Response of atmospheric particulate matter to changes in precursor emissions: a comparison of three air quality models

Sources and formation characteristics of particulate nitrate in the Pearl River Delta region of China: Insights from three-year online observations

Nitrate (NO3-) has been identified as a key component of particulate matter (PM2.5) in China. However, there is still a lack of understanding regarding its sources and how it forms, especially in the context of high-frequency and long-term data. In this study, NO3- levels were observed on an hourly basis over an almost three-year period at an urban site in the Pearl River Delta (PRD) region, China, from January 2019 to December 2021. The results reveal an average daily NO3- concentration ranging from 0.08 μg m-3 to 61.69 μg m-3, constituting 11.9 ± 12.5 % of PM2.5. This percentage rose to as high as 57 % during pollution episodes, highlighting NO3-'s significant role in pollution formation. The ammonia-rich environment was found to be the most important factor in promoting NO3- formation. Positive Matrix Factorization (PMF) analysis indicates that the primary sources of NO3- in the PRD region were vehicle emissions (43.8 ± 21.2 %) and coal combustion (39.1 ± 21.5 %), with shipping emissions, sea salt, soil dust and industrial emissions + biomass burning following in importance. Regarding source areas, the primary contributor of vehicle emissions was predominantly from the PRD region, whereas the coal combustion, aside from local contributions, also originates from the northern region. From a long-term perspective, NO3- pollution has remained relatively stable since the summer of 2020. Concurrently, coal combustion source has shown a localization trend. These insights derived from the extensive, high-frequency observation presented in this study serve as a valuable reference for devising strategies to control NO3- and PM2.5 in the PRD region and China.

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 (NO_x), ammonia (NH₃), and nitrous oxide (N₂O). The propensity of NO_x 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 NO_x 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.

Deep Learning for Prediction of the Air Quality Response to Emission Changes

Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM). By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods that achieve lower accuracy despite requiring 20+ CTM simulations (the benchmark statistical model). Our results demonstrate the utility of deep learning approaches for capturing the nonlinearity of atmospheric chemistry and physics and the prospects of the new method to support effective policymaking in other environment systems.

Source apportionment to support air quality planning: Strengths and weaknesses of existing approaches

Information on the origin of pollution constitutes an essential step of air quality management as it helps identifying measures to control air pollution. In this work, we review the most widely used source-apportionment methods for air quality management. Using theoretical and real-case datasets we study the differences among these methods and explain why they result in very different conclusions to support air quality planning. These differences are a consequence of the intrinsic assumptions that underpin the different methodologies and determine/limit their range of applicability. We show that ignoring their underlying assumptions is a risk for efficient/successful air quality management as these methods are sometimes used beyond their scope and range of applicability. The simplest approach based on increments (incremental approach) is often not suitable to support air quality planning. Contributions obtained through mass-transfer methods (receptor models or tagging approaches built in air quality models) are appropriate to support planning but only for specific pollutants. Impacts obtained via "brute-force" methods are the best suited but it is important to assess carefully their application range to make sure they reproduce correctly the prevailing chemical regimes.

Source apportionment of VOCs and their impact on air quality and health in the megacity of Seoul

The source apportionment of volatile organic compounds (VOCs) was examined using receptor models (positive matrix factorization and chemical mass balance) and a chemical transport model (CTM). The receptor model-based analysis was performed using the datasets collected from four different sites from the megacity of Seoul during the years 2013-2015. The contributions of VOC emission sources to ozone (O₃) and PM_2.5 concentrations and the subsequent health effects in the study area were also assessed during a photochemically active period (June 2015) using a three-dimensional CTM, Community Multi-scale Air Quality (CMAQ), and the Environmental Benefits Mapping and Analysis Program (BenMAP). The solvent use and the on-road mobile emission sources were found to exert dominant controls on the VOC levels observed in the target city. VOCs transported from regions outside of Seoul accounted for a significant proportion (up to approximately 35%) of ambient VOC levels during the study period. The solvent use accounted for 3.4% of the ambient O₃ concentrations during the day (daily mean of 2.6%) and made insignificant contributions to PM_2.5 (<1%) during the simulation period. Biogenic VOC made insignificant contributions to O₃ (<1%) and a small contribution to PM_2.5 during the day (5.6% with a daily mean of 2.4%). The number of premature deaths attributed indirectly (O₃ and PM_2.5 formations via the oxidation of VOCs) to solvent use is expected to be significant.

