Impacts assessment of nitrification inhibitors on U.S. agricultural emissions of reactive nitrogen gases.

Fertilizer-intensive agriculture leads to emissions of reactive nitrogen (Nr), posing threats to climate via nitrous oxide (N2O) and to air quality and human health via nitric oxide (NO) and ammonia (NH3) that form ozone and particulate matter (PM) downwind. Adding nitrification inhibitors (NIs) to fertilizers can mitigate N2O and NO emissions but may stimulate NH3 emissions. Quantifying the net effects of these trade-offs requires spatially resolving changes in emissions and associated impacts. We introduce an assessment framework to quantify such trade-off effects. It deploys an agroecosystem model with enhanced capabilities to predict emissions of Nr with or without the use of NIs, and a social cost of greenhouse gas to monetize the impacts of N2O on climate. The framework also incorporates reduced-complexity air quality and health models to monetize associated impacts of NO and NH3 emissions on human health downwind via ozone and PM. Evaluation of our model against available field measurements showed that it captured the direction of emission changes but underestimated reductions in N2O and overestimated increases in NH3 emissions. The model estimated that, averaged over applicable U.S. agricultural soils, NIs could reduce N2O and NO emissions by an average of 11% and 16%, respectively, while stimulating NH3 emissions by 87%. Impacts are largest in regions with moderate soil temperatures and occur mostly within two to three months of N fertilizer and NI application. An alternative estimate of NI-induced emission changes was obtained by multiplying the baseline emissions from the agroecosystem model by the reported relative changes in Nr emissions suggested from a global meta-analysis: -44% for N2O, -24% for NO and +20% for NH3. Monetized assessments indicate that on an annual scale, NI-induced harms from increased NH3 emissions outweigh (8.5-33.8 times) the benefits of reducing NO and N2O emissions in all agricultural regions, according to model-based estimates. Even under meta-analysis-based estimates, NI-induced damages exceed benefits by a factor of 1.1-4. Our study highlights the importance of considering multiple pollutants when assessing NIs, and underscores the need to mitigate NH3 emissions. Further field studies are needed to evaluate the robustness of multi-pollutant assessments.

Integrated Modeling of U.S. Agricultural Soil Emissions of Reactive Nitrogen and Associated Impacts on Air Pollution, Health, and Climate.

Agricultural soils are leading sources of reactive nitrogen (Nr) species including nitrogen oxides (NOx), ammonia (NH3), and nitrous oxide (N2O). The propensity of NOx and NH3 to generate ozone and fine particulate matter and associated impacts on health are highly variable, whereas the climate impacts of long-lived N2O are independent of emission timing and location. However, these impacts have rarely been compared on a spatially resolved monetized basis. In this study, we update the nitrogen scheme in an agroecosystem model to simulate the Nr emissions from fertilized soils across the contiguous United States. We then apply a reduced-form air pollution health effect model to assess air quality impacts from NOx and NH3 and a social cost of N2O to assess the climate impacts. Assuming an $8.2 million value of a statistical life and a $13,100/ton social cost of N2O, the air quality impacts are a factor of ∼7 to 15 times as large as the climate impacts in heavily populated coastal regions, whereas the ratios are closer to 2.5 in sparsely populated regions. Our results show that air pollution, health, and climate should be considered jointly in future assessments of how farming practices affect Nr emissions.

Race and ethnic minority, local pollution, and COVID-19 deaths in Texas.

The costs of COVID-19 are extensive, and, like the fallout of most health and environmental crises in the US, there is growing evidence that these costs weigh disproportionately on communities of color. We investigated whether county-level racial composition and fine particulate pollution (PM2.5) are indicators for COVID-19 incidence and death rates in the state of Texas. Using county-level data, we ran linear regressions of percent minority as well as historic 2000-2016 PM2.5 levels against COVID-19 cases and deaths per capita. We found that a county's percent minority racial composition, defined as the percentage of population that identifies as Black or Hispanic, highly correlates with COVID-19 case and death rates. Using Value-of-Statistical-Life calculations, we found that economic costs from COVID-19 deaths fall more heavily on Black and Hispanic residents in Harris County, the most populous county in Texas. We found no consistent evidence or significant correlations between historic county-average PM2.5 concentration and COVID-19 incidence or death. Our findings suggest that public health and economic aid policy should consider the racially-segregated burden of disease to better mitigate costs and support equity for the duration and aftermath of health crises.

Air quality and health benefits from potential coal power plant closures in Texas.

