The adjoint of CMAQ

Substantial differences in source contributions to carbon emissions and health damage necessitate balanced synergistic control plans in China

China's strategy to concurrently address climate change and air pollution mitigation is hindered by a lack of comprehensive information on source contributions to health damage and carbon emissions. Here we show notable discrepancies between source contributions to CO2 emissions and fine particulate matter (PM2.5)-related mortality by using adjoint emission sensitivity modeling to attribute premature mortality in 2017 to 53 sector and fuel/process combinations with high spatial resolution. Our findings reveal that monetized PM2.5 health damage exceeds climate impacts in over half of the analyzed subsectors. In addition to coal-fired energy generators and industrial boilers, the combined health and climate costs from energy-intensive processes, diesel-powered vehicles, domestic coal combustion, and agricultural activities exceed 100 billion US dollars, with health-related costs predominating. This research highlights the critical need to integrate the social costs of health damage with climate impacts to develop more balanced mitigation strategies toward these dual goals, particularly during fuel transition and industrial structure upgrading.

A case study of surface ozone source contributions in the Seoul metropolitan area using the adjoint of CMAQ

To quantitatively investigate the transboundary behaviors and source attributions of ozone (O3) and its precursor species over East Asia, we utilize the adjoint technique in the CMAQ modeling system (the CMAQ adjoint). Our focus is on the Seoul Metropolitan Area (SMA) in South Korea, which is the receptor region of this study. We examine the contributions of both local and transported emissions to an O3 exceedance episode observed on June 3, 2019, estimating up to four days in advance. By using the CMAQ adjoint, we can determine the sensitivity of O3 remaining in the SMA to changes in O3 precursor emissions (emissions-based sensitivity) and concentrations (concentrations-based sensitivity) along the long-range transport pathways and emission source regions overseas. These include Beijing-Tianjin-Hebei (BTH), Shandong, Yangtze River Delta (YRD), and Central China. CMAQ adjoint-derived source attributions suggest that overseas precursor emissions and O3 contributed significantly to the O3 exceedance event in SMA. The emissions-based sensitivities revealed that precursor emissions originating from Shandong, YRD, Central China, and BTH contributed 11.42 ppb, 4.28 ppb, 1.24 ppb, 0.9 ppb, respectively, to the O3 exceedance episode observed in the SMA. Meanwhile, Korean emissions contributed 31.1 ppb. Concentrations-based sensitivities indicated that 19.3 ppb of contributions originated in regions beyond eastern China and directly affected the O3 level in the SMA in the form of background O3. In addition to capturing the transboundary movements of air parcels between the source and receptor regions, we performed HYSPLIT backward trajectory analyses. The results align with the trajectories of O3 and its precursors that we obtained from the adjoint method. This study represents a unique effort in employing the adjoint technique to examine the impacts of regional O3 on South Korea, utilizing a combination of emissions-based and concentrations-based sensitivities.Implications: This research brings to light the critical role of both local and regional precursor emissions in contributing to an ozone (O3) exceedance event in the Seoul Metropolitan Area (SMA), South Korea. Utilizing the CMAQ adjoint technique, a novel approach in the context of South Korea's O3 investigations, we were able to delineate the quantitative contributions of different regions, both within South Korea and from overseas areas such as Beijing, Shandong, Shanghai, and Central China. Importantly, the results underscore the substantial influence of transboundary pollutant transport, emphasizing the need for international collaboration in addressing air quality issues. As metropolitan areas around the globe grapple with similar challenges, the methodology and insights from this study offer a potent tool and framework for regions seeking to understand and mitigate the impacts of O3 on human health and the environment. By integrating different sensitivity types, coupled with HYSPLIT backward trajectory analyses, this research equips policymakers with comprehensive data to design targeted interventions, emphasizing the significance of collaborative efforts in tackling regional air pollution challenges. However, it's important to note the limitation of this study, which is a case study conducted over a short time period. This constraint may impact the generalizability of the findings and suggests a need for further research to validate and expand upon these results.

A 1-D model to retrieve the vertical profiles of minor atmospheric constituents for cloud microphysical modeling: I. Formulation and validation

Determining the number concentration of minor constituents in the atmosphere is very important as it determines the whole tropospheric chemistry process. These constituents may act as cloud condensation nuclei (CCN) and ice nuclei (IN), impacting heterogeneous nucleation inside the cloud. However, the estimations of the number concentration of CCN/IN in cloud microphysical parameters are associated with uncertainties. In the present work, a hybrid Monte Carlo Gear solver has been developed to retrieve profiles of CH₄, N₂O, and SO₂. The idealized experiments have been carried out using this solver for retrieving vertical profiles of these constituents over four megacities, viz., Delhi, Mumbai, Chennai, and Kolkata. Community Long-term Infrared Microwave Coupled Atmospheric Product System (CLIMCAPS) dataset around 0800 UTC (2000UTC) has been used for initializing the number concentration of CH₄, N₂O, and SO₂ for daytime (nighttime). The daytime (nighttime) retrieved profiles have been validated using 2000 UTC (next day 0800 UTC) CLIMCAPS products. ERA5 temperature dataset has been used to estimate the kinematic rate of reactions with 1000 perturbations determined using Maximum Likelihood Estimation (MLE). The retrieved profiles and CLIMCAPS products are in very good agreement, as evidenced by the percentage difference between them within the range of 1.3 × 10^-5-60.8 % and the coefficient of determination mainly within the range between 81 and 97 %. However, during the passage of tropical cyclone and western disturbance, its value became as low as 27 and 65 % over Chennai and Kolkata, respectively. The enactment of synoptic scale systems such as western disturbances, tropical cyclone Amphan, and easterly waves caused disturbed weather over these megacities-the retrieved profiles during disturbed weather cause large deviations of vertical profiles of N₂O. However, the profiles of CH₄ and SO₂ have less deviation. It is inferred that incorporating this methodology in the dynamical model will be useful to simulate the realistic vertical profiles of the minor constituents in the atmosphere.

