHO. Second, HCHO reacts with dissolved SO2,aq to form hydroxymethanesulfonate (HMS), which upon oxidation with the hydroxyl radical (OH) forms sulfate and also reforms HCHO. Chemical transport model simulations using rate coefficients from laboratory studies of the reaction rate of HMHP with SO2,aq show that the HMHP pathways reduce the SO2 lifetime by up to a factor of 2 and contribute up to ∼18% of global sulfate. This contribution rises to >50% in isoprene-dominated regions such as the Amazon. Combined with recent results on HMS, this work demonstrates that the one-carbon molecules HMHP and HCHO contribute significantly to global PM, with HCHO playing a crucial catalytic role.

Isotopic evidence of multiple controls on atmospheric oxidants over climate transitions.

The abundance of tropospheric oxidants, such as ozone (O3) and hydroxyl (OH) and peroxy radicals (HO2 + RO2), determines the lifetimes of reduced trace gases such as methane and the production of particulate matter important for climate and human health. The response of tropospheric oxidants to climate change is poorly constrained owing to large uncertainties in the degree to which processes that influence oxidants may change with climate and owing to a lack of palaeo-records with which to constrain levels of atmospheric oxidants during past climate transitions. At present, it is thought that temperature-dependent emissions of tropospheric O3 precursors and water vapour abundance determine the climate response of oxidants, resulting in lower tropospheric O3 in cold climates while HOx (= OH + HO2 + RO2) remains relatively buffered. Here we report observations of oxygen-17 excess of nitrate (a proxy for the relative abundance of atmospheric O3 and HOx) from a Greenland ice core over the most recent glacial-interglacial cycle and for two Dansgaard-Oeschger events. We find that tropospheric oxidants are sensitive to climate change with an increase in the O3/HOx ratio in cold climates, the opposite of current expectations. We hypothesize that the observed increase in O3/HOx in cold climates is driven by enhanced stratosphere-to-troposphere transport of O3, and that reactive halogen chemistry is also enhanced in cold climates. Reactive halogens influence the oxidative capacity of the troposphere directly as oxidants themselves and indirectly via their influence on O3 and HOx. The strength of stratosphere-to-troposphere transport is largely controlled by the Brewer-Dobson circulation, which may be enhanced in colder climates owing to a stronger meridional gradient of sea surface temperatures, with implications for the response of tropospheric oxidants and stratospheric thermal and mass balance. These two processes may represent important, yet relatively unexplored, climate feedback mechanisms during major climate transitions.

Estimating the health effects of environmental mixtures using principal stratification.

The control of ambient air quality in the United States has been a major public health success since the passing of the Clean Air Act, with particulate matter (PM) reductions resulting in an estimated 160 000 premature deaths prevented in 2010 alone. Currently, public policy is oriented around lowering the levels of individual pollutants and this focus has driven the nature of much epidemiological research. Recently, attention has been given to viewing air pollution as a complex mixture and to developing a multi-pollutant approach to controlling ambient concentrations. We present a statistical approach for estimating the health impacts of complex environmental mixtures using a mixture-altering contrast, which is any comparison, intervention, policy, or natural experiment that changes a mixture's composition. We combine the notion of mixture-altering contrasts with sliced inverse regression, propensity score matching, and principal stratification to assess the health effects of different air pollution chemical mixtures. We demonstrate the application of this approach in an analysis of the health effects of wildfire PM air pollution in the Western US.

Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem.

