Community Multiscale Air Quality Research Articles

Modeling polycyclic aromatic hydrocarbons (PAHs) concentrations from wildfires in California

In recent years, wildfires in California have increased in frequency and intensity due to climate change and prolonged drought. The air pollutants released by wildfires cause significant health consequences, among which polycyclic aromatic hydrocarbons (PAHs) are particularly toxic. Estimating PAH emissions from wildfires is challenging due to variability in vegetation types. In this study, we estimate PAH emission rates across California at a high resolution, based on laboratory-measured PAH emission rates from 22 different vegetation types and detailed vegetation mapping. By combining these estimates with biomass burning data from the NCAR Fire Inventory, the Community Multiscale Air Quality Modeling System simulates PAH concentrations for the 2017 fire season. The modeling results compare favorably to measurements from three PAH monitoring sites in California. The peak PAH emissions from wildfire events are up to be 80 times higher in the gas phase and 32 times higher in the particle phase compared to a case without fire emissions. The population-weighted PAH concentrations from the fire case (0.053 µg/m3) are 47 % higher compared to a non-fire case (0.036 µg/m3) in the particle phase and 11 % higher in the gas phase (9.82 ppt compared to 8.83 ppt) during the study period. While highly depended on the meteorological condition, the simulated spatial distribution indicates that gas-phase PAHs are less likely to travel long distances from the fire source and are prone to aging into the particle phase during transport. Consequently, populations are more likely to be exposed to particle-phase PAHs during wildfire events. This finding has important implications for understanding the health impacts of wildfire-induced PAH concentrations, as particle-phase PAHs may have different toxicological effects compared to gas-phase PAHs.

Assessment of Nocturnal NOx Heterogeneous Reaction Mechanisms in the Community Multiscale Air Quality (CMAQ) Model

AbstractNitrogen oxides (NOx) have adverse human health impacts and play a central role in the production of ozone and PM2.5. Nighttime heterogeneous chemistry regulates the nocturnal reservoirs and sinks of NOx such as N2O5 removal and ClNO2 production. However, existing parameterizations of nocturnal NOx heterogeneous chemistry in air quality models do not capture the variability in observations. Here, we implemented for the first time in the Community Multiscale Air Quality (CMAQ) model the Gaston N2O5 uptake (γ(N2O5)) mechanism that accounts for the role of particulate organic matter in regulating N2O5 uptake and the Staudt ClNO2 yield (Φ(ClNO2)) mechanism that includes the role of reactive solutes in suppressing ClNO2 production. With the Gaston and Staudt parameterizations, the coarse mode contributed modestly to N2O5 loss (17.2%) but significantly to ClNO2 production (60.3%), highlighting the impact of coarse mode chemistry. The Gaston γ(N2O5) parameterization in the fine mode increased agreement between modeled N2O5 concentration and observations (RMSEnew = 0.37ppb) compared to the model default (RMSEdefault = 0.43ppb). The Gaston γ(N2O5) parameterization was overall biased low due to underestimates in modeled particle oxygen to carbon ratio (O:C). The Staudt Φ(ClNO2) parameterization resulted in further underestimation (NMBnew = −73.7%) compared to the model default (NMBdefault = −37.9%) because of underestimation of fine mode particle chloride concentration. We expect that the updated parameterizations may more accurately capture the mean state and variability in γ(N2O5) and Φ(ClNO2) under conditions where model particulate O:C and chloride are better represented.

Concentrations of Volatile Methyl Siloxanes in New York City Reflect Emissions from Personal Care and Industrial Use.