Location-Specific Control of Precursor Emissions to Mitigate Photochemical Air Pollution

The effects of precursor emission controls on air quality can vary greatly depending on where emission reductions occur. We use the adjoint of the Community Multiscale Air Quality (CMAQ) model to evaluate impacts of spatially targeted NO_x emission reductions on odd oxygen (O_x = O₃ + NO₂). The air quality responses studied here include one population-weighted regionwide and three city-level receptors in Central California. We map high-priority locations for NO_x control and their changes over decadal time scales. The desirability of NO_x-focused emission control programs has increased between 2000 and 2022. We find for present-day conditions that reducing NO_x emissions by 28% from targeted high-priority locations can achieve 60% of the air quality benefits of uniform NO_x reductions at all locations. High-priority source locations are found to differ for individual city-level versus regionwide receptors of interest. While high-impact emission hotspots for improving city-level metrics are found within the city itself or closely adjacent, the spatial pattern of emission hotspots for improving regionwide air quality is more complex and requires comprehensive consideration of upwind sources. Results of this study can help to inform strategic decision-making at local and regional levels about where to prioritize emission control efforts.

Accurate prediction of air quality response to emissions for effective control policy design

Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O₃ and PM_2.5 and the emissions of their precursors. However, the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O₃ and PM_2.5, which are formed by multiple precursors under complex physical and chemical processes; this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions. Different from the traditional sensitivity analysis and source apportionment methods, the reduced form models (RFMs) based on chemical transport models (CTMs) are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost. Here we review recent approaches used in RFMs and compare their structures, advantages and disadvantages, performance and applications. In general, RFMs are classified into three types including (1) sensitivity-based models, (2) models with simplified chemistry and physical processes, and (3) statistical models, with considerable differences in principles, characteristics and application ranges. The prediction of nonlinear responses by RFMs enables more in-depth analysis, not only in terms of real-time prediction of concentrations and quantification of human exposure, health impacts and economic damage, but also in optimizing control policies. Notably, data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management. In future studies, improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses. Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes, which pose great challenges to the quantification of response relationships. Additionally, with rising requirements for fine-scale air quality management, improving the performance of urban-scale simulations is worth considering. In short, accurate predictions of the response of air quality to emissions, though challenging, holds great promise for the present as well as for future scenarios.

How have Divergent Global Emission Trends Influenced Long-range Transported Ozone to North America?

Several locations across the United States in non-compliance with the national standard for ground-level ozone (O3) are thought to have sizeable influences from distant extra-regional emission sources or natural stratospheric O3, which complicates design of local emission control measures. To quantify the amount of long-range transported O3 (LRT O3), its origin, and change over time, we conduct and analyze detailed sensitivity calculations characterizing the response of O3 to emissions from different source regions across the Northern Hemisphere in conjunction with multi-decadal simulations of tropospheric O3 distributions and changes. Model calculations show that the amount of O3 at any location attributable to sources outside North America varies both spatially and seasonally. On a seasonal-mean basis, during 1990-2010, LRT O3 attributable to international sources steadily increased by 0.06-0.2 ppb yr-1 at locations across the United States and arose from superposition of unequal and contrasting trends in individual source-region contributions, which help inform attribution of the trend evident in O3 measurements. Contributions of emissions from Europe steadily declined through 2010, while those from Asian emissions increased and remained dominant. Steadily rising NOx emissions from international shipping resulted in increasing contributions to LRT O3, comparable to those from Asian emissions in recent years. Central American emissions contribute a significant fraction of LRT O3 in southwestern United States. In addition to the LRT O3 attributable to emissions outside of North America, background O3 across the continental United States is comprised of a sizeable and spatially variable fraction that is of stratospheric origin (29-78%).

Development of GRAPES-CUACE adjoint model version 2.0 and its application in sensitivity analysis of ozone pollution in north China

We presented the development of the gaseous chemistry adjoint module of the meteorological-chemical model system GRAPES-CUACE (Global/Regional Assimilation and PrEdiction System coupled with CMA Unified Atmospheric Chemistry Environmental Forecasting System) on the basis of the previously constructed aerosol adjoint module. The latest version of the GRAPES-CUACE adjoint model mainly includes the adjoint of the physical and chemical processes, the adjoint of the transport processes, and the adjoint of interface programs, of both gas and aerosol. The adjoint implementation was validated for the full model, and adjoint results showed good agreement with brute force sensitivities. We also applied the newly developed adjoint model to the sensitivity analysis of an ozone episode occurred in Beijing on July 2, 2017, as well as the design of emission-reduction strategies for this episode. The relationships between the ozone concentration and precursor emissions were well captured by the adjoint model. It is indicated that for a case used here, the Beijing peak ozone concentration was influenced mostly by local emissions (6.2-24.3%), as well as by surrounding emissions, including Hebei (4.4-16.8%), Tianjin (1.8-6.6%), Shandong (1.8-2.6%), and Shanxi (<1%). In addition, reduction of NO_x, VOCs, and CO emissions in these regions would effectively decrease the Beijing peak ozone concentration. This study highlights the capability of GRAPES-CUACE adjoint model in quantifying "emission-concentration" relationship and in providing guidance for environmental control policy.