The burning of fossil fuels - especially coal, petrol, and diesel - is a major source of airborne fine particulate matter (PM2.5), and a key contributor to the global burden of mortality and disease. Previous risk assessments have examined the health response to total PM2.5, not just PM2.5 from fossil fuel combustion, and have used a concentration-response function with limited support from the literature and data at both high and low concentrations. This assessment examines mortality associated with PM2.5 from only fossil fuel combustion, making use of a recent meta-analysis of newer studies with a wider range of exposure. We also estimated mortality due to lower respiratory infections (LRI) among children under the age of five in the Americas and Europe, regions for which we have reliable data on the relative risk of this health outcome from PM2.5 exposure. We used the chemical transport model GEOS-Chem to estimate global exposure levels to fossil-fuel related PM2.5 in 2012. Relative risks of mortality were modeled using functions that link long-term exposure to PM2.5 and mortality, incorporating nonlinearity in the concentration response. We estimate a global total of 10.2 (95% CI: -47.1 to 17.0) million premature deaths annually attributable to the fossil-fuel component of PM2.5. The greatest mortality impact is estimated over regions with substantial fossil fuel related PM2.5, notably China (3.9 million), India (2.5 million) and parts of eastern US, Europe and Southeast Asia. The estimate for China predates substantial decline in fossil fuel emissions and decreases to 2.4 million premature deaths due to 43.7% reduction in fossil fuel PM2.5 from 2012 to 2018 bringing the global total to 8.7 (95% CI: -1.8 to 14.0) million premature deaths. We also estimated excess annual deaths due to LRI in children (0-4 years old) of 876 in North America, 747 in South America, and 605 in Europe. This study demonstrates that the fossil fuel component of PM2.5 contributes a large mortality burden. The steeper concentration-response function slope at lower concentrations leads to larger estimates than previously found in Europe and North America, and the slower drop-off in slope at higher concentrations results in larger estimates in Asia. Fossil fuel combustion can be more readily controlled than other sources and precursors of PM2.5 such as dust or wildfire smoke, so this is a clear message to policymakers and stakeholders to further incentivize a shift to clean sources of energy.

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States.

In this paper, we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate the daily maximum 8 h O3 with high resolution over both space (at 1 km × 1 km grid cells covering the contiguous United States) and time (daily estimates between 2000 and 2016). We further quantify monthly model uncertainty for our 1 km × 1 km gridded domain. The results demonstrate high overall model performance with an average cross-validated R2 (coefficient of determination) against observations of 0.90 and 0.86 for annual averages. Overall, the model performance of the three machine learning algorithms was quite similar. The overall model performance from the ensemble model outperformed those from any single algorithm. The East North Central region of the United States had the highest R2, 0.93, and performance was weakest for the western mountainous regions (R2 of 0.86) and New England (R2 of 0.87). For the cross validation by season, our model had the best performance during summer with an R2 of 0.88. This study can be useful for the environmental health community to more accurately estimate the health impacts of O3 over space and time, especially in health studies at an intra-urban scale.

Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging.

NO2 is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO2 model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R2 of 0.788 overall, a spatial R2 of 0.844, and a temporal R2 of 0.729. The relationship between daily monitored and predicted NO2 is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO2 levels. This NO2 estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO2 in unmonitored areas. We found the highest NO2 levels along highways and in cities. We also observed that nationwide NO2 levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns.

We develop a statistical model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean-atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean-atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region.

Air Quality and Health Impact of Future Fossil Fuel Use for Electricity Generation and Transport in Africa.

Africa has ambitious plans to address energy deficits and sustain economic growth with fossil fueled power plants. The continent is also experiencing faster population growth than anywhere else in the world that will lead to proliferation of vehicles. Here, we estimate air pollutant emissions in Africa from future (2030) electricity generation and transport. We find that annual emissions of two precursors of fine particles (PM2.5) hazardous to health, sulfur dioxide (SO2) and nitrogen oxides (NOx), approximately double by 2030 relative to 2012, increasing from 2.5 to 5.5 Tg SO2 and 1.5 to 2.8 Tg NOx. We embed these emissions in the GEOS-Chem model nested over the African continent to simulate ambient concentrations of PM2.5 and determine the burden of disease (excess deaths) attributable to exposure to future fossil fuel use. We calculate 48000 avoidable deaths in 2030 (95% confidence interval: 6000-88000), mostly in South Africa (10400), Nigeria (7500), and Malawi (2400), with 3-times higher mortality rates from power plants than transport. Sensitivity of the burden of disease to either population growth or air quality varies regionally and suggests that emission mitigation strategies would be most effective in Southern Africa, whereas population growth is the main driver everywhere else.