As power production from renewable energy and natural gas grows, closures of some coal-fired power plants in Texas become increasingly likely. In this study, the potential effects of such closures on air quality and human health were analyzed by linking a regional photochemical model with a health impacts assessment tool. The impacts varied significantly across 13 of the state's largest coal-fired power plants, sometimes by more than an order of magnitude, even after normalizing by generation. While some power plants had negligible impacts on concentrations at important monitors, average impacts up to 0.5 parts per billion (ppb) and 0.2 µg/m3 and maximum impacts up to 3.3 ppb and 0.9 µg/m3 were seen for ozone and fine particulate matter (PM2.5), respectively. Individual power plants impacted average visibility by up to 0.25 deciviews in Class I Areas. Health impacts arose mostly from PM2.5 and were an order of magnitude higher for plants that lack scrubbers for SO2. Rankings of health impacts were largely consistent across the base model results and two reduced form models. Carbon dioxide emissions were relatively uniform, ranging from 1.00 to 1.26 short tons/MWh, and can be monetized based on a social cost of carbon. Despite all of these unpaid externalities, estimated direct costs of each power plant exceeded wholesale power prices in 2016. Implications: While their CO2 emission rates are fairly similar, sharply different NOx and SO2 emission rates and spatial factors cause coal-fired power plants to vary by an order of magnitude in their impacts on ozone, particulate matter, and associated health and visibility outcomes. On a monetized basis, the air pollution health impacts often exceed the value of the electricity generated and are of similar magnitude to climate impacts. This suggests that both air pollution and climate should be considered if externalities are used to inform decision making about power-plant dispatch and retirement.

Valuing the Air Quality Effects of Biochar Reductions on Soil NO Emissions.

While it is clear that biochar can alter soil N2O emissions, data on NO impacts are scarce. Reports range from 0 to 67% soil NO emission reductions postbiochar amendment. We use regional air quality and health cost models to assess how these soil NO reductions could influence U.S. air quality and health costs. We find that at 67% soil NO reduction, widespread application of biochar to fertilized agricultural soils could reduce O3 by up to 2.4 ppb and PM2.5 by up to 0.15 µg/m3 in some regions. Modeled biochar-mediated health benefits are up to $4.3 million/county in 2011, with impacts focused in the Midwest and Southwest. These potential air quality and health cobenefits of biochar use highlight the need for an improved understanding of biochar's impacts on soil NO emissions. The benefits reported here should be included with estimates of other biochar benefits, such as crop yield increase, soil water management, and N2O reductions.

Inferring Atmospheric Particulate Matter Concentrations from Chinese Social Media Data.

Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011-2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported.

Modeled and observed fine particulate matter reductions from state attainment demonstrations.

States rely upon photochemical models to predict the impacts of air quality attainment strategies, but the performance of those predictions is rarely evaluated retrospectively. State implementation plans (SIPs) developed to attain the 1997 U.S. standard for fine particulate matter (PM2.5; denoting particles smaller than 2.5 microns in diameter) by 2009 provide the first opportunity to assess modeled predictions of PM2.5 reductions at the state level. The SIPs were the first to rely upon a speciated modeled attainment test methodology recommended by the U.S. Environmental Protection Agency to predict PM2.5 concentrations and attainment status. Of the 23 eastern U.S. regions considered here, all but one achieved the 15 microg/m3 standard by 2009, and the other achieved it the following year with downward trends sustained in subsequent years. The attainment tests predicted 2009 PM2.5 design values at individual monitors with a mean bias of 0.38 microg/m3 and mean error of 0.68 microg/m3, and were 95% accurate in predicting whether a monitor would achieve the standard. All of the errors were false alarms, in which the monitor observed attainment after a modeled prediction of an exceedance; in these cases, the states used weight-of-evidence determinations to argue that attainment was likely. Overall, PM2.5 concentrations at monitors in the SIP regions declined by 2.6 microg/m3 from 2000-2004 to 2007-2009, compared with 1.6 microg/m3 in eastern U.S. regions originally designated as attainment. Air quality improvements tended to be largest at monitors that were initially the most polluted. Implications: As states prepare to develop plans for attaining a more stringent standard for fine particulate matter, this retrospective analysis documents substantial and sustained air quality improvements achieved under the previous standard. Significantly larger air quality improvements in regions initially designated nonattainment of the 1997 standard indicate that this status prompted heightened control efforts: The speciated modeled attainment test is found to be accurate and slightly conservative in predicting particulate concentrations for the cases considered here, providing confidence for its use in upcoming attainment plans.

Efficacy of recent state implementation plans for 8-hour ozone.

