Community Multiscale Air Quality Model Research Articles

Evolution of Reactive Organic Compounds and Their Potential Health Risk in Wildfire Smoke.

Wildfires are an increasing source of emissions into the air, with health effects modulated by the abundance and toxicity of individual species. In this work, we estimate reactive organic compounds (ROC) in western U.S. wildland forest fire smoke using a combination of observations from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign and predictions from the Community Multiscale Air Quality (CMAQ) model. Standard emission inventory methods capture 40-45% of the estimated ROC mass emitted, with estimates of primary organic aerosol particularly low (5-8×). Downwind, gas-phase species abundances in molar units reflect the production of fragmentation products such as formaldehyde and methanol. Mass-based units emphasize larger compounds, which tend to be unidentified at an individual species level, are less volatile, and are typically not measured in the gas phase. Fire emissions are estimated to total 1250 ± 60 g·C of ROC per kg·C of CO, implying as much carbon is emitted as ROC as is emitted as CO. Particulate ROC has the potential to dominate the cancer and noncancer risk of long-term exposure to inhaled smoke, and better constraining these estimates will require information on the toxicity of particulate ROC from forest fires.

An iteratively optimized downscaling method for city-scale air quality forecast emission inventory establishment

Air quality models (AQMs) are pivotal in forecasting air quality and shaping pollution control strategies. Nonetheless, the effectiveness of AQMs is often compromised in many cities due to the absence of accurate local emission inventories. To address this gap, this study presents a novel AQM-ready emission inventory generation technique with iterative optimization ability for city-scale applications in China. An efficient emission processing tool was introduced in this study, which utilizes the High-Resolution Multi-resolution Emission Inventory for China (HR-MEIC) as input. Using environmental observations and a region map, the tool can justify emissions of different regions iteratively. With the iterative optimization method, the model performance can be notably improved even without local emissions. The optimization was realized by splitting model-ready emissions into different regions and adjusting the emissions using scale factors calculated with the modeling results and the observations of each region. This methodology was applied to the Eight Cities in the Chengdu Plain (CP8C), located in the western margin of Sichuan Basin with complex topography and meteorological conditions, southwestern China, monthly throughout 2023. Air quality modeling was carried out using Weather Forecast and Research Model (WRF) and the Community Multiscale Air Quality Model (CMAQ). The results showed that the optimization acquired a good performance after five cycles for PM2.5 and NO2, with correlation coefficients (R values) surging from 0.62 and 0.37 to 0.77 and 0.73, respectively, while their normalized mean bias (NMB) substantially decreased from 22.8 % and 100.4 % to 3.6 % and 3.3 %. The underestimation on O3 concentration was also improved by the optimization, although enhancements in O3 modeling remained modest. This technique provides an easy-to-copy method to generate reasonable AQM-ready emission files with open emission data and observation data, which would be beneficial for the cities' air quality forecast in cities without local emission inventories.

Implications of 1.5 K climate warming on warm-season ozone exposure and atmospheric oxidation capacity in China