Who Among the Elderly Is Most Vulnerable to Exposure to and Health Risks of Fine Particulate Matter From Wildfire Smoke?

Wildfires burn more than 7 million acres in the United States annually, according to the US Forest Service. Little is known about which subpopulations are more vulnerable to health risks from wildfire smoke, including those associated with fine particulate matter. We estimated exposure to fine particles specifically from wildfires, as well as the associations between the presence of wildfire-specific fine particles and the amount of hospital admissions for respiratory causes among subpopulations older than 65 years of age in the western United States (2004-2009). Compared with other populations, higher fractions of persons who were black, lived in urban counties, and lived in California were exposed to more than 1 smoke wave (high-pollution episodes from wildfire smoke). The risks of respiratory admissions on smoke-wave days compared with non-smoke-wave days increased 10.4% (95% confidence interval: 1.9, 19.6) for women and 21.7% (95% confidence interval: 0.4, 47.3) for blacks. Our findings suggest that increased risks of respiratory admissions from wildfire smoke was significantly higher for women than for men (10.4% vs. 3.7%), blacks than whites (21.7% vs. 6.9%), and, although associations were not statistically different, people in lower-education counties than higher-educated counties (12.7% vs. 6.1%). Our study raised important environmental justice issues that can inform public health programs and wildfire management. As climate change increases the frequency and intensity of wildfires, evidence on vulnerable subpopulations can inform disaster preparedness and the understanding of climate change consequences.

Wildfire-specific Fine Particulate Matter and Risk of Hospital Admissions in Urban and Rural Counties.

BACKGROUND: The health impacts of wildfire smoke, including fine particles (PM2.5), are not well understood and may differ from those of PM2.5 from other sources due to differences in concentrations and chemical composition. METHODS: First, for the entire Western United States (561 counties) for 2004-2009, we estimated daily PM2.5 concentrations directly attributable to wildfires (wildfires-specific PM2.5), using a global chemical transport model. Second, we defined smoke wave as ≥2 consecutive days with daily wildfire-specific PM2.5 > 20 µg/m, with sensitivity analysis considering 23, 28, and 37 µg/m. Third, we estimated the risk of cardiovascular and respiratory hospital admissions associated with smoke waves for Medicare enrollees. We used a generalized linear mixed model to estimate the relative risk of hospital admissions on smoke wave days compared with matched comparison days without wildfire smoke. RESULTS: We estimated that about 46 million people of all ages were exposed to at least one smoke wave during 2004 to 2009 in the Western United States. Of these, 5 million are Medicare enrollees (≥65 years). We found a 7.2% (95% confidence interval: 0.25%, 15%) increase in risk of respiratory admissions during smoke wave days with high wildfire-specific PM2.5 (>37 µg/m) compared with matched non smoke wave days. We did not observe an association between smoke wave days with wildfire-specific PM2.5 ≤ 37 µg/mand respiratory or cardiovascular admissions. Respiratory effects of wildfire-specific PM2.5 may be stronger than that of PM2.5 from other sources. CONCLUSION: Short-term exposure to wildfire-specific PM2.5was associated with risk of respiratory diseases in the elderly population in the Western United States during severe smoke days. See video abstract at, http://links.lww.com/EDE/B137.

Formaldehyde (HCHO) As a Hazardous Air Pollutant: Mapping Surface Air Concentrations from Satellite and Inferring Cancer Risks in the United States.

