Multiscale Air Quality Modeling System Research Articles

Impacts of different vehicle emissions on ozone levels in Beijing: Insights into source contributions and formation processes

Beijing, with the highest number of motor vehicles in China, significantly contributes to O3 pollution through substantial NOx and VOC emissions in the on-road transportation sector. Understanding the unique impact of emissions from different vehicle types on O3 levels is crucial for developing targeted strategies for O3 pollution. This study applied the Community Multiscale Air Quality Modeling System (CMAQ) to comprehensively investigate the impacts of emissions from different vehicle types on O3 levels in various regions of Beijing and to provide valuable insights into source contributions and formation processes. The results revealed that various vehicle types exhibited different spatial-temporal emission patterns, with medium-heavy duty trucks (HDT) and mini-light passenger vehicles (LDPV) identified as the primary contributors to NOx (36.1 %) and VOC (57.6 %) emissions. Using the Integrated Source Apportionment Method (ISAM) coupled in CMAQ, we found the total vehicle emissions contributed to over 20 % of daily maximum 8–h average O3 (MDA8 O3) concentration, ranked as the second largest contributor after regional transport. Contributions to O3 formation from LDPV and medium-large passenger vehicles (MDPV) were 2.6–4.0 and 4.2–6.8 ppb and mainly concentrated in urban areas, while the contributions from mini-light duty trucks (LDT) and HDT were 3.5–4.8 and 3.7–6.2 ppb and mainly concentrated in suburban areas. Through scenario analysis that removed emissions from specific types of vehicles, we found removing LDPV emissions led to decreases in daytime O3 concentration by 0.3–3.8 ppb. In contrast, removing MDPV emissions led to notable O3 increases by 4.0–11.8 ppb at rush hours. Removing LDT and HDT emissions resulted in 0.6–8.0 ppb increases in nocturnal O3 concentrations while 0.8–2.0 ppb decreases during the afternoon. This research highlights the necessity of tailoring control strategies for different vehicle types to effectively reduce O3 levels in Beijing and provides useful information for decision-makers to formulate effective measures of vehicle management in the future.

Social cohesion as a modifier of joint air pollution exposure and incident dementia

Social cohesion can reduce stress, increase social interaction, and improve cognitive reserve. These social mechanisms may modify the effects of air pollution on dementia risk. This cohort study examines the potential moderating effect of social cohesion on associations between joint air pollution exposure and incident dementia leveraging data from 5112 community-dwelling adults ≥65 years of age enrolled in the National Health and Aging Trends Study (NHATS). Study participants were enrolled in 2011 and followed through 2018. We assigned 2010 residential census tract-level exposures to five air pollutants, particulate matter (PM) ≤ 10 μm in diameter, PM ≤ 2.5 μm in diameter, carbon monoxide, nitric oxide, and nitrogen dioxide, using the US Environmental Protection Agency's Community Multiscale Air Quality Modeling System. Dementia status was determined based on self- or proxy-reported dementia diagnosis or “probable dementia” according to NHATS cognitive screening tools. Participants' self-rated neighborhood social cohesion was evaluated based on three questions: neighbors knowing each other, being helpful, and being trustworthy. Social cohesion was dichotomized at the median into high vs low social cohesion. Associations between air pollutants and incident dementia were assessed using quantile g-computation Cox proportional hazard models and stratified by high vs low social cohesion, adjusting for age, sex, education, partner status, urbanicity, annual income, race and ethnicity, years lived at current residence, neighborhood disadvantage index, and tract segregation. High social cohesion (HR = 1.20, 95 % CI = 0.98, 1.47) and air pollution (HR = 1.08, 95 % CI = 0.92, 1.28) were not associated with incident dementia alone. However, when stratified, greater joint air pollution exposure increased dementia risk among participants at low (HR = 1.34, 95 % CI = 1.04, 1.72), but not high (HR = 1.00, 95 % CI = 0.93, 1.06) social cohesion. Air pollution was a risk factor for dementia only when reported social cohesion was low, suggesting that social interaction may play a protective role, mitigating dementia risk via air pollution exposure.