Surface ozone (O3) poses significant threats to public health, agricultural crops, and plants in natural ecosystems. Global warming is likely to increase future O3 mainly by altering atmospheric photochemical reactions and enhancing biogenic volatile organic compound (BVOC) emissions. To assess the impacts of the future 1.5 K climate target on O3 concentrations and ecological O3 exposure in China, numerical simulations were conducted using the CMAQ (Community Multiscale Air Quality) model during April–October 2018. Ecological O3 exposure was estimated using six indices (i.e., M7, M24, N100, SUM60, W126, and AOT40f). The results show that the temperature rise increases the MDA8 O3 (maximum daily eight-hour average O3) concentrations by ∼3 ppb and the number of O3 exceedance days by 10–20 days in the North China Plain (NCP), Yangtze River Delta (YRD), and Sichuan Basin (SCB) regions. All O3 exposure indices show substantial increases. M24 and M7 in eastern and southern China will rise by 1–3 ppb and 2–4 ppb, respectively. N100 increases by more than 120 h in the surrounding regions of Beijing. SUM60 increases by greater than 9 ppm h−1, W126 increases by greater than 15 ppm h−1 in Shaanxi and SCB, and AOT40f increases by 6 ppm h−1 in NCP and SCB. The temperature increase also promotes atmospheric oxidation capacity (AOC) levels, with the higher AOC contributed by OH radicals in southern China but by NO3 radicals in northern China. The change in the reaction rate caused by the temperature increase has a greater influence on O3 exposure and AOC than the change in BVOC emissions.摘要地表臭氧(O₃)对公众健康, 农作物以及自然生态系统构成重大威胁. 全球变暖会增强大气光化学反应以及增加生物源挥发性有机化合物(BVOC)排放, 从而导致 O₃浓度增加. 为了评估未来 1.5 K 气候目标对中国 O₃浓度以及生态 O₃暴露的影响, 在 2018 年 4 月至 10 月期间使用 CMAQ模型进行了数值模拟. 使用六个指标(即 M7, M24, N100, SUM60, W126 和 AOT40f)估算生态 O₃暴露. 结果表明, 在华北平原,长江三角洲和四川盆地地区, 温度升高使每日最大8 小时平均 O₃浓度增加约 3 ppb, O₃超标天数增加 10–20 天.所有 O₃暴露指标均显著增加. 中国东部和南部的 M24 和 M7 将分别增加 1–3 ppb 和 2–4 ppb. 北京周边地区的 N100 增加超过 120 小时. 陕西和四川盆地的 SUM60 增加超过 9 ppm h⁻¹, W126 增加超过 15 ppm h⁻¹, 华北平原和四川盆地的 AOT40f 增加 6 ppm h⁻¹.温度升高还提升了大气氧化能力(AOC)水平, 在中国南部较高的 AOC 由羟基自由基贡献, 而在中国北部则由硝基自由基贡献. 由温度升高引起的反应速率变化对 O₃暴露和 AOC 的影响比 BVOC 排放增加带来的贡献更大.

China’s methane emissions derived from the inversion of GOSAT observations with a CMAQ and EnKS-based regional data assimilation system

At present, inversions at higher temporal and spatial resolution tend to be in increasing demand. The resolution and precision of methane (CH4) inversions depend on quality of observations, transport model, and inversion scheme. Currently, most inversion-based estimates of CH4 in China use a global atmospheric transport model to relate emissions to observations, or a Lagrangian model to quantify the receptor-to-source sensitivity. Taking advantage of the ability that regional transport models have in mesoscale simulation, a regional inversion system was developed to infer China’s CH4 emissions. The CMAQ (Community Multi-scale Air Quality) model was configured for forward simulation of CH4, including processes of emission, transport, diffusion, and chemical transformation. Furthermore, the Ensemble Kalman Smoother was extended to assimilate satellite observations with joint optimization scheme of concentrations and emissions to reduce the impact of initial uncertainty. We found that the posterior annual estimated emissions (53.30 Tg a−1) in 2020 were closer to the official reported figure (53.57 Tg a−1) than to the prior (63.80 Tg a−1), with a downward correction of 16.45% in prior estimates based on extrapolation of the bottom-up inventory, which likely led to overestimation. Moreover, under current observational coverage, monthly posterior estimates reflected region-dependent responses to local conditions. Generally, the regional assimilation system estimated annual and monthly CH4 emissions well, attributable to reliable CMAQ simulation, joint assimilation scheme, and careful selection of satellite retrievals. In addition, evaluation of the posterior estimates indicates that inversion delivers reasonable improvements, but amelioration of uncertainties in prior information and observations is still needed.

Assessing the effectiveness of SO2, NOx, and NH3 emission reductions in mitigating winter PM2.5 in Taiwan using CMAQ

Abstract. Taiwan experiences higher air pollution in winter when fine particulate matter (PM2.5) levels frequently surpass national standards. This study employs the Community Multiscale Air Quality model to assess the effectiveness of reducing SO2, NOx, and NH3 emissions on PM2.5 secondary inorganic species (i.e., SO42-, NO3-, and NH4+). For sulfate, ∼ 43.7 % is derived from the chemical reactions of local SO2 emission, emphasizing the substantial contribution of regionally transported sulfate. In contrast, nitrate and ammonium are predominantly influenced by local NOx and NH3 emissions. Reducing SO2 emissions decreases sulfate levels, which in turn leads to more NH3 remaining in the gas phase, resulting in lower ammonium concentrations. Similarly, reducing NOx emissions lowers HNO3 formation, impacting nitrate and ammonium concentrations by decreasing the available HNO3 and leaving more NH3 in the gas phase. A significant finding is that reducing NH3 emissions decreases not only ammonium and nitrate but also sulfate by altering cloud droplet pH and SO2 oxidation processes. While the impact of SO2 reduction on PM2.5 is less than that of NOx and NH3, it emphasizes the complexity of regional sensitivities. Most of western Taiwan is NOx-sensitive, so reducing NOx emissions has a more substantial impact on lowering PM2.5 levels. However, given the higher mass emissions of NOx than NH3 in Taiwan, NH3 has a more significant consequence in mitigating PM2.5 per unit mass emission reduction (i.e., 2.43 × 10−5 and 0.85 × 10−5 µg m−3 (t yr−1)−1​​​​​​​ for NH3 and NOx, respectively, under current emission reduction). The cost-effectiveness analysis suggests that NH3 reduction outperforms SO2 and NOx reduction (i.e., USD 0.06 billion yr−1 µg−1 m3, USD 0.1 billion yr−1 µg−1 m3, and USD 1 billion yr−1 µg−1 m3 for NH3, SO2, and NOx, respectively, under the current emission reduction). Nevertheless, the costs of emission reduction vary due to differences in methodology and regional emission sources. Overall, this study considers both the efficiency and costs, highlighting NH3 emissions reduction as a promising strategy for PM2.5 mitigation in the studied environment in Taiwan.

Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community

Abstract. The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures.

Enhancing real-time PM2.5 forecasts: A hybrid approach of WRF-CMAQ model and CNN algorithm

As fine particulate matter (PM2.5) poses significant environmental and human health risks, there is an urgent need for accurate forecasting systems. In Taiwan, the current air quality forecasting (AQF) system based on the Weather Research and Forecasting meteorological model and Community Multiscale Air Quality model provides essential predictions but is limited by biases and computational complexities. This study introduces a convolutional neural network (CNN)-based PM2.5 forecasting model to enhance prediction accuracy. The CNN model incorporates hourly PM2.5 concentrations from surface observations and the AQF system, along with synoptic weather patterns (SWPs), to predict PM2.5 levels up to 72 h in advance. Three CNN models were developed: CNN-BASE (without SWPs), CNN-SWP (with SWPs), and CNN-SWPW (with SWPs and a weighted loss function). Performance assessment reveals a significant reduction in the mean RMSE of 72-h PM2.5 prediction, from 10.48 μg/m3 with the AQF system to 6.88 μg/m3 with the CNN-BASE model. However, CNN-BASE showed the lowest prediction accuracy for high PM2.5 concentrations (only 26.2%) due to a small subset of samples. Including SWPs improves the model's ability to capture meteorological influences, enhancing predictions of high PM2.5 concentrations. Furthermore, CNN-SWPW incorporates a weighted loss function to address imbalanced sample size distributions, further enhancing the accuracy of high PM2.5 predictions. This study demonstrates the potential of CNNs in operational air quality forecasting.

Comparison of CAMS and CMAQ analyses of surface-level PM2.5 and O3 over the conterminous United States (CONUS)

To reduce economic and health impacts from poor air quality (AQ) in the U.S., the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) produces forecasts of surface-level ozone (O3), fine particulate matter (PM2.5), and other pollutants so that advance notice and warning can be issued to help individuals and communities limit their exposure. The NAQFC uses the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model for operational forecasts. This study is a first step in proposing a potential upgrade to the current operational NAQFC bias-correction system, by examining potential candidates for a gridded analysis (“truth”) dataset.In this paper, we compare the performance of the “analysis” time series over the period of August 2020–December 2021 at EPA AirNow stations for both PM2.5 and O3 from raw Copernicus Atmosphere Monitoring Service (CAMS) reanalyses, raw CAMS near real-time forecasts, raw near real-time CMAQ forecasts, bias-corrected CAMS forecasts, and bias-corrected CMAQ forecasts (CMAQ FC BC). This 17-month period spans two wildfire seasons, to assess model “analysis” performance in high-end AQ events. In addition to determining the best-performing gridded product, this process allows us to benchmark the performance of CMAQ forecasts against other global datasets (CAMS reanalysis and forecasts). For both PM2.5 and O3, the bias correction algorithm employed here greatly improved upon the raw model time series, and CMAQ FC BC was the best-performing model “analysis” time series, having the lowest RMSE, smallest bias error, and largest critical success index at multiple thresholds.