Major Source Contributions to Ambient PM2.5 and Exposures within the New South Wales Greater Metropolitan Region

The coupled Conformal Cubic Atmospheric Model (CCAM) and Chemical Transport Model (CTM) (CCAM-CTM) was undertaken with eleven emission scenarios segregated from the 2008 New South Wales Greater Metropolitan Region (NSW GMR) Air Emission Inventory to predict major source contributions to ambient PM2.5 and exposure in the NSW GMR. Model results illustrate that populated areas in the NSW GMR are characterised with annual average PM2.5 of 6–7 µg/m3, while natural sources including biogenic emissions, sea salt and wind-blown dust contribute 2–4 µg/m3 to it. Summer and winter regional average PM2.5 ranges from 5.2–6.1 µg/m3 and 3.7–7.7 µg/m3 across Sydney East, Sydney Northwest, Sydney Southwest, Illawarra and Newcastle regions. Secondary inorganic aerosols (particulate nitrate, sulphate and ammonium) and sodium account for up to 23% and 18% of total PM2.5 mass in both summer and winter. The increase in elemental carbon (EC) mass from summer to winter is found across all regions but particularly remarkable in the Sydney East region. Among human-made sources, “wood heaters” is the first or second major source contributing to total PM2.5 and EC mass across Sydney in winter. “On-road mobile vehicles” is the top contributor to EC mass across regions, and it also has significant contributions to total PM2.5 mass, particulate nitrate and sulphate mass in the Sydney East region. “Power stations” is identified to be the third major contributor to the summer total PM2.5 mass across regions, and the first or second contributor to sulphate and ammonium mass in both summer and winter. “Non-road diesel and marine” plays a relatively important role in EC mass across regions except Illawarra. “Industry” is identified to be the first or second major contributor to sulphate and ammonium mass, and the second or third major contributor to total PM2.5 mass across regions. By multiplying modelled predictions with Australian Bureau of Statistics 1-km resolution gridded population data, the natural and human-made sources are found to contribute 60% (3.55 µg/m3) and 40% (2.41 µg/m3) to the population-weighted annual average PM2.5 (5.96 µg/m3). Major source groups “wood heaters”, “industry”, “on-road motor vehicles”, “power stations” and “non-road diesel and marine” accounts for 31%, 26%, 19%, 17% and 6% of the total human-made sources contribution, respectively. The results in this study enhance the quantitative understanding of major source contributions to ambient PM2.5 and its major chemical components. A greater understanding of the contribution of the major sources to PM2.5 exposures is the basis for air quality management interventions aiming to deliver improved public health outcomes.

Development and application of observable response indicators for design of an effective ozone and fine particle pollution control strategy in China