Responses of Photochemical Air Pollution in California's San Joaquin Valley to Spatially and Temporally Resolved Changes in Precursor Emissions

Ground-level ozone adversely affects human health and ecosystems. The effectiveness of control programs depends on which precursor(s) are controlled, by how much, and where and when emission reductions occur. We use the adjoint of the Community Multiscale Air Quality model to investigate odd oxygen (O_x ≡ O₃ + NO₂) sensitivities in California's San Joaquin Valley (SJV) to precursor emissions from local and upwind sources. Sensitivities are mapped and disaggregated by hour and day. Taken together, impacts of precursor emissions in the San Francisco Bay area and Sacramento Valley are similar in magnitude to impacts of local SJV emissions. Same-day emission sensitivities are mostly attributable to local sources, with the most influential anthropogenic emissions of VOCs (volatile organic compounds) and NO_x (nitrogen oxides) occurring in the morning (9-11 am) and early afternoon hours (1-3 pm), respectively. For the northernmost SJV receptor, the influence from Sacramento Valley emissions peaks 5-6 h later than Bay area emissions; this difference diminishes for SJV receptors located further downwind. Results show a shift toward more NO_x-sensitive conditions in the afternoon with all but the southernmost receptor shifting from VOC- to NO_x-sensitive conditions. We also evaluate opportunities to control pollution through shifts in precursor emission location and timing.

Characterization factors and other air quality impact metrics: Case study for PM2.5-emitting area sources from biofuel feedstock supply

In this paper, we develop a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health for applications in air quality planning and life cycle impact assessment (LCIA). Our framework is based on a pollutant's characterization factor (CF) and three new metrics: Available Regulatory Capacity for Incremental Emissions (ARCIE), Source CF Ratio, and Activity Health Impact (AHI) Ratio. ARCIE can be used to assess whether a receptor location has capacity to accommodate additional source emissions while complying with regulatory limits. We present CF as a midpoint indicator of health impacts per unit mass of emitted pollutant. Source CF Ratio enables comparison of potential new-source locations based on human health impacts. The AHI Ratio estimates the health impacts of a pollutant in relation to the utilization of the source for each unit of product or service. These metrics can be applied to any pollutant, energy source sector (e.g., agriculture, electricity), source type (point, line, area), and spatial modeling domain (nation, state, city, region). We demonstrate these metrics through a case study of fine particulate (PM_2.5) emissions from U.S. corn stover harvesting and local processing at various scales, representing steps in the biofuel production process. We model PM_2.5 formation in the atmosphere using a novel reduced-complexity chemical transport model called the Intervention Model for Air Pollution (InMAP). Through this case study, we present the first area-source PM_2.5 CFs that address the recommendations of several LCIA studies to establish spatially explicit CFs specific to an energy source sector or type. Overall, the framework developed in this work provides multiple new ways to consider the potential impacts of air emissions through spatially differentiated metrics.

Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations

In this research, a new time-resolved emission inversion system was developed to investigate variations in SO2 emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO2 inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO2 emission variations and the corresponding prediction improvement. The SO2 emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO2 emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO2 emission inventory significantly improved the model SO2 predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM2.5 was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system.

Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System

We present a parametric Kalman filter data assimilation system using GOSAT methane observations within the hemispheric CMAQ model. The assimilation system produces forecasts and analyses of concentrations and explicitly computes its evolving error variance while remaining computationally competitive with other data assimilation schemes such as 4-dimensional variational (4D-Var) and ensemble Kalman filter (EnKF). The error variance in this system is advected using the native advection scheme of the CMAQ model and updated at each analysis while the error correlations are kept fixed. We discuss extensions to the CMAQ model to include methane transport and emissions (both anthropogenic and natural) and perform a bias correction for the GOSAT observations. The results using synthetic observations show that the analysis error and analysis increments follow the advective flow while conserving the information content (i.e., total variance). We also demonstrate that the vertical error correlation contributes to the inference of variables down to the surface. In a companion paper, we use this assimilation system to obtain optimal assimilation of GOSAT observations.

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.

A new approach for health-oriented ozone control strategy: Adjoint-based optimization of NOx emission reductions using metaheuristic algorithms

While levels of particulate matters in the Pearl River Delta Region (PRD) show a significant reduction, ozone (O₃) has an opposite increasing trend, becoming the critical air quality target in this decade. Emission control strategies are typically formulated sector by sector, spatial variability in emissions reductions and health impacts of air pollutants may not be taken into account, affecting the overall effectiveness of control strategies. This study proposes an adjoint-based optimization framework to facilitate health-oriented O₃ control over PRD. The location-specific adjoint sensitivity coefficients, which reflect the spatiotemporal influences from emissions of nitrogen dioxide (NO_x) on O₃ health impacts, are combined with metaheuristic algorithms to minimize the O₃-related premature mortalities over receptor regions. Using the proposed optimization methodology, the regional O₃ health benefits under current emission reduction policy can be increased by 16-27%. The results show that relatively larger NO_x emissions reductions occurred at highly developed and populated areas. Particularly, significant reductions in NO_x emissions are observed at Shenzhen and urban Guangzhou. Furthermore, implementing regional NO_x emissions abatement has advantages to achieve an overall O₃ health benefits for all cities. The interregional influences of NO_x emissions abatement between cities indicate a promising strategy of health-oriented O₃ control in PRD.

Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model

Attributing sources of air pollution events by deploying an efficient observational network is an important and interesting problem in air quality control and forecast studies, but it is very challenging. In order to estimate the sensitivities of pollution events to emission sources, a comprehensive framework is built based on a horizontal 2-dimensional transport model and its adjoint in solving this problem. In an analysis of an idealized air pollution event of PM_2.5 over the region of North China, an objective function is defined to optimally estimate the initial concentrations and emission sources through a series of minimization procedures. Results by means of the 4-dimensional variational approach show that, with the optimal initial conditions and emission sources, the model can successfully forecast the pollution event in a few days. The optimal observing network based on sensitivity analysis takes only one third of the cost but greatly retains predictability skill compared to the full-grid observing system, while nearly no predictability skill is detectable if the same number of observational sites is randomly deployed. We evaluate air pollution predictability in the point of focusing on to what degree the root mean square errors between the modeled concentration and the targeted air pollution are limited by the optimal observational network. Results show that air pollution predictability in association with the optimal observational network is limited in the time scales about 6 days. With the high efficiency and in an economic fashion, such a sensitivity-based optimal observing system holds promise for accurately predicting an air pollution event in the targeted area once the adjoint and variational procedure of a realistic atmosphere model including transport and chemical processes is performed.