Regional source contributions to summertime ozone in the Yangtze River Delta

Ozone (O3) pollution is increasing in the Yangtze River Delta (YRD), with significant influences from regional transport during pollution events. However, the specific contributions of different regions remain imprecisely quantified. This study estimated the regional contributions of eight regions to summer O3 in the YRD using the Community Multiscale Air Quality (CMAQ) model with a source-tagged photochemical mechanism. Non-background O3 is attributed to nitrogen oxides (NOX) and volatile organic compounds (VOCs) from different regions. Background O3 is the predominant contributor with average ratios of 75.0%, 65.3%, 64.8% and 63.0% for Shanghai, Nanjing, Hangzhou and the entire YRD, respectively. Locally formed O3 are 39.9, 42.0, 39.6, and 36.9 parts per billion (ppb) by volume in the above areas. North Zhejiang and south Jiangsu are the primary sources of heavy pollution episodes as the maximum daily 8-h average (MDA8) O3 concentrations in these regions increase the most. NOX is the predominant contributor to heavy pollution episodes with the maximum increment attributed to NOX (O3N) of 14.3 ppb, while VOCs only contribute to 2.1 ppb (O3V) in Jiangsu. Relative humidity is crucial in heavy pollution episodes while high temperature, low planetary boundary layer (PBL) height and the wind field are associated with regional transport. Diurnal variation underscores the importance of understanding O3 formation in the afternoon (12:00–16:00), which is essential for devising effective mitigation policies.

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.

Health Impacts of Fine Particulate Matter Shift Due to Urbanization in China.

Rapid urbanization and industrialization have resulted in diverse anthropogenic activities and emissions between urban and non-urban regions, leading to varying levels of exposure to air pollutants and associated health risks. However, endeavors to mitigate air pollution and health benefits have displayed considerable heterogeneity across different regions. Therefore, comprehending the changes in air pollutant concentrations and health impacts within an urbanization context is imperative for promoting environmental equity. This paper uses gross domestic product (GDP)- and population-weighted methods to distinguish anthropogenic emissions from urban and non-urban areas in China and quantified their contributions to fine particulate matter (PM2.5) using the Community Multiscale Air Quality (CMAQ) model in 2010 and 2019. Anthropogenic emissions from urban and non-urban (outside urban) regions decreased by 26 and 44% from 2010 to 2019, respectively, resulting in 31 and 28% reductions of PM2.5 in China. PM2.5-related premature mortality attributed to non-urban and urban anthropogenic emission decreases by 8%. Non-urban anthropogenic activities are the main contributor to PM2.5 (56% in 2010 and 2019) and its associated premature mortality (59%), which also predominantly affects non-urban premature mortality (37-42% in 2010-2019). Population changes increase the proportion of premature mortality in urban populations (7-19%) from 2010 to 2019. This study emphasizes the shift of affected populations due to urbanization and population changes.

Improving visibility forecasting during haze-fog processes in shanghai and eastern China: The significance of aerosol and hydrometeor extinction

Aerosols and droplets are the main factors of visibility reduction by scattering and absorbing light. In this study we use the 3-D meteorology fields from the 9-km WRF-ADAS Real-Time Modeling System (WARMS) to drive the Community Multiscale Air Quality (CMAQ) model. A visibility forecasting framework is developed by combining extinction due to hydrometeors and aerosols, based on WARMS and CMAQ predictions. We have analyzed the results of a one-month forecasting period during the winter of 2021–2022 to assess the performance of the WARM-CMAQ model and to understand the impact of hydrometeor and aerosol extinction on operational visibility forecasting in Shanghai, the Yangtze River Delta (YRD), and part of the North China Plain (NCP). With our newly developed framework, we generally find improved or comparable results in visibility forecasts when compared to recent studies. We find that for the city of Shanghai, aerosol extinction has a minor impact on the model's performance when forecasting visibility below 1 km, but it becomes crucial for predictions spanning 1–10 km. Comparison against observations shows that the framework well captures the general contributions from various chemical constituents with nitrate as the most important factor in aerosol extinction (∼60%). Finally, our assessment of the YRD and part of the NCP highlights that in highly polluted areas aerosols could be significant for visibility below 1 km. This study emphasizes the necessity of integrating both aerosol and hydrometeor extinction in visibility forecasting during haze-fog processes, particularly for regions characterized by diverse pollutant levels and environmental conditions.