Formaldehyde (HCHO) is the most important carcinogen in outdoor air among the 187 hazardous air pollutants (HAPs) identified by the U.S. Environmental Protection Agency (EPA), not including ozone and particulate matter. However, surface observations of HCHO are sparse and the EPA monitoring network could be prone to positive interferences. Here we use 2005-2016 summertime HCHO column data from the OMI satellite instrument, validated with high-quality aircraft data and oversampled on a 5 × 5 km2 grid, to map surface air HCHO concentrations across the contiguous U.S. OMI-derived summertime HCHO values are converted to annual averages using the GEOS-Chem chemical transport model. Results are in good agreement with high-quality summertime observations from urban sites (-2% bias, r = 0.95) but a factor of 1.9 lower than annual means from the EPA network. We thus estimate that up to 6600-12 500 people in the U.S. will develop cancer over their lifetimes by exposure to outdoor HCHO. The main HCHO source in the U.S. is atmospheric oxidation of biogenic isoprene, but the corresponding HCHO yield decreases as the concentration of nitrogen oxides (NOx ≡ NO + NO2) decreases. A GEOS-Chem sensitivity simulation indicates that HCHO levels would decrease by 20-30% in the absence of U.S. anthropogenic NOx emissions. Thus, NOx emission controls to improve ozone air quality have a significant cobenefit in reducing HCHO-related cancer risks.

Effects of El Niño on summertime ozone air quality in the eastern United States.

We investigate the effect of El Niño on maximum daily 8-hour average surface ozone over the eastern United States in summer during 1980-2016. El Niño can influence the extra-tropical climate through the propagation of stationary waves, leading to (1) reduced transport of moist, clean air into the mid- and southern Atlantic states and greater subsidence, reduced precipitation, and increased surface solar radiation in this region, as well as (2) intensified southerly flow into the south central states, which here enhances flux of moist and clean air. As a result, each standard deviation increase in the Niño 1+2 index is associated with an increase of 1-2 ppbv ozone in the Atlantic states and a decrease of 0.5-2 ppbv ozone in the south central states. These influences can be predicted 4 month in advance. We show that U.S. summertime ozone responds differently to eastern-type El Niño events compared to central-type events.

Strong Dependence of U.S. Summertime Air Quality on the Decadal Variability of Atlantic Sea Surface Temperatures.

We find that summertime air quality in the eastern U.S. displays strong dependence on North Atlantic sea surface temperatures, resulting from large-scale ocean-atmosphere interactions. Using observations, reanalysis data sets, and climate model simulations, we further identify a multidecadal variability in surface air quality driven by the Atlantic Multidecadal Oscillation (AMO). In one-half cycle (~35 years) of the AMO from cold to warm phase, summertime maximum daily 8 h ozone concentrations increase by 1-4 ppbv and PM2.5 concentrations increase by 0.3-1.0 µg m-3 over much of the east. These air quality changes are related to warmer, drier, and more stagnant weather in the AMO warm phase, together with anomalous circulation patterns at the surface and aloft. If the AMO shifts to the cold phase in future years, it could partly offset the climate penalty on U.S. air quality brought by global warming, an effect which should be considered in long-term air quality planning.

Future fire emissions associated with projected land use change in Sumatra.

Indonesia has experienced rapid land use change over the last few decades as forests and peatswamps have been cleared for more intensively managed land uses, including oil palm and timber plantations. Fires are the predominant method of clearing and managing land for more intensive uses, and the related emissions affect public health by contributing to regional particulate matter and ozone concentrations and adding to global atmospheric carbon dioxide concentrations. Here, we examine emissions from fires associated with land use clearing and land management on the Indonesian island of Sumatra and the sensitivity of this fire activity to interannual meteorological variability. We find ~80% of 2005-2009 Sumatra emissions are associated with degradation or land use maintenance instead of immediate land use conversion, especially in dry years. We estimate Sumatra fire emissions from land use change and maintenance for the next two decades with five scenarios of land use change, the Global Fire Emissions Database Version 3, detailed 1-km2 land use change maps, and MODIS fire radiative power observations. Despite comprising only 16% of the original study area, we predict that 37-48% of future Sumatra emissions from land use change will occur in fuel-rich peatswamps unless this land cover type is protected effectively. This result means that the impact of fires on future air quality and climate in Equatorial Asia will be decided in part by the conservation status given to the remaining peatswamps on Sumatra. Results from this article will be implemented in an atmospheric transport model to quantify the public health impacts from the transport of fire emissions associated with future land use scenarios in Sumatra.