The Combined Effects of Fine Particulate Matter and Temperature on Preterm Birth in Seoul, 2010-2016

Background: Preterm birth contributes to the morbidity and mortality of newborns and infants. Recent studies have shown that maternal exposure to particulate matter and extreme temperatures results in immune dysfunction, which can induce preterm birth. This study aimed to evaluate the association between fine particulate matter (PM_2.5) exposure, temperature, and preterm birth in Seoul, Republic of Korea. Methods: We used 2010–2016 birth data from Seoul, obtained from the Korea National Statistical Office Microdata. PM_2.5 concentration data from Seoul were generated through the Community Multiscale Air Quality (CMAQ) model. Seoul temperature data were collected from the Korea Meteorological Administration (KMA). The exposure period of PM_2.5 and temperature were divided into the first (TR1), second (TR2), and third (TR3) trimesters of pregnancy. The mean PM_2.5 concentration was used in units of ×10 µg/m³ and the mean temperature was divided into four categories based on quartiles. Logistic regression analyses were performed to evaluate the association between PM_2.5 exposure and preterm birth, as well as the combined effects of PM_2.5 exposure and temperature on preterm birth. Result: In a model that includes three trimesters of PM_2.5 and temperature data as exposures, which assumes an interaction between PM_2.5 and temperature in each trimester, the risk of preterm birth was positively associated with TR1 PM_2.5 exposure among pregnant women exposed to relatively low mean temperatures (<3.4 °C) during TR1 (OR 1.134, 95% CI 1.061–1.213, p < 0.001). Conclusions: When we assumed the interaction between PM_2.5 exposure and temperature exposure, PM_2.5 exposure during TR1 increased the risk of preterm birth among pregnant women exposed to low temperatures during TR1. Pregnant women should be aware of the risk associated with combined exposure to particulate matter and low temperatures during TR1 to prevent preterm birth.

Effect of heat waves and fine particulate matter on preterm births in Korea from 2010 to 2016

BACKGROUND

Previous studies have reported that fine particulate matter (PM_2.5) affects the incidence of premature births. In addition, recent studies have suggested that heat waves have a negative impact on birth outcomes. However, the combined effect of PM_2.5 and heat waves on the incidence of premature birth is controversial. This study investigated the independent and combined effects of PM_2.5 and heat wave exposures during the 1st and 2nd trimesters on premature birth.

METHODS

The National Statistical Office of Korea provided birth data from 2010 to 2016. Preterm birth was defined as birth between 22 and 36 weeks. To assess the exposure to PM_2.5 and heat waves, we used PM_2.5 data estimated by the Community Multiscale Air Quality Modeling System (CMAQ) and heat wave warning data provided by the Korea Meteorological Administration. A multivariate logistic regression was used to investigate the risk of preterm birth according to the exposure to PM_2.5 and heat waves during the 1st and 2nd trimesters, and it was adjusted for residential area, year of birth, season of birth, parity, education level of the mother, age of the mother, and sex of the baby.

RESULTS

In the 2nd trimester, compared with the 0 h of heat wave exposure (≤67 percentile), 62.50-314.00 h (79-88 percentile) and>315.00 h of heat wave exposure (>88 percentile) were both significantly associated with preterm birth (OR for 79-88 percentile, 1.037, 95% CI, 1.003-1.073; OR for > 88 percentile, 1.174, 95% CI, 1.134-1.215). However, PM_2.5 exposure was not significantly associated with preterm birth. On the other hand, in the analysis to evaluate the combined effect of PM_2.5 and heat wave exposures of the 2nd trimester, compared with 0 h of heat wave exposure (≤67 percentile) and<11.64 μg/m³ (≤25 percentile) of PM_2.5, 11.64-22.74 μg/m³ (≤25 percentile), 22.74-27.58 μg/m³ (26-50 percentile), and 27.57-32.39 μg/m³ (51-75 percentile) of PM_2.5 exposure combined with>315.00 h of heat wave exposure (>88 percentile) were all significantly associated with preterm birth. In addition, the effect size was increased with an increase of PM_2.5 exposure (OR for ≤ 25 percentile, 1.148, 95% CI, 1.095-1.203; OR for 26-50 percentile, 1.248, 95% CI, 1.178-1.323; OR for 51-75 percentile, 1.370, 95% CI, 1.245-1.507).

CONCLUSION

Our findings suggest that the combined effect of heat wave and PM_2.5 exposure during the 2nd trimester on the risk of preterm birth was greater than that of each exposure alone. In other words, exposure to PM_2.5 increases the impact of heat waves on the risk of preterm birth. These results indicate that control of prenatal exposure to fine particular matter and extreme temperatures is important for the prevention of preterm birth.

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.

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.

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.

Numerical study of air pollution over a typical basin topography: Source appointment of fine particulate matter during one severe haze in the megacity Xi'an

Many cities are located in lands with typical basin topographies, which are not conducive to the spread of air pollutants. In the winter of 2016/2017, a severe haze happened in Xi'an, the main city in the Guanzhong Basin in central China. When the peak daily concentration of fine particulate matter (PM_2.5) reaches 499 μg/m³, the source of the atmospheric pollution needs to be found urgently in order to take countermeasures. The comprehensive air quality model with extensions, coupled with the tracer tagging particulate source apportionment technology (PSAT) module, and an improved emission inventory, higher grid resolution, and bigger inner domain area, have been applied to quantify the contributions of local and regional emissions to the PM_2.5 pollutions. The model performed well in time period considered in this study. The correlation of the simulated daily PM_2.5 concentration data reaches 0.82, and the fraction of predictions within a factor of two of observations approaches 84%. With the PSAT module, the PM_2.5 contributions from local and regional sources to the urban centre and rural areas during the severe winter haze event are analysed in detail. The PM_2.5 concentrations in the urban centre in Xi'an is mainly originating from local emissions (60%), and Xianyang City is the largest contributor among the surrounding source regions (11.6%), while the transportation sector outside the Shaanxi Province (5.1%) also contributes significantly. Comparatively, the rural areas have lower local contributions and higher transport contributions. In particular, in the northern rural area Yanliang, the contribution from surrounding source regions approaches 82%. The results of this study suggest that to improve the air quality in a typical basin city, a regional-scale coordinated emissions control should be used, focusing on the emissions from both local and surrounding areas.