Different contributions of meteorological conditions and emission reductions to the ozone pollution during Shanghai’s COVID-19 lockdowns in winter and spring

Shanghai experienced the COVID-19 lockdowns (LCD) in the winter of 2020 and the spring of 2022, respectively. In this paper, the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) modeling system was used to simulate ozone (O3) in Shanghai, and the contributions of meteorological conditions and emission reductions to the increases of the ground-level O3 concentrations in Shanghai during the two LCD periods were analyzed. The relationships between O3 concentrations and meteorological factors were quantified by the multiple linear regression model (MLR). The results showed that the increases in the concentrations of daily maximum 8-h moving average O3 (MDA8 O3) were caused by both meteorological changes and emission reductions, but their relative contributions to O3 concentrations were different in winter and spring. Unfavorable meteorological conditions and the emission reductions during the 2020 winter LCD period increased MDA8 O3 by 2.2 μg/m3 (2.2%) and 2.8 μg/m3 (2.8%), respectively, relative to the same period in the previous year. On the other hand, the meteorological conditions during the 2022 spring LCD period increased MDA8 O3 by 7.9 μg/m3 (6.6%), while the emission reductions only increased MDA8 O3 by 1.1 μg/m3 (0.9%). The O3 formation in Shanghai urban areas was under the VOC-limited conditions during two lockdowns in winter and spring. Therefore, the VOCs emission reductions were more effective for the control of O3 in Shanghai urban areas. The results emphasized the complex relationship between emission reductions, meteorological conditions and short-term O3 changes.

Mortality attributable to PM 2.5 from wildland fires in California from 2008 to 2018

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM 2.5 ), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM 2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM 2.5 estimates. We used a concentration-response function for PM 2.5 , applying ZIP code–level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM 2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Observation-constrained kinetic modeling of isoprene SOA formation in the atmosphere

Abstract. Isoprene has the largest global non-methane hydrocarbon emission, and the oxidation of isoprene plays a crucial role in the formation of secondary organic aerosol (SOA). Two primary processes are known to contribute to SOA formation from isoprene oxidation: (1) the reactive uptake of isoprene-derived epoxides on acidic or aqueous particle surfaces and (2) the absorptive gas–particle partitioning of low-volatility oxidation products. In this study, we developed a new multiphase condensed isoprene oxidation mechanism that includes these processes with key molecular intermediates and products. The new mechanism was applied to simulate isoprene gas-phase oxidation products and SOA formation from previously published chamber experiments under a variety of conditions and atmospheric observations during the Southern Oxidant and Aerosol Studies (SOAS) field campaign. Our results show that SOA formation from most of the chamber experiments is reasonably reproduced using our mechanism, except when the concentration ratios of initial nitric oxide to isoprene exceed ∼ 2, the formed SOA is significantly underpredicted. The SOAS simulations also reasonably agree with the measurements regarding the diurnal pattern and concentrations of different product categories, while the total isoprene SOA remains underestimated. The molecular compositions of the modeled SOA indicate that multifunctional low-volatility products contribute to isoprene SOA more significantly than previously thought, with a median mass contribution of ∼ 57 % to the total modeled isoprene SOA. However, this contribution is intricately intertwined with IEPOX-derived SOA (IEPOX: isoprene-derived epoxydiols), posing challenges for their differentiation using bulk aerosol composition analysis (e.g., the aerosol mass spectrometer with positive matrix factorization). Furthermore, the SOA from these pathways may vary greatly, mainly dependent on the volatility estimation and treatment of particle-phase processes (i.e., photolysis and hydrolysis). Our findings emphasize that the various pathways to produce these low-volatility species should be considered in models to more accurately predict isoprene SOA formation. The new condensed isoprene chemical mechanism can be further incorporated into regional-scale air quality models, such as the Community Multiscale Air Quality Modelling System (CMAQ), to assess isoprene SOA formation on a larger scale.