Volatile methyl siloxanes (VMS) are a group of organosilicon compounds of interest because of their potential health effects, their ability to form secondary organic aerosols, and their use as tracer compounds. VMS are emitted in the gas-phase from using consumer and personal care products, including deodorants, lotions, and hair conditioners. Because of this emission route, airborne concentrations are expected to increase with population density, although there are few studies in large urban centers. Here, we report summertime concentrations and daily variations of VMS congeners measured in New York City. Median concentrations of the 6 studied congeners, D3 (20 ng m-3), D4 (57 ng m-3), D5 (230 ng m-3), D6 (11 ng m-3), L5 (2.5 ng m-3), and L7 (1.3 ng m-3) are among the highest reported outdoor concentrations in the literature to date. Average congener ratios of D5:D4 and D5:D6 were consistent with previously reported emissions ratios, suggesting that concentrations were dominated by local emissions. Measured concentrations agree with previously published results from a Community Multiscale Air Quality model and support commonly accepted emissions rates for D4, D5, and D6 of 32.8, 135, and 6.1 mg per capita per day. Concentrations of D4, D5, D6, L5, and L7 and total VMS were significantly lower during the day than during the night, consistent with daytime oxidation reactivity. Concentrations of D3 did not show the same diurnal trend but exhibited a strong directional dependence, suggesting that it may be emitted by industrial point sources in the area rather than personal care product use. Concentrations of all congeners had large temporal variations but showed relatively weak relationships with wind speed, temperature, and mixing height.

Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China

Ozone pollution presents a growing air quality threat in urban agglomerations in China. It remains challenge to distinguish the roles of emissions of precursors, chemical production and transportations in shaping the ground-level ozone trends, largely due to complicated interactions among these 3 major processes. This study elucidates the formation factors of ozone pollution and categorizes them into local emissions (anthropogenic and biogenic emissions), transport (precursor transport and direct transport from various regions), and meteorology. Particularly, we attribute meteorology, which affects biogenic emissions and chemical formation as well as transportation, to a perturbation term with fluctuating ranges. The Community Multiscale Air Quality (CMAQ) model was utilized to implement this framework, using the Pearl River Delta region as a case study, to simulate a severe ozone pollution episode in autumn 2019 that affected the entire country. Our findings demonstrate that the average impact of meteorological conditions changed consistently with the variation of ozone pollution levels, indicating that meteorological conditions can exert significant control over the degree of ozone pollution. As the maximum daily 8-hour average (MDA8) ozone concentrations increased from 20 % below to 30 % above the National Ambient Air Quality Standard II, contributions from emissions and precursor transport were enhanced. Concurrently, direct transport within Guangdong province rose from 13.8 % to 22.7 %, underscoring the importance of regional joint prevention and control measures under adverse weather conditions. Regarding biogenic emissions and precursor transport that cannot be directly controlled, we found that their contributions were generally greater in urban areas with high nitrogen oxides (NOx) levels, primarily due to the stronger atmospheric oxidation capacity facilitating ozone formation. Our results indicate that not only local anthropogenic emissions can be controlled in urban areas, but also the impacts of local biogenic emissions and precursor transport can be potentially regulated through reducing atmospheric oxidation capacity.

Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method

Abstract. Chemical transport models (CTMs) are widely used for air pollution modeling, which suffer from significant biases due to uncertainties in simplified parameterization, meteorological fields, and emission inventories. Accurate diagnosis of simulation biases is critical for the improvement of models, interpretation of results, and management of air quality, especially for the simulation of fine particulate matter (PM2.5). In this study, an efficient method with high speed and a low computational resource requirement based on the tree-based machine learning (ML) method, the light gradient boosting machine (LightGBM), was designed to diagnose CTM simulation biases. The drivers of the Community Multiscale Air Quality (CMAQ) model biases are compared to observations obtained by simulating PM2.5 concentrations from the perspectives of meteorology, chemical composition, and emission sources. The source-oriented CMAQ was used to diagnose the influences of different emission sources on PM2.5 biases. The model can capture the complex relationship between input variables and simulation bias well; meteorology, PM2.5 components, and source sectors can partially explain the simulation bias. The CMAQ model underestimates PM2.5 by −19.25 to −2.66 µg m−3 in 2019, especially in winter and spring and during high-PM2.5 events. Secondary organic components showed the largest contribution to the PM2.5 simulation bias for different regions and seasons (13.8 %–22.6 %) of all components. Relative humidity, cloud cover, and soil surface moisture were the main meteorological factors contributing to PM2.5 bias in the North China Plain, Pearl River Delta, and northwestern China, respectively. Primary and secondary inorganic components from residential sources showed the two largest contributions to this bias (12.05 % and 12.78 %), implying large uncertainties in this sector. The ML-based methods provide valuable complements to traditional-mechanism-based methods for model improvement, with high efficiency and low reliance on prior information.