The development of state implementation plans (SIPs) for attainment of criteria pollutant standards is an integral component of air quality management in the United States. However, the content and efficacy of SIPs have rarely been examined systematically. Here, 20 SIPs developed in response to the 1997 8-hr ozone standard are reviewed as case studies of attainment efforts at the state level. Comparison of observed and model predicted ozone concentrations shows the US Environmental Protection Agency (EPA) recommended modeled attainment test to be a somewhat conservative predictor of attainment. Among 12 SIPs for regions that sought attainment by 2009, the test correctly predicted attainment and nonattainment in four and five regions, respectively; in the other three regions, attainment was observed despite predictions of nonattainment. However weight-of-evidence determinations and deviations from the recommended modeled attainment test methodology led five of these SIPs to predict attainment that was not in fact observed by 2009; three of those regions achieved attainment in 2010. Ozone and NO2 concentrations declined across much of the United States during the period covered by the SIPs, with rates of improvement strongly correlated with the initial pollution levels and hence greatest in nonattainment regions. However at monitors with mid-range levels of ozone initially, rates of reduction were largely independent of the initial attainment status of the region. This is consistent with thefact that apart from California, the majority of ozone precursor reductions documented by SIPs resulted from federal measures rather than from state or local controls specific to the nonattainment regions.

Uncertainties influencing health-based prioritization of ozone abatement options.

The primary goal of air quality management is protection of human health. Therefore, formulation of ground-level ozone mitigation policies could be informed by considering not just attainment of regulatory standards but also how control measures benefit public health. However, evaluation of health impacts is complicated by uncertainties associated with photochemical modeling and epidemiological studies. This study demonstrates methods to characterize uncertainties influencing health-benefits estimation of ozone reduction (averted premature mortalities due to short-term exposure) in the Dallas-Fort Worth (DFW) region. Uncertainty in photochemical modeling and the selection of temporal metric (duration of ozone exposure) for concentration-response relationships can each affect the health-based prioritization of ozone control options. For example, deterministic results (neglecting uncertainties) based on 8-h daily maximum ozone reduction shows DFW anthropogenic NO(x) controls to yield 9.23 times as much benefit per ton as VOC controls. However, the rankings reverse under 5.7% of the cases (including 2.8% cases that exhibit incremental mortalities due to NO(X) control) when uncertainties in the photochemical model are considered. Evaluated ozone exposure on a 24-h rather than an 8-h basis also reverses the rankings.

Meta-analysis of the Association between Short-Term Exposure to Ambient Ozone and Respiratory Hospital Admissions.

Ozone is associated with health impacts including respiratory outcomes; however, results differ across studies. Meta-analysis is an increasingly important approach to synthesizing evidence across studies. We conducted meta-analysis of short-term ozone exposure and respiratory hospitalizations to evaluate variation across studies and explore some of the challenges in meta-analysis. We identified 136 estimates from 96 studies and investigated how estimates differed by age, ozone metric, season, lag, region, disease category, and hospitalization type. Overall results indicate associations between ozone and various types of respiratory hospitalizations; however, study characteristics affected risk estimates. Estimates were similar, but higher, for the elderly compared to all ages and for previous day exposure compared to same day exposure. Comparison across studies was hindered by variation in definitions of disease categories, as some (e.g., asthma) were identified through ≥3 different sets of ICD codes. Although not all analyses exhibited evidence of publication bias, adjustment for publication bias generally lowered overall estimates. Emergency hospitalizations for total respiratory disease increased 4.47% (95% interval 2.48, 6.50%) per 10ppb 24-hr ozone among the elderly without adjustment for publication bias and 2.97% (1.05, 4.94%) with adjustment. Comparison of multi-city study results and meta-analysis based on single-city studies further suggested publication bias.

Likelihood of achieving air quality targets under model uncertainties.

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Uncertainty analysis of ozone formation and response to emission controls using higher-order sensitivities.

Understanding ozone response to its precursor emissions is crucial for effective air quality management practices. This nonlinear response is usually simulated using chemical transport models, and the modeling results are affected by uncertainties in emissions inputs. In this study, a high ozone episode in the southeastern United States is simulated using the Community Multiscale Air Quality (CMAQ) model. Uncertainties in ozone formation and response to emissions controls due to uncertainties in emission rates are quantified using the Monte Carlo method. Instead of propagating emissions uncertainties through the original CMAQ a reduced form of CMAQ is formulated using directly calculated first- and second-order sensitivities that capture the nonlinear ozone concentration-emission responses. This modification greatly reduces the associated computational cost. Quantified uncertainties in modeled ozone concentrations and responses to various emissions controls are much less than the uncertainties in emissions inputs. Average uncertainties in modeled ozone concentrations for the Atlanta area are less than 10% (as measured by the inferred coefficient of variance [ICOV]) even when emissions uncertainties are assumed to vary between a factor of 1.5 and 2. Uncertainties in the ozone responses generally decrease with increased emission controls. Average uncertainties (ICOV) in emission-normalized ozone responses range from 4 to 22%, with the smaller being associated with controlling of the relatively certain point nitrogen oxide (NOx) emissions and the larger resulting from controlling of the less certain mobile NOx emissions. These small uncertainties provide confidence in the model applications, such as in performance evaluation, attainment demonstration, and control strategy development.