Air Pollution and Blood Pressure: Evidence From Indonesia.

Indonesia faces significant air quality issues due to multiple emissions sources, including rapid urbanization and peatland fires associated with agricultural land management. Limited prior research has estimated the episodic shock of intense fires on morbidity and mortality in Indonesia but has largely ignored the impact of poor air quality throughout the year on biomarkers of cardiovascular disease risk. We conducted a cross-sectional study of the association between particulate matter less than 2.5 microns in diameter (PM2.5) and blood pressure. Blood pressure measurements were obtained from the fifth wave of the Indonesian Family Life Survey (IFLS5), an ongoing population-based socioeconomic and health survey. We used the GEOS-Chem chemical transport model to simulate daily PM2.5 concentrations at 0.5° × 0.625° resolution across the IFLS domain. We assessed the association between PM2.5 and diastolic and systolic blood pressure, using mixed effects models with random intercepts for regency/municipality and household and adjusted for individual covariates. An interquartile range increase in monthly PM2.5 exposure was associated with a 0.234 (95% CI: 0.003, 0.464) higher diastolic blood pressure, with a greater association seen in participants age 65 and over (1.16 [95% CI: 0.24, 2.08]). For the same exposure metric, there was a 1.90 (95% CI: 0.43, 3.37) higher systolic blood pressure in participants 65 and older. Our assessment of fire-specific PM2.5 yielded null results, potentially due to the timing and locations of health data collection. To our knowledge, this is the first study to provide evidence for an association between PM2.5 and blood pressure in Indonesia.

Climate Change and Cardiovascular Health: A Systematic Review.

Importance: Climate change may increase the risk of adverse cardiovascular outcomes by causing direct physiologic changes, psychological distress, and disruption of health-related infrastructure. Yet, the association between numerous climate change-related environmental stressors and the incidence of adverse cardiovascular events has not been systematically reviewed. Objective: To review the current evidence on the association between climate change-related environmental stressors and adverse cardiovascular outcomes. Evidence Review: PubMed, Embase, Web of Science, and Cochrane Library were searched to identify peer-reviewed publications from January 1, 1970, through November 15, 2023, that evaluated associations between environmental exposures and cardiovascular mortality, acute cardiovascular events, and related health care utilization. Studies that examined only nonwildfire-sourced particulate air pollution were excluded. Two investigators independently screened 20 798 articles and selected 2564 for full-text review. Study quality was assessed using the Navigation Guide framework. Findings were qualitatively synthesized as substantial differences in study design precluded quantitative meta-analysis. Findings: Of 492 observational studies that met inclusion criteria, 182 examined extreme temperature, 210 ground-level ozone, 45 wildfire smoke, and 63 extreme weather events, such as hurricanes, dust storms, and droughts. These studies presented findings from 30 high-income countries, 17 middle-income countries, and 1 low-income country. The strength of evidence was rated as sufficient for extreme temperature; ground-level ozone; tropical storms, hurricanes, and cyclones; and dust storms. Evidence was limited for wildfire smoke and inadequate for drought and mudslides. Exposure to extreme temperature was associated with increased cardiovascular mortality and morbidity, but the magnitude varied with temperature and duration of exposure. Ground-level ozone amplified the risk associated with higher temperatures and vice versa. Extreme weather events, such as hurricanes, were associated with increased cardiovascular risk that persisted for many months after the initial event. Some studies noted a small increase in cardiovascular mortality, out-of-hospital cardiac arrests, and hospitalizations for ischemic heart disease after exposure to wildfire smoke, while others found no association. Older adults, racial and ethnic minoritized populations, and lower-wealth communities were disproportionately affected. Conclusions and Relevance: Several environmental stressors that are predicted to increase in frequency and intensity with climate change are associated with increased cardiovascular risk, but data on outcomes in low-income countries are lacking. Urgent action is needed to mitigate climate change-associated cardiovascular risk, particularly in vulnerable populations.