Determination of major drive of ozone formation and improvement of O3 prediction in typical North China Plain based on interpretable random forest model

O3 pollution in China has become prominent in recent years, and it has become one of the most challenging issues in air pollution control. We used data on atmospheric pollutants and meteorology from 2019 to 2021 to build an interpretable random forest (RF) model, applying this model to predict O3 concentration in 2022 in five cities in the Southwest North China Plain. The model was also used to identify and explain the influence of various factors on O3 formation. The correlation coefficient R2 between the predicted O3 concentration and observed O3 concentration was 0.82, the MAE was 15.15 μg/m3, and the RMSE was 20.29 μg/m3, indicating that the model can effectively predict O3 concentration in the studying area. The results of correlation analysis, feature importance, and the driving factor analysis from SHapley Additive exPlanations (SHAP) model indicated that temperature (T), NO2, and relative humidity (RH) are the top three features affecting O3 prediction, while the weights of wind speed and wind direction were relatively low. Thus, O3 in the southwestern region of Henan may mainly come from the formation of local photochemical activities. The dominant factors behind O3 also varied in different seasons. In spring and autumn, O3 pollution is more likely to occur under high NO2 concentration and high-temperature conditions, while in summer, it is more likely to occur under high-temperature and precipitation-free weather. In winter, NO2 is the dominant factor in O3 formation. Finally, the interpretable RF model is used to predict future O3 concentration based on features provided by Community Multiscale Air Quality (CMAQ) and Weather Research & Forecast (WRF) model, and the simulation performance of CMAQ on O3 concentration is enhanced to a certain extent, improving the prediction of future O3 pollution situations and guiding pollution control.

Summertime tropospheric ozone source apportionment study in the Madrid region (Spain)

Abstract. The design of emission abatement measures to effectively reduce high ground-level ozone (O3) concentrations in urban areas is very complex. In addition to the strongly non-linear chemistry of this secondary pollutant, precursors can be released by a variety of sources in different regions, and locally produced O3 is mixed with that transported from the regional or continental scales. All of these processes depend also on the specific meteorological conditions and topography of the study area. Consequently, high-resolution comprehensive modeling tools are needed to understand the drivers of photochemical pollution and to assess the potential of local strategies to reduce adverse impacts from high tropospheric O3 levels. In this study, we apply the Integrated Source Apportionment Method (ISAM) implemented in the Community Multiscale Air Quality (CMAQ v5.3.2) model to investigate the origin of summertime O3 in the Madrid region (Spain). Consistent with previous studies, our results confirm that O3 levels are dominated by non-local contributions, representing around 70 % of mean values across the region. Nonetheless, precursors emitted by local sources, mainly road traffic, play a more important role during O3 peaks, with contributions as high as 25 ppb. The potential impact of local measures is higher under unfavorable meteorological conditions associated with regional accumulation patterns. These findings suggest that this modeling system may be used in the future to simulate the potential outcomes of specific emission abatement measures to prevent high-O3 episodes in the Madrid metropolitan area.

Prenatal exposure to air pollutant mixtures and birthweight in the upstate KIDS cohort

BackgroundSingle-pollutant models have linked prenatal PM2.5 exposure to lower birthweight. However, analyzing air pollutant mixtures better captures pollutant interactions and total effects. Unfortunately, strong correlations between pollutants restrict traditional methods. ObjectivesWe explored the association between exposure to a mixture of air pollutants during different gestational age windows of pregnancy and birthweight. MethodsWe included 4,635 mother-infant dyads from a New York State birth cohort born 2008–2010. Air pollution data were sourced from the EPA’s Community Multiscale Air Quality model and matched to the census tract centroid of each maternal home address. Birthweight and gestational age were extracted from vital records. We applied linear regression to study the association between prenatal exposure to PM2.5, PM10, NOX, SO2, and CO and birthweight during six sensitive windows. We then utilized Bayesian kernel machine regression to examine the non-linear effects and interactions within this five-pollutant mixture. Final models adjusted for maternal socio-demographics, infant characteristics, and seasonality. ResultsSingle-pollutant linear regression models indicated that most pollutants were associated with a decrement in birthweight, specifically during the two-week window before birth. An interquartile range increase in PM2.5 exposure (IQR: 3.3 µg/m3) from the median during this window correlated with a 34 g decrement in birthweight (95 % CI: −54, −14), followed by SO2 (IQR: 2.0 ppb; β: −31), PM10 (IQR: 4.6 µg/m3; β: −29), CO (IQR: 60.8 ppb; β: −27), and NOX (IQR: 7.9 ppb; β: −26). Multi-pollutant BKMR models revealed that PM2.5, NOX, and CO exposure were negatively and non-linearly linked with birthweight. As the five-pollutant mixture increased, birthweight decreased until the median level of exposure. DiscussionPrenatal exposure to air pollutants, notably PM2.5, during the final two weeks of pregnancy may negatively impact birthweight. The non-linear relationships between air pollution and birthweight highlight the importance of studying pollutant mixtures and their interactions.