Mapping ozone source-receptor relationship and apportioning the health impact in the Pearl River Delta region using adjoint sensitivity analysis

While fine particulate matters are decreasing in the Pearl River Delta (PRD) region, the regional ozone (O₃) shows an increasing trend that affects human health, leading to an urgent need for scientific understanding of source-receptor relationship between O₃ and its precursor emissions given the changing background composition. We advanced and applied an adjoint air quality model to map contributions of individual O₃ precursor emission sources [nitrogen oxides (NO_x) and volatile organic compound (VOC)] at each location to annual regional O₃ concentrations and to identify the possible dominant influential pathways of emission sources to O₃ at different spatiotemporal scales. Additionally, we introduced the novel adjoint sensitivity approach to assess the relationship between precursor emissions and O₃-induced premature mortality. Adjoint results show that Shenzhen was a major source contributor to regional O₃ throughout all seasons, of which 49.4% (3.8%) were from its NO_x (VOC) emissions. Local emissions (within PRD) contributed to 83% of the regional O₃ whereas only ~54% of the estimated ~4000 regional O₃-induced premature mortalities. The discrepancy between these two contributions was because O₃-induced mortalities are dependent on not only O₃ concentration, but incident rate and population density. We also found that a city with low O₃-induced mortalities could have significant emission contributions to health impact in the region since the transport pathways could be through transport of local O₃ or through transport of O₃ precursors that form regional O₃ thereafter. It is therefore necessary to formulate emission control policies from both air quality and public health perspectives, and it is also critical to have better understanding of influential pathways of emission sources to O₃.

Fine Particulate Air Pollution from Electricity Generation in the US: Health Impacts by Race, Income, and Geography

Electricity generation is a large contributor to fine particulate matter (PM_2.5) air pollution. However, the demographic distribution of the resulting exposure is largely unknown. We estimate exposures to and health impacts of PM_2.5 from electricity generation in the US, for each of the seven Regional Transmission Organizations (RTOs), for each US state, by income and by race. We find that average exposures are the highest for blacks, followed by non-Latino whites. Exposures for remaining groups (e.g., Asians, Native Americans, Latinos) are somewhat lower. Disparities by race/ethnicity are observed for each income category, indicating that the racial/ethnic differences hold even after accounting for differences in income. Levels of disparity differ by state and RTO. Exposures are higher for lower-income than for higher-income, but disparities are larger by race than by income. Geographically, we observe large differences between where electricity is generated and where people experience the resulting PM_2.5 health consequences; some states are net exporters of health impacts, other are net importers. For 36 US states, most of the health impacts are attributable to emissions in other states. Most of the total impacts are attributable to coal rather than other fuels.

Source contributions of surface ozone in China using an adjoint sensitivity analysis

Air pollution has become an adverse environmental problem in China, resulting in serious public health impacts. This study advanced and applied the CMAQ adjoint model to quantitatively assess the source-receptor relationships between surface ozone (O₃) changes over different receptor regions and precursor emissions across all locations in China. Five receptor regions were defined based on the administrative division, including northern China (NC), southern China (SC), Pearl River Delta region (PRD), Yangtz River Delta region (YRD), and Beijing-Tianjin-Hebei region (BTH). Our results identified the different influential pathways of atmospheric processes and emissions to O₃ pollution. We found that the atmospheric processes such as horizontal and vertical advection could offset the O₃ removal through chemical reactions in VOC-limited areas inside the receptor regions. In addition, O₃ pollution can be induced by transport of O₃ directly or its precursors. Our results of relative source contributions to O₃ show that transboundary O₃ pollution was significant in SC, NC and YRD, while the O₃ pollution in PRD and BTH were more contributed by local sources. Anhui, Hubei and Jiangsu provinces were the three largest source areas of NO_x and VOC emissions to O₃ in SC (>52%) and YRD (>69%). NO_x and VOC emissions from Tianjin and Beijing were the largest contributors to O₃ in NC (>34%) and BTH (>51%). PRD was the dominant source areas of NO_x (>89%) and VOC emissions (~98%) to its own regional O₃.

A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations-a case study of Xuzhou, China

Carbon monoxide (CO) emission inventory data are crucial for air quality control. However, the emission inventories are labor-intensive and time-consuming and generally have large uncertainties. In this study, we developed a new regional data assimilation system (TracersTracker) for estimating the surface CO emission flux from continuous mixing ratio observations using the proper orthogonal decomposition (POD)-based four-dimensional variational (4D-VAR) data assimilation method (POD-4DVar) and a coupled regional model (Weather Research and Forecasting model (WRF) with the Models-3 Community Multi-scale Air Quality (CMAQ) model). This system was applied to estimate CO emissions in Xuzhou city, China. An experiment was conducted with the continuous hourly surface CO mixing ratio observations from 21 monitoring towers in January and July of 2016. The experimental results of the system were examined and compared with the continuous surface CO observations (a priori emission). We found that the retrieved CO emission fluxes were higher than the a priori emission and were mainly distributed in urban and industrial areas, which were 104% higher in January (winter) and 44% higher in July (summer).