Impact of aerosol actinic radiative effect on ozone during haze pollution in the Pearl River Delta region

Large amounts of aerosol in the atmosphere can cause significant attenuation of photochemical radiation and have a substantial impact on ozone and other secondary pollutants involved in photochemical reactions. This study examines the impact of aerosol actinic radiative effect on ozone and secondary pollutants during typical haze processes in the Pearl River Delta (PRD) region. The investigation employed lidar data and Weather Research Forecast–Community Multiscale Air Quality (WRF-CMAQ) modeling system. The results revealed that the aerosol caused a significant decrease in ground-level photolysis rates of NO2 and ozone by 22% and 29%, respectively, when haze pollution was severe. The actinic radiative effect of aerosol caused an average reduction of more than 10 ppb in ground level ozone concentration in the PRD, with the core area experiencing a maximum reduction of 30 ppb. Additionally, the core area witnessed a maximum reduction of 2 ppb in peroxyacetyl nitrate (PAN) and 2 μg/m3 in PM2.5. Due to reduced photolysis rates, nitrogen oxides increased by more than 5 ppb on average, reaching up to 10 ppb, while volatile organic compounds (VOCs) increased by a maximum of 10 ppb in the core region. Temporally, the aerosol actinic radiative effect was most pronounced in the morning and less significant in the afternoon.

Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil

Abstract. Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (https://doi.org/10.57760/sciencedb.09858, Hoinaski et al., 2023a; https://doi.org/10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (https://doi.org/10.57760/sciencedb.09857, Hoinaski and Will, 2023a; https://doi.org/10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (https://doi.org/10.57760/sciencedb.09859, Hoinaski and Will, 2023b; https://doi.org/10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​

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.

A hybrid model for enhanced forecasting of PM2.5 spatiotemporal concentrations with high resolution and accuracy

Forecasting concentrations of PM2.5 is important due to its known impacts on public health and environment. However, PM2.5 concentrations can vary significantly over short distances and time, which can be influenced by local emissions and short-term weather patterns. This spatiotemporal variability makes accurate PM2.5 forecasting an inherently complex and challenging task. This study presented novel methodologies for short-term PM2.5 concentration forecast by combining the atmospheric chemistry transport model Community Multiscale Air Quality Modeling System (CMAQ) with data-driven machine learning methods, namely long short-term memory (LSTM) and random forest (RF) models. The combined model system forecast PM2.5 with 1 h, 1km × 1 km spatiotemporal resolution. The LSTM system forecast time-dependent PM2.5 concentrations at observation sites with a maximum root mean square error (RMSE) of 3.66 μg/m3 for 1-hr forecast and 23.75 μg/m3 for 72-hr forecast, leveraging results obtained from the atmospheric transport model with RMSE of 45.81 μg/m3. Wavelet transform in the LSTM system allowed learning and prediction of PM2.5 concentrations at different frequencies, capturing temporal variability of PM2.5 at various time scales. The RF model predicted distributions of PM2.5 concentrations by learning LSTM results and integrating crucial features such as CMAQ results, meteorological and topographical information. The feature significance of CMAQ results was the highest among the input features in RF models. Overall, the hybrid model could help with managing and mitigating the adverse effects of air pollution by enabling informed decision-making at the individual, community and policy levels.

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.

Atmospheric nitrate, including nitric acid (HNO3), particulate nitrate (pNO3), and organic nitrate (RONO2), is a key atmosphere component with implications for air quality, nutrient deposition, and climate. However, accurately representing atmospheric nitrate concentrations within atmospheric chemistry models is a persistent challenge. A contributing factor to this challenge is the intricate chemical transformations involving HNO3 formation, which can be difficult for models to replicate. Here, we present a novel model framework that utilizes the oxygen stable isotope anomaly (Δ17O) to quantitatively depict ozone (O3) involvement in precursor nitrogen oxides photochemical cycling and HNO3 formation. This framework has been integrated into the US EPA Community Multiscale Air Quality (CMAQ) modeling system to facilitate a comprehensive assessment of NO x oxidation and HNO3 formation. In application across the northeastern US, the model Δ17O compares well with recently conducted diurnal Δ17O(NO2) and spatiotemporal Δ17O(HNO3) observations, with a root mean square error between model and observations of 2.6 ‰ for Δ17O(HNO3). The model indicates the major formation pathways of annual HNO3 production within the northeastern US are NO+OH (46 %), N2O5 hydrolysis (34 %), and organic nitrate hydrolysis (12 %). This model can evaluate NO x chemistry in CMAQ in future air quality and deposition studies involving reactive nitrogen.