Joint effect of long-term exposure to ambient air pollution on the prevalence of chronic obstructive pulmonary disease using the Korea National Health and Nutrition Examination Survey 2010–2019

BackgroundLittle is known about the relationship between long-term joint exposure to mixtures of air pollutants and the prevalence of chronic obstructive pulmonary disease (COPD). We aimed to assess the joint impact of long-term exposure to ambient air pollution on the prevalence of COPD in Korea, especially in areas with high levels of air pollution. MethodsWe included 22,387 participants who underwent spirometry tests in 2010–2019. The community multiscale air quality model was used to estimate the levels of ambient air pollution at residential addresses. The average exposure over the 5 years before the examination date was used to calculate the concentrations of air pollution. Forced expiratory volume in 1 s and forced vital capacity were used to define restrictive lung disease, COPD, and moderate-to-severe COPD. Quantile-based g-computation models were used to assess the joint impact of air pollution on COPD prevalence. ResultsA total of 2535 cases of restrictive lung disease, 2787 cases of COPD, and 1399 cases of moderate-to-severe COPD were identified. In the individual pollutant model, long-term exposure was significantly associated with both restrictive lung disease and COPD. In the mixture pollutant model, the odds ratios (ORs, 95% confidence intervals) for restrictive lung disease increased with each quartile increment in the 1- to 5-year average mixtures: 1.14 (1.02–1.28, 1 year), 1.25 (1.11–1.41, 2 years), 1.26 (1.11–1.42, 3 years), 1.32 (1.16–1.51, 4 years), and 1.37 (1.19–1.58, 5 years), respectively. The increase in ORs of restrictive lung disease accelerated over time. By contrast, the ORs of COPD showed a decreasing trend over time. ConclusionsLong-term exposure to air pollutants, both individually and jointly, was associated with an increased risk of developing COPD, particularly restrictive lung disease. Our findings highlight the importance of comprehensively assessing exposure to various air pollutants in relation to COPD.

Designing regional joint prevention and control schemes of PM2.5 based on source apportionment of chemical transport model: A case study of a heavy pollution episode

Fine particular matter (PM2.5) pollution still occurs frequently in China. Despite recognizing the importance of regional joint prevention and control (RJPC) for mitigating haze pollution, there is still a limited understanding of scheme design and effectiveness. In this study, with the Fenhe Plain in China serving as the case, the Integrated Source Apportionment Method (ISAM), incorporated into the Community Multiscale Air Quality Modeling System (CMAQ), was used to quantitively identify PM2.5 sources during a typical heavy pollution episode from Jan. 20th to 25th, 2021. Informed by source apportionment, we designed and assessed multiple RJPC scenarios for eliminating heavy pollution, yielding recommended RJPC schemes. Source apportionment results revealed a substantial increase in the contribution percentage of regions within a 350-km radius during the pollution episode compared to the non-polluted period. Sulfate was predominantly contributed from long-distance transport, whereas nitrate was chiefly contributed from closer regions. Ammonium exhibited higher local contributions than nitrate from the Fenhe Plain. Moreover, Taiyuan and Linfen were identified as PM2.5 exporters, whereas Sanmenxia, Jinzhong, and Luliang served as PM2.5 importers, and Yuncheng acted as a comprehensive city. The proposed specific RJPC schemes aimed at eliminating heavy pollution for the Fenhe Plain emphasized the importance of controlling emissions of particulate matter, ammonia, nitrogen oxides, and volatile organic compounds in areas within an approximate 350-km radius outside the Fenhe Plain, in addition to strengthening the local emission control. Nevertheless, only through the comprehensive and in-depth implementation of emission control measures across a larger area can PM2.5 compliance be achieved.