A Call for an Aloft Air Quality Monitoring Network: Need, Feasibility, and Potential Value

Changing precursor emission patterns in conjunction with stringent health protective air quality standards necessitate accurate quantification of nonlocal contributions to ozone pollution at a location due to atmospheric transport, that by nature predominantly occurs aloft nocturnally. Concerted efforts to characterize ozone aloft on a continuous basis to quantify its contribution to ground-level concentrations, however, are lacking. By applying our classical understanding of air pollution dynamics to analyze variations in widespread surface-level ozone measurements, in conjunction with process-based interpretation from a comprehensive air pollution modeling system and detailed backward-sensitivity calculations that quantitatively link surface-level and aloft pollution, we show that accurate quantification of the amount of ozone in the air entrained from aloft every morning as the atmospheric boundary layer grows is the key missing component for characterizing background pollution at a location, and we propose a cost-effective continuous aloft ozone measurement strategy to address critical scientific gaps in current air quality management. Continuous aloft air pollution measurements can be achieved cost-effectively through leveraging advances in sensor technology and proliferation of tall telecommunications masts. Resultant improvements in ozone distribution characterization at 400-500 m altitude are estimated to be 3-4 times more effective in characterizing the surface-level daily maximum 8-h average ozone (DM8O3) than improvements from surface measurements since they directly quantify the amount of pollution imported to a location and furnish key missing information on processes and sources regulating background ozone and its modulation of ground-level concentrations. Since >80% of the DM8O3 sensitivity to tropospheric ozone is potentially captured through measurements between 200 and 1200 m altitude (a possible design goal for future remote sensing instrumentation), their assimilation will dramatically improve air quality forecast and health advisories.

Public health costs accounting of inorganic PM2.5 pollution in metropolitan areas of the United States using a risk-based source-receptor model

In order to design effective strategies to reduce the public health burden of ambient fine particulate matter (PM_2.5) imposed in an area, it is necessary to identify the emissions sources affecting that location and quantify their contributions. However, it is challenging because PM_2.5 travels long distances and most constituents are the result of complex chemical processes. We developed a reduced-form source-receptor model for estimating locations and magnitudes of downwind health costs from a source or, conversely, the upwind sources that contribute to health costs at a receptor location. Built upon outputs from a state-of-the-art air quality model, our model produces comprehensive risk-based source apportionment results with trivial computational costs. Using the model, we analyzed all the sources contributing to the inorganic PM_2.5 health burden in 14 metropolitan statistical areas (MSAs) in the United States. Our analysis for 12 source categories shows that 80-90% of the burden borne by these areas originates from emissions sources outside of the area and that emissions sources up to 800 km away need to be included to account for 80% of the burden. Conversely, 60-80% of the impacts of an MSA's emissions occurs outside of that MSA. The results demonstrate the importance of regionally coordinated measures to improve air quality in metropolitan areas.

Attribution of PM2.5 exposure in Beijing-Tianjin-Hebei region to emissions: implication to control strategies

The Beijing-Tianjin-Hebei (BTH) region is one of the most heavily polluted regions in China, with both high PM_2.5 concentrations and a high population density. A quantitative source-receptor relationship can provide valuable insights that can inform effective emission control strategies. Both source apportionment (SA) and source sensitivity (SS) can provide such information from different perspectives. In this study, both methods are applied in northern China to identify the most significant emission categories and source regions for PM_2.5 exposure in BTH in 2013. Despite their differences, both models show similar distribution patterns for population and simulated PM_2.5 concentrations, resulting in overall high PM_2.5 exposure values (approximately 110μg/m³) and particularly high exposure values during the winter (approximately 200μg/m³). Both methods show that local emissions play a dominant role (70%), with some contribution from surrounding provinces (e.g., Shandong) via regional transport. The two methods also agree on the priority of local emission controls: both identify industrial, residential, and agricultural emissions as the top three categories that should be controlled locally. In addition, the effect of controlling agricultural ammonia emissions is approximately doubled when the co-benefits of reducing nitrate are considered. The synthesis of SA and SS for addressing specific categories of emissions provides a quantitative basis for the development of emission control strategies and policies for controlling PM_2.5 in China.

InMAP: A model for air pollution interventions

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations-the air pollution outcome generally causing the largest monetized health damages-attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.

Optimal Ozone Control with Inclusion of Spatiotemporal Marginal Damages and Electricity Demand

Marginal damage (MD), or damage per ton of emission, is a policy metric used for effective pollution control and reducing the corresponding adverse health impacts. However, for a pollutant such as NOx, the MD varies by the time and location of the emissions, a complication that is not adequately accounted for in the currently implemented economic instruments. Policies accounting for MD information would aim to encourage emitters with large MDs to reduce their emissions. An optimization framework is implemented to account for NOx spatiotemporal MDs calculated through adjoint sensitivity analysis and to simulate power plants' behavior under emission and simplified electricity constraints. The results from a case study of U.S. power plants indicate that time-specific MDs are high around noon and low in the evening. Furthermore, an emissions reduction of about 40% and a net benefit of about $1200 million can be gained for this subset of power plants if a larger fraction of the electricity demand is supplied by power plants at low-damage times and in low-damage locations. The results also indicate that the consideration of temporal effects in NOx control policies results in a comparable net benefit to the consideration of spatial or spatiotemporal effects, thus providing a promising option for policy development.