Potential environmental impact of the chlorine-containing disinfectants usage during the COVID-19

Chlorine radicals (Cl) play a crucial role in atmospheric chemistry. The COVID-19 (coronavirus disease 2019) pandemic significantly increased the use of chlorine-containing disinfectants in China, leading to enhanced emissions of chlorine gas (Cl2) and hypochlorous acid (HOCl). In this study, we use the Community Multiscale Air Quality (CMAQ) model with updated chlorine chemistry to evaluate the potential air quality impact of chlorine emissions from disinfectant usage during February 2020, a month with a large outbreak of COVID-19 in mainland China. Results indicate that during the pandemic, there was a sharp increase of reactive chlorine emissions, with Cl2 and HOCl emissions reaching 773.9 t and 5913.1 t, making 3 times increase. The emissions of chlorine enhanced atmospheric oxidation capacity (AOC) through the oxidation of VOC by Cl and OH, with the average enhancement in Shanghai, Beijing and Wuhan areas reaching 4.6% ± 1.8%, 10.3% ± 6.6% and 7.4% ± 4.6%, respectively. Consequently, the use of chlorine-containing disinfectants may have contributed to observed increases in the monthly average of the maximum daily 8-h average ozone (MDA8 O3) and fine particulate matter (PM2.5) concentrations by up to 1.5 ppbv (5%) and 1.7 μg/m3 (1%), respectively. Especially, the maximum hourly increase values of O3 and PM2.5 concentrations were 4.8 ppbv and 11.4 μg/m³, 2.5 ppbv and 4.5 μg/m³ in Beijing and Wuhan, respectively. These results indicate that chlorine emissions from the widespread use of disinfectants have had a significant impact on air quality in certain regions (Beijing and Wuhan) and during specific periods (9:00∼12:00).

A comparison of population-level exposure and equity tradeoffs across strategies to reduce fine particulate matter emissions from transportation sources in the northeastern US

BackgroundMany climate mitigation policies to reduce transportation emissions have public health benefits related to ambient air pollution. However, few health analyses consider the equity implications of alternative policies. Equity can be conceptualized in many different ways that may be relevant to communities, decision-makers, and other stakeholders. ObjectivesTo evaluate alternative transportation emissions reduction scenarios across the northeastern United States considering population exposure reductions and multiple equity constructs. MethodsWe developed four quantitative indicators reflecting equity constructs that aligned with stakeholder perspectives, including racial/ethnic exposure inequities, proportion of benefits in environmental justice communities, distribution of benefits among participating states, and rural/urban share of benefits. We analyzed numerous transportation emissions reduction scenarios for directly emitted fine particulate matter (primary PM2.5) covering 12 Northeast states and the District of Columbia. We used the Community Multiscale Air Quality model with the decoupled direct method to estimate the reduction in population-weighted primary PM2.5 exposure and the impact on equity for each scenario. ResultsScenarios that yielded greater reductions in population-weighted primary PM2.5 exposure generally emphasized emissions reductions in urban areas or states with large urban centers, with a more than threefold difference in benefits across scenarios. The higher exposure-benefit scenarios typically also had greater reductions in racial/ethnic exposure inequities but led to higher between-state or rural/urban inequality. Scenarios that targeted uniform percentage emission reductions from light or heavy-duty trucks best addressed rural/urban inequalities but led to the smallest reductions in racial/ethnic inequity. ConclusionThere are intrinsic tradeoffs among equity constructs, where focusing resources on distributing benefits across states or between urban and rural populations could come at the expense of less reduction in racial/ethnic exposure inequities or in environmental justice communities. Future health benefits analyses should incorporate multiple equity indicators that reflect different stakeholder perspectives and articulate the underlying constructs and tradeoffs.

Optimization of a PM2.5 automatic monitoring network based on the modeling approach – A case study in Long An province, Vietnam

Abstract Currently, fine particulate matter (PM2.5) pollution is one of the well-recognized serious environmental health risks. In particular, PM2.5 has remarkably impacted the human respiratory, cardiovascular, and circulatory systems. Vietnam has frequently ranked among the countries with the highest annual mean PM2.5 levels in Southeast Asia and worldwide. Thus, building a real-time continuous ambient PM2.5 monitoring network is an important environmental management tool to reduce health effects attributable to PM2.5 exposure. Nevertheless, measurement locations of an air quality monitoring (AQM) network depend strongly on meteorological conditions in the area and sensitivity to local primary emission sources, which significantly determines the AQM network’s operational efficiency. This study aims to estimate and optimize outdoor PM2.5 monitoring sites of an automatic air quality monitoring (AAQM) network using a system of low-cost sensors in Long An province, based on the spatio-temporal distribution assessment results of PM2.5 concentrations from WRF (Weather Research and Forecasting Model)/CMAQ (the Community Multiscale Air Quality) models. The prominent study outcomes have shown that the 2018 average PM2.5 concentration in Long An province exceeded the threshold of QCVN 05:2023/BTNMT (25 μg/m3) from 5.6 to 7.9 times. Moreover, this study has proposed four optimal monitoring sites (AQ1, AQ2, AQ3, and AQ4) for the automatically real-time PM2.5 observation in the areas of Tan An City, Can Giuoc District, the ring roads 3 and 4, and Kien Tuong Town. The study has provided an important scientific basis and support for local air quality management activities; furthermore, the newly proposed monitoring locations will complement the province’s periodic monitoring program towards 2025.