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 concentration (r = −0.23) was lower than that between VI and NO2 concentration (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.

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.

Risk of dementia due to Co-exposure to air pollution and neighborhood disadvantage

BackgroundCo-exposure to air pollution and neighborhood disadvantage may influence cognition decline. We tested these associations in the context of dementia risk. MethodsWe leveraged a cohort of adults ≥65 years (n = 5397) enrolled from 2011 to 2018 in the National Health and Aging Trends Study (NHATS). Particulate matter (PM) ≤ 10 μm in diameter, PM ≤ 2.5 μm in diameter, carbon monoxide, nitric oxide, and nitrogen dioxide - and neighborhood disadvantage were tested for joint associations with dementia risk. Pollutant concentrations at the 2010 census tract level were assigned using the US Environmental Protection Agency's Community Multiscale Air Quality Modeling System. Neighborhood disadvantage was defined using the tract Social Deprivation Index (SDI). Dementia was determined through self- or proxy-report or scores indicative of “probable dementia” according to NHATS screening tools. Joint effects of air pollutants and SDI were tested using quantile g-computation Cox proportional hazards models. We also stratified joint air pollution effects across SDI tertiles. Analyses adjusted for age at enrollment, sex, education, partner status, urbanicity, income, race and ethnicity, years at residence, census segregation, and census region. ResultsSDI score (aHR = 1.08; 95% CI 0.96, 1.22), joint air pollution (aHR = 1.03, 95% CI 0.92, 1.16) and joint SDI with air pollution (aHR = 1.04, 95% CI 0.89, 1.22) were not associated with dementia risk. After accounting for competing risk of death, joint SDI with air pollution was not associated with dementia risk (aHR = 1.06; 95% CI 0.87, 1.29). In stratified models, joint air pollution was associated with greater risk of dementia at high (aHR = 1.19; 95% CI 0.87, 1.63), but not at medium or low SDI. ConclusionAir pollution was associated with greater dementia risk in disadvantaged areas after accounting for competing risks. Air pollution associations with dementia incidence may be attenuated when other risk factors are more prominent in disadvantaged neighborhoods.

Climate change-induced impacts on PM2.5 in Taiwan under 2 and 4 °C global warming

Climate change can perturb meteorological parameters and affect air quality, human health, and the economy. This study examined the climate change-induced meteorological impacts on PM2.5 in Taiwan, an island with a dense population and complex topography. We conducted high-resolution simulations of the current and future climate using the National Center for Atmospheric Research's Weather Research and Forecasting model and the Community Multiscale Air Quality Modeling System. As verified by observations, the model could reproduce the spatial and seasonal characteristics of PM2.5 in Taiwan, including higher concentrations in cold seasons and lower concentrations in warm seasons. The number of polluted days in the current climate was more than 30 days per season in central Taiwan and more than 70 days in southern Taiwan in autumn and winter. Under the 2 °C and 4 °C global warming scenarios, climate change-induced impacts worsened the PM2.5 air quality in Taiwan in cold seasons. In winter, the number of polluted days increased by 3–6 days in the whole of Taiwan, while it decreased by 3–5 days in southern Taiwan in autumn. Analysis of climate change-induced impacts on northeasterly days, air stagnation, and ventilation indices revealed that the weaker low-level wind in cold seasons was the dominant factor of the worsened PM2.5 air quality under global warming in Taiwan. In addition, the climate change-induced worsening of PM2.5 air quality could be locally mitigated by northeasterly days and nighttime warming in urban areas in northern Taiwan and by precipitation in southern Taiwan. Overall, these findings indicated that climate change-induced impacts on air quality cover both urban and global scales. To better adapt to climate change, discussing interactions between the responses to global warming in urban areas using hierarchical models and high-resolution simulations is necessary.