Modeling the Oxygen Isotope Anomaly (Δ17O) of Reactive Nitrogen in the Community Multiscale Air Quality Model: Insights into Nitrogen Oxide Chemistry in the Northeastern United States

Modeling the Oxygen Isotope Anomaly (Δ¹⁷O) of Reactive Nitrogen in the Community Multiscale Air Quality Model: Insights into Nitrogen Oxide Chemistry in the Northeastern United States

Long-term variations of air pollutants and public exposure in China during 2000–2020

Since 2000, China has faced severe air pollution challenges，prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 μg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 μg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 μg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 μg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000–2020. It serves as a foundational resource for future efforts in air pollution control and health research.

Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China

Abstract. Two-way coupled meteorology and air quality models, which account for aerosol–radiation–cloud interactions, have been employed to simulate meteorology and air quality more realistically. Although numerous related studies have been conducted, none have compared the performances of multiple two-way coupled models in simulating meteorology and air quality over eastern China. Thus, we systematically evaluated annual and seasonal meteorological and air quality variables simulated by three open-source, widely utilized two-way coupled models (Weather Research and Forecasting (WRF)–Community Multiscale Air Quality (WRF–CMAQ), WRF coupled with chemistry (WRF–Chem), and WRF coupled with a regional chemistry-transport model named CHIMERE (WRF–CHIMERE)) by validating their results with surface and satellite observations for eastern China in 2017. Although we have made every effort to evaluate these three coupled models by using configurations that are as consistent as possible, there are still unavoidable differences between them in their treatments of physical and chemical processes. Our thorough evaluations revealed that all three two-way coupled models captured the annual and seasonal spatiotemporal characteristics of meteorology and air quality reasonably well. Notably, the role of the aerosol–cloud interaction (ACI) in improving the models' performances was limited compared to that of the aerosol–radiation interaction (ARI). The sources of uncertainties and bias in the different ACI schemes in the two-way coupled models were identified. With sufficient computational resources, these models can provide more accurate air quality forecasting to support atmospheric environment management and deliver timely warnings of heavy air pollution events. Finally, we propose potential improvements to two-way coupled models for future research.

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

Abstract In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a one-dimensional (1D) convolutional neural network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorological, and land-use/land-cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an index of agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal threefold cross validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley additive explanations analysis. The results revealed solar radiation reaching the surface, planetary boundary layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times as fast in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well. Significance Statement This work develops an emulator of the Community Multiscale Air Quality model, using a one-dimensional convolutional neural network to predict hourly surface NO2 concentrations across densely populated regions in Texas. Our emulator is capable of providing rapid and highly accurate NO2 estimates, enabling it to model diverse scenarios and facilitating informed decision-making to improve public health outcomes. Notably, this model outperforms traditional methods in computational efficiency, making it a robust, time-efficient tool for air pollution mitigation efforts. The findings suggest that key variables like solar radiation, planetary boundary layer height, and NOx (NO + NO2) emissions significantly influence the model’s NO2 predictions. By adding appropriate training data, this work can be extended to other regions and other pollutants such as O3, PM2.5, and PM10, offering a powerful tool for pollution mitigation and public health improvement efforts worldwide.

Role of air stagnation in determining daily average PM2.5 concentrations in areas with significant impact of long-range transport

In areas where the regional transport of air pollutants exerts a significant impact, ascertaining whether short-term air stagnation affects PM2.5 concentrations is crucial for accurate air quality forecasting and effective management planning. However, this research area remains underexplored. In this study, we analyzed the relationship between stagnant atmospheric conditions and daily average PM2.5 concentrations in areas with a substantial long-range transport impact (LRT). Specifically, we focused on days with elevated PM2.5 concentrations (daily average ≥ 35 μg/m3) from January to March 2019 in South Korea. The analysis was performed using the Weather Research and Forecasting and the Community Multiscale Air Quality Modeling System. Stagnant conditions were quantified using ventilation index (VI), calculated as the product of planetary boundary layer height and 10-m wind speed. The correlation coefficient between daily average VI and PM2.5 (r = –0.23) was lower than that between VI and NO2 (r = –0.60). This can be attributed to the fact that LRT was 2.9-fold higher than local emission impact (LEI) during days of elevated PM2.5 concentrations. Notably, the correlation coefficient between LRT and VI (r = –0.03) was considerably lower than that of LEI (r = –0.70). Hence, predicting the daily average PM2.5 concentration solely based on VI proved challenging in an area characterized by substantial LRT. For future research, in areas where LRT plays a substantial role, accurate prediction of PM2.5 concentrations requires distinguishing the effects of LRT and LEI on PM2.5 concentrations.