Efficient characterization of pollutant-emission response under parametric uncertainty.

An essential requirement of modeling for air quality management is to accurately simulate the responses of pollutant concentrations to changes in emissions. Uncertain model input parameters such as emission rates and reaction rate constants lead to uncertainty in model responses. However, traditional methods for characterizing parametric uncertainty are exceedingly computationally intensive. This paper presents methods for using high-order sensitivity coefficients in analytical equations to efficiently represent how the responsiveness of pollutants to emission reductions in the underlying photochemical model varies with simultaneous perturbations in multiple model input parameters. Separate approaches are introduced for characterizing the parametric uncertainty of pollutant response to a fixed or a variable amount of emission reduction. The approaches are demonstrated for an air pollution episode used in recent attainment planning in Georgia. For hypothetical scenarios in which domain-wide emission rates and photolysis rates are perturbed simultaneously by 50%, the reduced form models yield highly accurate predictions of the ozone impacts due to 50% reductions in nitrogen-oxide emissions in Atlanta (normalized mean bias 6.0%, normalized mean error 9.7%, R2=0.992) and inorganic particulate responses to Atlanta sulfur-dioxide emissions (-2.9% bias, 3.7% error, R2=1.000). Similar accuracy is achieved for pollutant responses to power plant emission controls.

Single-source impact analysis using three-dimensional air quality models.

Isolating the effects of an individual emissions source on secondary air pollutants such as ozone and some components of particulate matter must incorporate complex nonlinear processes, be sensitive to small emissions perturbations, and account for impacts that may occur hundreds of kilometers away. The ability to evaluate these impacts is becoming increasingly important for efficient air quality management. Here, as part of a recent compliance enforcement action for a violation of the Clean Air Act and as an evaluation of ozone response to single-source emissions plumes, two three-dimensional regional photochemical air quality models are used to assess the impact on ozone from approximately 2000 to 3000 excess t/month of nitrogen oxides emitted from a single power plant in Ohio. Periods in May, July, and August are evaluated. Two sensitivity methods are applied: the "brute-force" (B-F) method and the decoupled direct method (DDM). Using DDM, maximum 1-hr averaged ozone concentrations are found to increase by up to 1.8, 1.3, and 2.2 ppbv during May, July, and August episodes, respectively, and concentration increases greater than 0.5 ppbv occur in Ohio, Pennsylvania, Maryland, New York, West Virginia, Virginia, and North and South Carolina. B-F results for the August episode show a maximum 1-hr averaged ozone concentration increase of 2.3 ppbv. Significant localized decreases are also simulated, with a maximum of 3.6 ppbv in Ohio during the August episode and decreases of 0.50 ppbv and greater in Ohio, Pennsylvania, Maryland, West Virginia, and Virginia. Maximum increases are compared with maximum decreases for the August period using second-order DDM and are found, in aggregate, to be greater in magnitude by 42%. When evaluated during hours when ozone concentrations exceed 0.060 ppm, the maximum increases in ozone are higher than decreases by 82%. The spatial extent of ozone increase in both cases is about triple that of reduction.

Sensitivity analysis of ozone formation and transport for a central California air pollution episode.

We developed a first- and second-order sensitivity analysis approach with the decoupled direct method to examine spatial and temporal variations of ozone-limiting reagents and the importance of local vs upwind emission sources in the San Joaquin Valley of central California for a 5 day ozone episode (Jul 29th to Aug 3rd, 2000). Despite considerable spatial variations, nitrogen oxides (NO(x)) emission reductions are overall more effective than volatile organic compound (VOC) control for attaining the 8 h ozone standard in this region for this episode, in contrast to the VOC control that works better for attaining the prior 1 h ozone standard. Interbasin source contributions of NO(x) emissions are limited to the northern part of the SJV, while anthropogenic VOC (AVOC) emissions, especially those emitted at night, influence ozone formation in the SJV further downwind. Among model input parameters studied here, uncertainties in emissions of NO(x) and AVOC, and the rate coefficient of the OH + NO2 termination reaction, have the greatest effect on first-order ozone responses to changes in NO(x) emissions. Uncertainties in biogenic VOC emissions only have a modest effect because they are generally not collocated with anthropogenic sources in this region.