Designing effective control policies requires efficient quantification of the nonlinear response of air pollution to emissions. However, neither the current observable indicators nor the current indicators based on response-surface modeling (RSM) can fulfill this requirement. Therefore, this study developed new observable RSM-based indicators and applied them to ambient fine particle (PM_2.5) and ozone (O₃) pollution control in China. The performance of these observable indicators in predicting O₃ and PM_2.5 chemistry was compared with that of the current RSM-based indicators. H₂O₂×HCHO/NO₂ and total ammonia ratio, which exhibited the best performance among indicators, were proposed as new observable O₃- and PM_2.5-chemistry indicators, respectively. Strong correlations between RSM-based and traditional observable indicators suggested that a combination of ambient concentrations of certain chemical species can serve as an indicator to approximately quantify the response of O₃ and PM_2.5 to changes in precursor emissions. The observable RSM-based indicator for O₃ (observable peak ratio) effectively captured the strong NO_x-saturated regime in January and the NO_x-limited regime in July, as well as the strong NO_x-saturated regime in northern and eastern China and their key regions, including the Yangtze River Delta and Pearl River Delta. The observable RSM-based indicator for PM_2.5 (observable flex ratio) also captured strong NH₃-poor condition in January and NH₃-rich condition in April and July, as well as NH₃-rich in northern and eastern China and the Sichuan Basin. Moreover, analysis of these newly developed observable response indicators suggested that the simultaneous control of NH₃ and NO_x emissions produces greater benefits in provinces with higher PM_2.5 exposure by up to 1.2 μg m^-3 PM_2.5 per 10 % NH₃ reduction compared with NO_x control only. Control of volatile organic compound (VOC) emissions by as much as 40 % of NO_x controls is necessary to obtain the cobenefits of reducing both O₃ and PM_2.5 exposure at the national level when controlling NO_x emissions. However, the VOC-to-NO_x ratio required to maintain benefits varies significantly from 0 to 1.2 in different provinces, suggesting that a more localized control strategy should be designed for each province.

Modeling an air pollution episode in northwestern United States: identifying the effect of nitrogen oxide and volatile organic compound emission changes on air pollutants formation using direct sensitivity analysis

Air quality impacts of volatile organic compound (VOC) and nitrogen oxide (NO(x)) emissions from major sources over the northwestern United States are simulated. The comprehensive nested modeling system comprises three models: Community Multiscale Air Quality (CMAQ), Weather Research and Forecasting (WRF), and Sparse Matrix Operator Kernel Emissions (SMOKE). In addition, the decoupled direct method in three dimensions (DDM-3D) is used to determine the sensitivities of pollutant concentrations to changes in precursor emissions during a severe smog episode in July of 2006. The average simulated 8-hr daily maximum O3 concentration is 48.9 ppb, with 1-hr O3 maxima up to 106 ppb (40 km southeast of Seattle). The average simulated PM2.5 (particulate matter with an aerodynamic diameter < 2.5 microm) concentration at the measurement sites is 9.06 microg m(-3), which is in good agreement with the observed concentration (8.06 microg m(-3)). In urban areas (i.e., Seattle, Vancouver, etc.), the model predicts that, on average, a reduction of NO(x) emissions is simulated to lead to an increase in average 8-hr daily maximum O3 concentrations, and will be most prominent in Seattle (where the greatest sensitivity is -O.2 ppb per % change of mobile sources). On the other hand, decreasing NO(x) emissions is simulated to decrease the 8-hr maximum O3 concentrations in remote and forested areas. Decreased NO(x) emissions are simulated to slightly increase PM2.5 in major urban areas. In urban areas, a decrease in VOC emissions will result in a decrease of 8-hr maximum O3 concentrations. The impact of decreased VOC emissions from biogenic, mobile, nonroad, and area sources on average 8-hr daily maximum O3 concentrations is up to 0.05 ppb decrease per % of emission change, each. Decreased emissions of VOCs decrease average PM2.5 concentrations in the entire modeling domain. In major cities, PM2.5 concentrations are more sensitive to emissions of VOCs from biogenic sources than other sources of VOCs. These results can be used to interpret the effectiveness of VOC or NO(x) controls over pollutant concentrations, especially for localities that may exceed National Ambient Air Quality Standards (NAAQS).

IMPLICATIONS

The effect of NO(x) and VOC controls on ozone and PM2.5 concentrations in the northwestern United States is examined using the decoupled direct method in three dimensions (DDM-3D) in a state-of-the-art three-dimensional chemical transport model (CMAQ). NO(x) controls are predicted to increase PM2.5 and ozone in major urban areas and decrease ozone in more remote and forested areas. VOC reductions are helpful in reducing ozone and PM2.5 concentrations in urban areas. Biogenic VOC sources have the largest impact on O3 and PM2.5 concentrations.