Response of warm season secondary pollutants to emissions and meteorology in the North China Plain region during 2018–2022

Air quality in China has continued to improve since 2013, although severe pollution events still occur over the North China Plain (NCP). It is noticeable that contributions from secondary pollutants have increased, but understanding of their formation and variations with changing emissions and meteorological conditions remains limited. In this study, the warm season of May to September from 2018 to 2022 was selected to explore the response of secondary pollutants to meteorology and emissions using the Community Multi-scale Air Quality model (CMAQ). Fine particulate matter (PM2.5) concentrations over the NCP decreased significantly by 31%–37% from 2018 to 2022, while ozone (O3) and nitrogen dioxide (NO2) generally showed decreasing trends by 1% and 0.5% per year, respectively. Secondary inorganic aerosol (including sulfate, nitrate, and ammonium) and secondary organic aerosol (SOA) also decreased significantly, by 9% and 6% per year, respectively. The results showed that emissions contributed 96% to the decreases in PM2.5 concentrations, while O3 fluctuated due to meteorological changes. Although the decreases in sulfate and ammonium were mainly associated with emission reductions, that of nitrate was more sensitive to meteorological changes. Meteorological and emission changes were similarly important for the overall decrease in SOA, with anthropogenic SOA being more sensitive to emissions control, while biogenic SOA was more easily attributed to meteorological changes. This research emphasizes the importance of controlling anthropogenic emissions in relieving summer secondary pollution in the NCP region.

Associations between short-term exposure to air pollution and thyroid function in a representative sample of the Korean population

BackgroundDisruption of thyroid function can profoundly affect various organ systems. However, studies on the association between air pollution and thyroid function are relatively scarce and most studies have focused on the long-term effects of air pollution among pregnant women. ObjectivesThis study aimed to explore the associations between short-term exposure to air pollution and thyroid function in the general population. MethodsData from the Korea National Health and Nutrition Examination Survey (2013–2015) were analyzed (n = 5,626). Air pollution concentrations in residential addresses were estimated using Community Multiscale Air Quality models. The moving averages of air pollution over 7 days were set as exposure variables through exploratory analyses. Linear regression and quantile g-computation models were constructed to assess the effects of individual air pollutants and air pollution mixture, respectively. ResultsA 10-ppb increase in NO2 (18.8-μg/m3 increase) and CO (11.5-μg/m3 increase) was associated with 2.43% [95% confidence interval (CI): 0.42, 4.48] and 0.19% (95% CI: 0.01, 0.36) higher thyroid-stimulating hormone (TSH) levels, respectively. A 10-μg/m3 increase in PM2.5 and a 10-ppb increase in O3 (19.6-μg/m3 increment) were associated with 0.87% (95% CI: -1.47, -0.27) and 0.59% (95% CI: -1.18, -0.001) lower free thyroxine (fT4) levels, respectively. A simultaneous quartile increase in PM2.5, NO2, O3, and CO levels was associated with lower fT4 but not TSH levels. ConclusionsAs the subtle changes in thyroid function can affect various organ systems, the present results may have substantial public health implications despite the relatively modest effect sizes. Because this was a cross-sectional study, it is necessary to conduct further experimental or repeated-measures studies to consolidate the current results.