High-resolution mapping of sources contributing to urban air pollution using adjoint sensitivity analysis: benzene and diesel black carbon

The adjoint of the Community Multiscale Air Quality (CMAQ) model at 1 km horizontal resolution is used to map emissions that contribute to ambient concentrations of benzene and diesel black carbon (BC) in the San Francisco Bay area. Model responses of interest include population-weighted average concentrations for three highly polluted receptor areas and the entire air basin. We consider both summer (July) and winter (December) conditions. We introduce a novel approach to evaluate adjoint sensitivity calculations that complements existing methods. Adjoint sensitivities to emissions are found to be accurate to within a few percent, except at some locations associated with large sensitivities to emissions. Sensitivity of model responses to emissions is larger in winter, reflecting weaker atmospheric transport and mixing. The contribution of sources located within each receptor area to the same receptor's air pollution burden increases from 38-74% in summer to 56-85% in winter. The contribution of local sources is higher for diesel BC (62-85%) than for benzene (38-71%), reflecting the difference in these pollutants' atmospheric lifetimes. Morning (6-9am) and afternoon (4-7 pm) commuting-related emissions dominate region-wide benzene levels in winter (14 and 25% of the total response, respectively). In contrast, afternoon rush hour emissions do not contribute significantly in summer. Similar morning and afternoon peaks in sensitivity to emissions are observed for the BC response; these peaks are shifted toward midday because most diesel truck traffic occurs during off-peak hours.

Optimization of multipollutant air quality management strategies: A case study for five cities in the United States

Developing regional air quality management strategies is a difficult task because formation of air pollutants is interdependent and air quality at different locations may have different responses to emissions from common sources. We developed an optimization-based model, OPtimal integrated Emission Reduction Alternatives (OPERA), which allows for identifications of least-cost control strategies for attaining multipollutant air quality targets at multiple locations simultaneously. To implement OPERA, first, sensitivities of air quality to precursor emission changes are quantified. Second, cost functions of emission reductions are estimated using a cost analysis tool that includes a pool of available control measures. The third step is to determine desired reductions in concentrations of air pollutants. The last step is to identify the optimal control strategies by minimizing costs of emission controls using the sensitivities of air pollutants to emission changes, cost functions, and constraints for feasible emission reduction ratios. A case study that investigates ozone and PM2.5 air quality in the summer of 2007 for five major cities in the eastern United States is presented in this paper. The results of the OPERA calculations show that reductions in regional NOx and VOC as well as local primary PM2.5 emissions were more cost-effective than SO2 controls for decreasing ozone and total PM2.5 concentrations in the summer of 2007. This was because reductions in SO2 emissions would only decrease PM2.5 concentrations, and reductions in primary PM2.5 emissions were more cost-effective than SO2 emission controls.

IMPLICATIONS

We developed an optimization-based model, OPtimal integrated Emission Reduction Alternatives (OPERA), which allows for identification of least-cost emission control strategies for attaining multipollutant air quality targets at multiple locations simultaneously. A major strength of OPERA is its flexibility, which allows for changes in air quality regulations, involving agencies, study regions, and so on, to be readily incorporated. Overall, it has been demonstrated that OPERA is useful in developing least-cost emission control strategies for achieving multipollutant air quality targets at multiple locations simultaneously and could be useful for policymakers developing integrated air quality management plans.

Changes in inorganic fine particulate matter sensitivities to precursors due to large-scale US emissions reductions

We examined the impact of large US emissions changes, similar to those estimated to have occurred between 2005 and 2012 (high and low emissions cases, respectively), on inorganic PM2.5 sensitivities to further NOx, SO2, and NH3 emissions reductions using the chemical transport model GEOS-Chem. Sensitivities to SO2 emissions are larger year-round and across the US in the low emissions case than the high emissions case due to more aqueous-phase SO2 oxidation. Sensitivities to winter NOx emissions are larger in the low emissions case, more than 2× those of the high emissions case in parts of the northern Midwest. Sensitivities to NH3 emissions are smaller (∼40%) in the low emissions case, year-round, and across the US. Differences in NOx and NH3 sensitivities indicate an altered atmospheric acidity. Larger sensitivities to SO2 and NOx in the low emissions case imply that reducing these emissions may improve air quality more now than they would have in 2005; conversely, NH3 reductions may not improve air quality as much as previously assumed.

Differences between magnitudes and health impacts of BC emissions across the United States using 12 km scale seasonal source apportionment

Recent assessments have analyzed the health impacts of PM2.5 from emissions from different locations and sectors using simplified or reduced-form air quality models. Here we present an alternative approach using the adjoint of the Community Multiscale Air Quality (CMAQ) model, which provides source-receptor relationships at highly resolved sectoral, spatial, and temporal scales. While damage resulting from anthropogenic emissions of BC is strongly correlated with population and premature death, we found little correlation between damage and emission magnitude, suggesting that controls on the largest emissions may not be the most efficient means of reducing damage resulting from anthropogenic BC emissions. Rather, the best proxy for locations with damaging BC emissions is locations where premature deaths occur. Onroad diesel and nonroad vehicle emissions are the largest contributors to premature deaths attributed to exposure to BC, while onroad gasoline emissions cause the highest deaths per amount emitted. Emissions in fall and winter contribute to more premature deaths (and more per amount emitted) than emissions in spring and summer. Overall, these results show the value of the high-resolution source attribution for determining the locations, seasons, and sectors for which BC emission controls have the most effective health benefits.

A method to quantitatively apportion pollutants at high spatial and temporal resolution: the Stochastic Lagrangian Apportionment Method (SLAM)

We introduce a method to quantify upwind contributions to concentrations of atmospheric pollutants. The Stochastic Lagrangian Apportionment Method (SLAM) carries out the following: (1) account for chemical transformations and depositional losses; (2) incorporate the effects of turbulent dispersion; (3) simulate the locations of the sources with high spatial and temporal resolution; and (4) minimize the impact from numerical diffusion. SLAM accomplishes these four features by using a time-reversed Lagrangian particle dispersion model and then simulating chemical changes forward in time, while tagging and keeping track of different sources. As an example of SLAM's application, we show its use in apportioning sources contributing to ammonia (NH3) and ammonium particulates (p-NH4(+)) at a site in southern Ontario, Canada. Agricultural emissions are seen to dominate contributions to NH3 and p-NH4(+) at the site. The source region of NH3 was significantly smaller than that of p-NH4(+), which covered numerous states of the American Midwest. The source apportionment results from SLAM were compared against those from zeroing-out individual sources ("brute force method"; BFM). The comparisons show SLAM to produce almost identical results as BFM for NH3, but higher concentrations of p-NH4(+), likely due to indirect effects that affect BFM. Finally, uncertainties in the SLAM approach and ways to address such shortcomings by combining SLAM with inverse methods are discussed.