Effect of hydrophilic organic seed aerosols on secondary organic aerosol formation from ozonolysis of α-pinene

Gas-particle partitioning theory is widely used in atmospheric models to predict organic aerosol loadings. This theory predicts that secondary organic aerosol (SOA) yield of an oxidized volatile organic compound product will increase as the mass loading of preexisting organic aerosol increases. In a previous work, we showed that the presence of model hydrophobic primary organic aerosol (POA) had no detectable effect on the SOA yields from ozonolysis of α-pinene, suggesting that the condensing SOA compounds form a separate phase from the preexisting POA. However, a substantial faction of atmospheric aerosol is composed of polar, hydrophilic organic compounds. In this work, we investigate the effects of model hydrophilic organic aerosol (OA) species such as fulvic acid, adipic acid, and citric acid on the gas-particle partitioning of SOA from α-pinene ozonolysis. The results show that only citric acid seed significantly enhances the absorption of α-pinene SOA into the particle-phase. The other two seed particles have a negligible effect on the α-pinene SOA yields, suggesting that α-pinene SOA forms a well-mixed organic aerosol phase with citric acid and a separate phase with adipic acid and fulvic acid. This finding highlights the need to improve the thermodynamics treatment of organics in current aerosol models that simply lump all hydrophilic organic species into a single phase, thereby potentially introducing an erroneous sensitivity of SOA mass to emitted OA species.

Quantifying the sources of ozone, fine particulate matter, and regional haze in the Southeastern United States

A detailed sensitivity analysis was conducted to quantify the contributions of various emission sources to ozone (O3), fine particulate matter (PM2.5), and regional haze in the Southeastern United States. O3 and particulate matter (PM) levels were estimated using the Community Multiscale Air Quality (CMAQ) modeling system and light extinction values were calculated from modeled PM concentrations. First, the base case was established using the emission projections for the year 2009. Then, in each model run, SO2, primary carbon (PC), NH3, NO(x) or VOC emissions from a particular source category in a certain geographic area were reduced by 30% and the responses were determined by calculating the difference between the results of the reduced emission case and the base case. The sensitivity of summertime O3 to VOC emissions is small in the Southeast and ground-level NO(x) controls are generally more beneficial than elevated NO(x) controls (per unit mass of emissions reduced). SO2 emission reduction is the most beneficial control strategy in reducing summertime PM2.5 levels and improving visibility in the Southeast and electric generating utilities are the single largest source of SO2. Controlling PC emissions can be very effective locally, especially in winter. Reducing NH3 emissions is an effective strategy to reduce wintertime ammonium nitrate (NO3NH4) levels and improve visibility; NO(x) emissions reductions are not as effective. The results presented here will help the development of specific emission control strategies for future attainment of the National Ambient Air Quality Standards in the region.

Organic aerosol spatial/temporal patterns: perspectives of measurements and model

Ambient measurements from SEARCH and model results from CMAQ-MADRID are analyzed side by side for the southeastern United States to understand the strengths and weaknesses of an air quality model in reproducing key spatial and temporal patterns related to organic aerosol (OA), with inferences regarding secondary organic aerosol (SOA). The model predicts a larger difference in OA concentrations between an urban (JST) and a rural site (YRK) than indicated by measurements. Modeled OA concentrations at JST and YRK are more strongly correlated than measurements. On average, models may understate urban OA emissions, while overstating urban SOA production; measurements indicate that SOA production takes place on the regional scale. Modeled diurnal fluctuations for OA are stronger than measured, due partially to overestimations of the temperature dependence parameters (deltaH(vap)) for SOA in the model. Urban-rural differences in the composition of SOA, inferred from the variations of estimated deltaH(vap), are not properly captured by the model, which does not represent multiple generations of SOA or varied reaction pathways as a function of chemical regimes. Model results are hampered by day-of-the-week and diurnal allocation issues related to EC and OA emissions. Top quintile (20%) afternoon OA concentrations are observed in both warm and cold seasons at the urban site. The frequency of high OA in the cold season is overstated in the model. The model predicts the warm vs cold season frequency of elevated OA episodes better at YRK than at JST, suggesting that regional emissions, chemistry, and transport are better simulated than urban processes.