Emissions and Atmospheric Dry and Wet Deposition of Trace Metals from Natural and Anthropogenic Sources in Mainland China

Trace metals from natural and anthropogenic sources impact the atmospheric environment and enter the soil through dry and wet atmospheric deposition, ultimately affecting human health. In this study, we established an emission inventory of Pb, As, Cr, and Cd in East Asia (80° E–140° E, 15° N–50° N) for the year 2017, including dust and anthropogenic sources from both land and marine. We modified the Community Multiscale Air Quality (CMAQ) model to provide gridded data on concentrations, as well as dry and wet atmospheric deposition fluxes of metals, with a focus on mainland China. The emissions of Pb, As, Cr, and Cd in East Asia were 19,253, 3415, 3332, and 9379 tons, respectively, in 2017, with 55%, 69%, 25%, and 58% distributed in the fine mode. The spatial distribution of atmospheric concentrations and dry deposition of trace metals was similar to that of emissions, while the spatial distribution of precipitation-related wet deposition was further east and greater in the south than in the north. In mainland China, the average bulk-deposition fluxes of Pb, As, Cr, and Cd were 1036.5, 170.3, 465.9, and 185.0 μg·m−2·year−1, respectively. Our study provides gridded data on trace metals in mainland China, which can be used for assessing air quality, human exposure risks, and metal inputs to soils.

Assessing the role of cold front passage and synoptic patterns on air pollution in the Korean Peninsula

Various numerical experiments using WRF (Weather Research & Forecasting Model) and CMAQ (Community Multiscale Air Quality Modeling System) were performed to analyze the phenomenon of rapidly high concentration PM2.5 after the passage of a cold front in an area with limited local emissions. The episode period was from January 14 to 23, 2018, and analysis was conducted by dividing it into two stages according to the characteristics of changes in PM2.5 concentrations during the period. Through the analysis of observational data during the episode period, we confirmed meteorological impacts (decrease in temperature, increase in wind speed and relative humidity) and an increase in air pollution (PM10 and PM2.5) attributed to the passage of a cold front. Using CMAQ's IPR (Integrated Process Rate) analysis, the contribution of the horizontal advection process was observed in transporting PM2.5 to Gangneung at higher altitudes, and the PM2.5 concentrations at the surface increased because the vertical advection process was influenced by the terrain. Notably, in Stage 2 (64 μg·m−3), a higher contribution of the vertical advection process compared to Stage 1 (35 μg·m−3) was observed, which is attributed to the differences in synoptic patterns following the passage of the cold front. During Stage 2, following the cold front, atmospheric stability (dominance of high-pressure system) led to air subsidence and the presence of a temperature inversion layer, creating favorable meteorological conditions for the accumulation of air pollutants. This study offers the mechanisms of air pollution over the Korean Peninsula under non-stationary meteorological conditions, particularly in relation to the passage of the cold front (low-pressure system). Notably, the influence of a cold front can vary according to the synoptic patterns that develop following its passage.

Pregnancy exposure to PM2.5 from wildland fire smoke and preterm birth in California

BackgroundWildfires in the Western United States are a growing and significant source of air pollution that is eroding decades of progress in air pollution reduction. The effects on preterm birth during critical periods of pregnancy are unknown. MethodsWe assessed associations between prenatal exposure to wildland fire smoke and risk of preterm birth (gestational age < 37 weeks). We assigned smoke exposure to geocoded residence at birth for all live singleton births in California conceived 2007–2018, using weekly average concentrations of particulate matter ≤ 2.5 µm (PM2.5) attributable to wildland fires from United States Environmental Protection Agency’s Community Multiscale Air Quality Model. Logistic regression yielded odds ratio (OR) for preterm birth in relation to increases in average exposure across the whole pregnancy, each trimester, and each week of pregnancy. Models adjusted for season, age, education, race/ethnicity, medical insurance, and smoking of the birthing parent. ResultsFor the 5,155,026 births, higher wildland fire PM2.5 exposure averaged across pregnancy, or any trimester, was associated with higher odds of preterm birth. The OR for an increase of 1 µg/m3 of average wildland fire PM2.5 during pregnancy was 1.013 (95 % CI:1.008,1.017). Wildland fire PM2.5 during most weeks of pregnancy was associated with higher odds. Strongest estimates were observed in weeks in the second and third trimesters. A 10 µg/m3 increase in average wildland fire PM2·5 in gestational week 23 was associated with OR = 1.034; 95 % CI: 1.019, 1.049 for preterm birth. ConclusionsPreterm birth is sensitive to wildland fire PM2.5; therefore, we must reduce exposure during pregnancy.