Toward verifying fossil fuel CO2 emissions with the CMAQ model: motivation, model description and initial simulation

Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2 spatiotemporal variability and verify CO2 fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2 simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere-atmosphere exchange, and meteorology in regulating the spatial distribution of CO2 near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2 concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver CO, the CMAQ model using hourly varying, high-resolution CO2 fossil-fuel emissions from the Vulcan inventory and Carbon Tracker optimized NEE reproduced the observed diurnal profile of CO2 reasonably well but with a low bias in the early morning. The spatial distribution of CO2 was found to correlate with NO(x), SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2 observations and understand CO2 variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2 source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2 spatiotemporal variability and various uncertainties in the future.

IMPLICATIONS

Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant.

Attainment vs exposure: ozone metric responses to source-specific NOx controls using adjoint sensitivity analysis

We establish linkages between sources of NOx emissions and two types of national ozone metrics in Canada and the U.S. using the adjoint of an air quality model. We define an attainment-based metric using probabilistic design values (PDVs) exceeding 65 ppb to represent polluted regions and define an exposure-based metric as the premature mortality count related to short-term ozone exposure, both in Canada and the U.S. Our results reveal differences in both temporally averaged and day-specific influences of NOx emission controls across source locations. We find NOx emission reductions in California and the eastern U.S. to be most effective for reducing attainment- and exposure-based metrics, amounting to a total reduction of 6500 ppb in PDVs and 613 deaths/season nationally from a 10% reduction in NOx emissions from those source locations. While source controls in the remainder of the western U.S. are beneficial at reducing nonattainment, these reductions are less influential on ozone mortality. We also find that while exposure-based metrics are sensitive to daily emission reductions, much of the reduction in PDVs arises from controlling emissions on only a fraction of simulation days. We further illustrate the dependency of adjoint estimates of emission influences on the choice of averaging period as a follow-up to previous work.

Optimal ozone reduction policy design using adjoint-based NOx marginal damage information

Despite substantial reductions in nitrogen oxide (NOx) emissions in the United States, the success of emission control programs in optimal ozone reduction is disputable because they do not consider the spatial and temporal differences in health and environmental damages caused by NOx emissions. This shortcoming in the current U.S. NOx control policy is explored, and various methodologies for identifying optimal NOx emission control strategies are evaluated. The proposed approach combines an optimization platform with an adjoint (or backward) sensitivity analysis model and is able to examine the environmental performance of the current cap-and-trade policy and two damage-based emissions-differentiated policies. Using the proposed methodology, a 2007 case study of 218 U.S. electricity generation units participating in the NOx trading program is examined. The results indicate that inclusion of damage information can significantly enhance public health performance of an economic instrument. The net benefit under the policy that minimizes the social cost (i.e., health costs plus abatement costs) is six times larger than that of an exchange rate cap-and-trade policy.

Source attribution of health benefits from air pollution abatement in Canada and the United States: an adjoint sensitivity analysis

BACKGROUND

Decision making regarding air pollution can be better informed if air quality impacts are traced back to individual emission sources. Adjoint or backward sensitivity analysis is a modeling tool that can achieve this goal by allowing for quantification of how emissions from sources in different locations influence human health metrics.

OBJECTIVES

We attributed short-term mortality (valuated as an overall "health benefit") in Canada and the United States to anthropogenic nitrogen oxides (NO(x)) and volatile organic compound (VOC) emissions across North America.

METHODS

We integrated epidemiological data derived from Canadian and U.S. time-series studies with the adjoint of an air quality model and also estimated influences of anthropogenic emissions at each location on nationwide health benefits.

RESULTS

We found significant spatiotemporal variability in estimated health benefit influences of NO(x) and VOC emission reductions on Canada and U.S. mortality. The largest estimated influences on Canada (up to $250,000/day) were from emissions originating in the Quebec City-Windsor Corridor, where population centers are concentrated. Estimated influences on the United States tend to be widespread and more substantial owing to both larger emissions and larger populations. The health benefit influences calculated using 24-hr average ozone (O(3)) concentrations are lower in magnitude than estimates calculated using daily 1-hr maximum O(3) concentrations.

CONCLUSIONS

Source specificity of the adjoint approach provides valuable information for guiding air quality decision making. Adjoint results suggest that the health benefits of reducing NO(x) and VOC emissions are substantial and highly variable across North America.

Improving NO(x) cap-and-trade system with adjoint-based emission exchange rates

Cap-and-trade programs have proven to be effective instruments for achieving environmental goals while incurring minimum cost. The nature of the pollutant, however, affects the design of these programs. NO(x), an ozone precursor, is a nonuniformly mixed pollutant with a short atmospheric lifetime. NO(x) cap-and-trade programs in the U.S. are successful in reducing total NO(x) emissions but may result in suboptimal environmental performance because location-specific ozone formation potentials are neglected. In this paper, the current NO(x) cap-and-trade system is contrasted to a hypothetical NO(x) trading policy with sensitivity-based exchange rates. Location-specific exchange rates, calculated through adjoint sensitivity analysis, are combined with constrained optimization for prediction of NO(x) emissions trading behavior and post-trade ozone concentrations. The current and proposed policies are examined in a case study for 218 coal-fired power plants that participated in the NO(x) Budget Trading Program in 2007. We find that better environmental performance at negligibly higher system-wide abatement cost can be achieved through inclusion of emission exchange rates. Exposure-based exchange rates result in better environmental performance than those based on concentrations.

Development of risk-based air quality management strategies under impacts of climate change

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30-50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies.

IMPLICATIONS

Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.