Identifying sensitive precursors of ozone formation using photochemical indicators and transition values comprised by continuous automatic monitoring data

The O3 photochemical indicator is a tool used to identify the sensitive precursors of O3 formation. Establishing a reliable photochemical indicator and its transition value composed of continuous automatic monitoring of chemical components can not only analyze sensitive precursors of O3 formation in real-time but also analyze the time and space distribution characteristics of sensitive precursors of O3 formation at a relatively lower cost. Among the photochemical indicators that are often mentioned at present, the ratio of ozone to nitrogen oxides (O3/NOx, O3/NOZ, and O3/NOY) should have the greatest potential for continuous monitoring. This study used the Community Multiscale Air Quality Modeling System to evaluate whether these three indicators are applicable to environmental conditions in Taiwan. Research results show that the indicator O3/NOZ was less suitable for Taiwan, while the indicator O3/NOY (transition value = 5.3) and the indicator O3/NOx (transition value = 29.3) could more accurately evaluate the ozone sensitivity of Taiwan's overall grid, and had no obvious bias in judging the ozone sensitivity of rural and urban grids. This study also found that it is not appropriate to directly use the photochemical indicator transition values from the literature. Usually, because the value is too high or too low, there is an obvious bias in judging the ozone sensitivity of rural and urban grids. This result also demonstrates the need to consider local meteorological and emission conditions when estimating transition values of photochemical indicators. In addition, when using the photochemical indicator O3/NOY, attention should be paid to the calculation of NOY species composition. The more complete the NOY composition, the better its ability to correctly judge ozone sensitivity.

Association between short-term exposure to ambient air pollutants and biomarkers indicative of inflammation and oxidative stress: a cross-sectional study using KoGES-HEXA data.

Air pollution-induced systemic inflammation and oxidative stress are hypothesized to be the major biological mechanisms underlying pathological outcomes. We examined the association between short-term exposure to ambient air pollutants and biomarkers of inflammation and oxidative stress in 2199 general middle-aged Korean population residing in metropolitan areas. Serum levels of inflammatory cytokines (interleukin [IL]-1β, IL-6, IL-8, IL-10, and tumor necrosis factor [TNF]-α) and urinary levels of 8-hydroxy-2'-deoxyguanosine (8-OHdG) were measured. Daily concentrations of a series of air pollutants (particulate matter [PM]10, PM2.5, SO2, NO2, CO, and O3) were predicted using the Community Multiscale Air Quality modeling system, and participant-level pollutant exposure was determined using geocoded residential addresses. Short-term exposure was defined as the 1- to 7-day moving averages. The multivariable-adjusted linear models controlling for the sociodemographic, lifestyle, temporal, and meteorological factors identified positive associations of PM with IL-1β, IL-8, IL-10, TNF-α, and 8-OHdG levels; SO2 with IL-10 levels, CO with IL-1β, IL-10, and TNF-α levels; and O3 with IL-1β, IL-8, and 8-OHdG levels. O3 levels were inversely associated with IL-10 levels. For each pollutant, the strongest associations were observed for the 7-day average PM and CO with IL-1β (per 10-µg/m3 increase in PM10: 2.7%, 95% confidence interval [CI] = 0.6-4.8; per 10-µg/m3 increase in PM2.5: 6.4%, 95% CI = 2.4-10.5; per 0.1-ppm increase in CO: 3.3%, 95% CI = 0.3-6.5); the 2-day average SO2 with IL-10 levels (per 1-ppb increase in SO2: 1.1%, 95% CI = 0.1-2.1); and the 7-day average O3 with IL-8 levels (per 1-ppb increase in O3: 1.3%, 95% CI = 0.7-1.9). Short-term exposure to ambient air pollutants may induce oxidative damage and pro-inflammatory roles, together with counter-regulatory anti-inflammatory response.