Improved biomass burning emissions from 1750 to 2010 using ice core records and inverse modeling.

Estimating fire emissions prior to the satellite era is challenging because observations are limited, leading to large uncertainties in the calculated aerosol climate forcing following the preindustrial era. This challenge further limits the ability of climate models to accurately project future climate change. Here, we reconstruct a gridded dataset of global biomass burning emissions from 1750 to 2010 using inverse analysis that leveraged a global array of 31 ice core records of black carbon deposition fluxes, two different historical emission inventories as a priori estimates, and emission-deposition sensitivities simulated by the atmospheric chemical transport model GEOS-Chem. The reconstructed emissions exhibit greater temporal variabilities which are more consistent with paleoclimate proxies. Our ice core constrained emissions reduced the uncertainties in simulated cloud condensation nuclei and aerosol radiative forcing associated with the discrepancy in preindustrial biomass burning emissions. The derived emissions can also be used in studies of ocean and terrestrial biogeochemistry.

Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century.

We estimate future wildfire activity over the western United States during the mid-21st century (2046-2065), based on results from 15 climate models following the A1B scenario. We develop fire prediction models by regressing meteorological variables from the current and previous years together with fire indexes onto observed regional area burned. The regressions explain 0.25-0.60 of the variance in observed annual area burned during 1980-2004, depending on the ecoregion. We also parameterize daily area burned with temperature, precipitation, and relative humidity. This approach explains ~0.5 of the variance in observed area burned over forest ecoregions but shows no predictive capability in the semi-arid regions of Nevada and California. By applying the meteorological fields from 15 climate models to our fire prediction models, we quantify the robustness of our wildfire projections at mid-century. We calculate increases of 24-124% in area burned using regressions and 63-169% with the parameterization. Our projections are most robust in the southwestern desert, where all GCMs predict significant (p<0.05) meteorological changes. For forested ecoregions, more GCMs predict significant increases in future area burned with the parameterization than with the regressions, because the latter approach is sensitive to hydrological variables that show large inter-model variability in the climate projections. The parameterization predicts that the fire season lengthens by 23 days in the warmer and drier climate at mid-century. Using a chemical transport model, we find that wildfire emissions will increase summertime surface organic carbon aerosol over the western United States by 46-70% and black carbon by 20-27% at midcentury, relative to the present day. The pollution is most enhanced during extreme episodes: above the 84th percentile of concentrations, OC increases by ~90% and BC by ~50%, while visibility decreases from 130 km to 100 km in 32 Federal Class 1 areas in Rocky Mountains Forest.

Kudzu (Pueraria montana) invasion doubles emissions of nitric oxide and increases ozone pollution.

The nitrogen-fixing legume kudzu (Pueraria montana) is a widespread invasive plant in the southeastern United States with physiological traits that may lead to important impacts on ecosystems and the atmosphere. Its spread has the potential to raise ozone levels in the region by increasing nitric oxide (NO) emissions from soils as a consequence of increasing nitrogen (N) inputs and cycling in soils. We studied the effects of kudzu invasions on soils and trace N gas emissions at three sites in Madison County, Georgia in 2007 and used the results to model the effects of kudzu invasion on regional air quality. We found that rates of net N mineralization increased by up to 1,000%, and net nitrification increased by up to 500% in invaded soils in Georgia. Nitric oxide emissions from invaded soils were more than 100% higher (2.81 vs. 1.24 ng NO-N cm(-2) h(-1)). We used the GEOS-Chem chemical transport model to evaluate the potential impact of kudzu invasion on regional atmospheric chemistry and air quality. In an extreme scenario, extensive kudzu invasion leads directly to an increase in the number of high ozone events (above 70 ppb) of up to 7 days each summer in some areas, up from 10 to 20 days in a control scenario with no kudzu invasion. These results establish a quantitative link between a biological invasion and ozone formation and suggest that in this extreme scenario, kudzu invasion can overcome some of the air quality benefits of legislative control.

Data-Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice.

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.