An integrated framework for multipollutant air quality management and its application in Georgia.

Air protection agencies in the United States increasingly confront non-attainment of air quality standards for multiple pollutants sharing interrelated emission origins. Traditional approaches to attainment planning face important limitations that are magnified in the multipollutant context. Recognizing those limitations, the Georgia Environmental Protection Division has adopted an integrated framework to address ozone, fine particulate matter, and regional haze in the state. Rather than applying atmospheric modeling merely as a final check of an overall strategy, photochemical sensitivity analysis is conducted upfront to compare the effectiveness of controlling various precursor emission species and source regions. Emerging software enables the modeling of health benefits and associated economic valuations resulting from air pollution control. Photochemical sensitivity and health benefits analyses, applied together with traditional cost and feasibility assessments, provide a more comprehensive characterization of the implications of various control options. The fuller characterization both informs the selection of control options and facilitates the communication of impacts to affected stakeholders and the public. Although the integrated framework represents a clear improvement over previous attainment-planning efforts, key remaining shortcomings are also discussed.

Control strategy optimization for attainment and exposure mitigation: case study for ozone in Macon, Georgia.

Implementation of more stringent 8-hour ozone standards has led the U.S. Environmental Protection Agency to designate nonattainment status to 474 counties nationwide, many of which had never previously violated air quality standards. As states select emission control measures to achieve attainment in these regions, their choices pose significant implications to local economies and the health of their citizens. Considering a case study of one such nonattainment region, Macon, Georgia, we develop a menu of potential controls that could be implemented locally or in neighboring parts of the state. The control menu offers the potential to control about 20-35% of ozone precursor emissions in most Georgia regions, but marginal costs increase rapidly beyond 15-20%. We link high-order ozone sensitivities with the control menu to identify cost-optimized strategies for achieving attainment and for alternative goals such as reducing spatially averaged or population-weighted ozone concentrations. Strategies targeted toward attainment of Macon ozone would prioritize local reductions of nitrogen oxides, whereas controls in the more densely populated Atlanta region are shown to be more effective for reducing statewide potential population exposure to ozone. A U.S. EPA-sanctioned approach for demonstrating ozone attainment with photochemical models is shown to be highly dependent on the choice of a baseline period and may not foster optimal strategies for assuring attainment and protecting human health.

Cost-benefit analysis in the selection of efficient multipollutant strategies.

Pollution control efforts are motivated by the desire to protect human health and the environment. Often, those efforts involve selecting among multiple options for attaining air quality objectives. For example, state and local decision-makers must choose the mix of control strategies for meeting the requirements of the National Ambient Air Quality Standards (NAAQS) and the Regional Haze Rule. We demonstrate that including assessments of the human health and environmental benefits when evaluating alternative strategies may help decision-makers to identify multipollutant attainment strategies that achieve greater net benefits than would accrue under strategies optimized for cost alone. This paper presents a conceptual framework that decision-makers could use to choose among alternative multipollutant control strategies, accounting for the benefits and the costs of different types and locations of emissions reductions.

Nonlinear response of ozone to emissions: source apportionment and sensitivity analysis.

For secondary air pollutants, precursor emissions may impact concentrations in nonlinear and interdependent manners. We explore the nonlinear responses of one such pollutant, ozone, to emissions of its precursors, nitrogen oxides (NOx) and volatile organic compounds. Modeling is conducted for a high ozone episode in the southeastern United States, applying a second-order direct sensitivity method in a regional air quality model. As applied here, the sensitivity method neglects most aerosol and aqueous chemistry processes. Inclusion of second-order sensitivities is shown to enable accurate characterization of response to large perturbations in emissions. An index is introduced to characterize the nonlinearity of ozone response to NOx emitted from each source region. Nonlinearity is found to increase with the tonnage and emission density of the source region. Interactions among the impacts of emission sources are shown to lead to discrepancies between source contribution attributed to an ensemble of emitters and the sum of the contributions attributed to each component. A method is introduced for applying these "cross-sensitivity" interactions to assess the uncertainty of sensitivity and source apportionment estimates arising from uncertainty in an emissions inventory. For ozone response to NOx, underestimates in emission rates lead to underprediction of total source contribution but overprediction of per-ton sensitivity.