Role of an Ultra-Large Coal-Fired Power Plant in PM2.5 Pollution in Taiwan

Taichung Power Plant (TPP) is Taiwan’s largest coal-fired power plant and is considered a major source of air pollution. During periods of deteriorating air quality, it is often required to reduce the load to reduce emissions. However, frequent power load shedding not only requires cost but also requires safety considerations. Therefore, it is necessary to explore the role that thermal power plant emissions play in air pollution in Taiwan. This study employed the Community Multiscale Air Quality modeling system with the brute-force method to analyze the PM2.5 concentration contributed by TPP. The results showed that among the various air basins in Taiwan, the Yun-Chi-Nan air basin (YCNAB), located to the south of TPP, was most severely affected by TPP’s emissions, with an annual average affected concentration of 1.0 µg m−3 (3.3%). However, when serious PM2.5 pollution events (daily concentration > 70 µg/m3) occurred due to low wind speeds, the Central Taiwan air basin (CTAB), where TPP is located, became the area most severely affected by TPP’s emissions. The low wind speed was caused by the interaction between the easterly wind field around Taiwan and Taiwan’s north–south mountain ranges. When this happens, TPP’s emissions would have a greater impact on the PM2.5 concentration at nearby stations in the CTAB and YCNAB, up to about 11%. Overall, on pollution days caused by low wind speeds, the largest TPP load reduction (40%) still had a certain effect as an emergency measure to improve the high PM2.5 pollution in central and southern Taiwan.

On the sources of ambient SOA in PM2.5: An integrated analysis over Jinan city of China

Secondary organic aerosol (SOA) accounts for a significant fraction of fine particulate matter (PM2.5) and is a crucial component that affects climate and public health. However, the formation of SOA in urban areas remains unclear and sources contributed to SOA are poorly characterized. In this study, we combined ambient measurements and the Weather Research and Forecasting (WRF)-Community Multi-scale Air Quality (CMAQ) modeling system to investigate the evolution of SOA during winter over Jinan city in China. Relative contributions of different anthropogenic sources to ambient SOA were subsequently probed by the Integrated Source Apportionment Method (ISAM) to explore the impact of anthropogenic emissions. The mean concentration of SOA was 11.2 μg m−3, accounting for 29.4% in PM2.5, and SOA concentrations on clean days (4.9 μg m−3) increased by 4.8 times on polluted days (23.3 μg m−3), and strong correlation between the elevated PM2.5 episodes and increased SOA concentrations was found (R2 > 0.5). In addition, SOA concentrations and the proportion of SOA in PM2.5 were higher during the daytime (24.9 μg m−3, 32.4%) compared to nighttime (21.9 μg m−3, 25.1%) on polluted days, which is primarily attributed to the intense atmospheric photochemical reactions and anthropogenic activities. Mobile sources act as the dominant contributor to SOA (57.6%) and road diesel emissions (30.9%) made the largest contribution to SOA, followed by non-road mobile sources (17.4%) among subsectors in mobile sources. This study sheds new insights into ambient SOA formation and SOA source apportionment for typical megacities in China, guiding the design of source-oriented regulation efforts on SOA mitigation.

Influence of meteorological reanalysis field on air quality modeling in the Yangtze River Delta, China

The meteorological reanalysis field is one of the key inputs to drive mesoscale meteorology simulations. To investigate the impacts of different reanalysis data on predictions of the meteorology parameters, as well as predictions of air quality, this study utilized the Weather Research and Forecasting/Community Multiscale Air Quality modeling system (WRF/CMAQ) with two sets of meteorological reanalysis inputs, the NCEP Final Operational Global Analysis (FNL) and the ECMWF Reanalysis v5.0 (ERA5) to predict the concentrations of ozone (O3) and fine particulate matter (PM2.5) in the Yangtze River Delta (YRD) in 2018. The results showed that ERA5 outperformed FNL in simulating 2-m temperature and relative humidity, while FNL demonstrated better accuracy in predicting wind fields. Both FNL and ERA5 underestimated the PM2.5 in spring and summer while overestimating it in autumn and winter. Furthermore, the Normalized mean bias (NMB) value for ERA5 exceeded the standard in winter (0.34), and the Normalized mean error (NME) value exceeded the standard in autumn (0.54). Both FNL and ERA5 underestimated the PM2.5 concentration during the pollution episodes, and FNL's simulations of PM2.5 concentrations were closer to observations than ERA5's as pollution levels intensified. Regarding O3 concentrations, ERA5 exhibited better performance than FNL, with the NMB (0.30, 0.27) and NME (0.39, 0.37) of FNL and ERA5 both exceeding the standard only in wintertime.