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.

Health benefits of phasing out coal-fired power plants in Ontario, Alberta, and Canada

Coal-fired power plants (CFPPs) in Ontario were decommissioned in a phased approach from 2003 to 2014. After Ontario's commitment to eliminate coal-based electricity generation, Alberta and Canada have planned on phasing out coal in the electricity sector. The coal regulation in Canada targets coal-free electricity generation by 2030, accelerating the criteria air pollutant emission reductions in the CFPPs nationwide. The Regulatory Impact Analysis Statement (RIAS) under the coal regulation estimated substantial health benefits based on scenario analysis. Here we use adjoint sensitivity analysis as an alternative approach to estimate the health impacts of CFPPs and non-coal electric generating units (EGUs). The adjoint of the U.S. EPA's Community Multiscale Air Quality (CMAQ) model is used to attribute PM2.5-related health impacts for primary and precursor PM2.5 emissions emitted from the CFPPs and non-coal EGUs in Canada using various epidemiological studies. Our findings suggest that the reduction of CFPP emissions yields more total benefits rather than other fuel types in EGUs. A significant amount of total health burden is estimated to occur within the province that hosts the facility, and with a larger portion of this benefit coming in warmer seasons. Our retrospective analysis of Ontario coal phase-out estimates $1B CAD total annual health benefits in 2003, or projected 2014 benefits of $1.8 B CAD. The transboundary impact of emission reductions from coal-fired power plants in the U.S. entails significant health benefits to Ontario and Canada, more than twice that of Ontario's own coal phase-out. Nationwide, total health burden estimate for CFPPs and non-coal EGUs is $418M CAD in 2014. The choice of epidemiological model has a sizeable impact on estimated burdens or benefits.

Diverging trends in aerosol sulfate and nitrate measured in the remote North Atlantic in Barbados are attributed to clean air policies, African smoke, and anthropogenic emissions

Abstract. Sulfate and nitrate aerosols degrade air quality, modulate radiative forcing and the hydrological cycle, and affect biogeochemical cycles, yet their global cycles are poorly understood. Here, we examined trends in 21 years of aerosol measurements made at Ragged Point, Barbados, the easternmost promontory on the island located in the eastern Caribbean Basin. Though the site has historically been used to characterize African dust transport, here we focused on changes in nitrate and non-sea-salt (nss) sulfate aerosols from 1990–2011. Nitrate aerosol concentrations averaged over the entire period were stable at 0.59 µg m−3 ± 0.04 µg m−3, except for elevated nitrate concentrations in the spring of 2010 and during the summer and fall of 2008 due to the transport of biomass burning emissions from both northern and southern Africa to our site. In contrast, from 1990 to 2000, nss-sulfate decreased 30 % at a rate of 0.023 µg m−3 yr−1, a trend which we attribute to air quality policies enacted in the United States (US) and Europe. From 2000–2011, sulfate gradually increased at a rate of 0.021 µg m−3 yr−1 to pre-1990s levels of 0.90 µg m−3. We used the Community Multiscale Air Quality (CMAQ) model simulations from the EPA's Air QUAlity TimE Series (EQUATES) to better understand the changes in nss-sulfate after 2000. The model simulations estimate that increases in anthropogenic emissions from Africa explain the increase in nss-sulfate observed in Barbados. Our results highlight the need to better constrain emissions from developing countries and to assess their impact on aerosol burdens in remote source regions.

The role of naphthalene and its derivatives in the formation of secondary organic aerosol in the Yangtze River Delta region, China

Abstract. Naphthalene (Nap) and its derivatives, including 1-methylnaphthalene (1-MN) and 2-methylnaphthalene (2-MN), serve as prominent intermediate volatile organic compounds (IVOCs) and contribute to the formation of secondary organic aerosol (SOA). In this study, the Community Multiscale Air Quality (CMAQ) model coupled with detailed emissions and reactions of these compounds was utilized to examine their roles in the formation of SOA and other secondary pollutants in the Yangtze River Delta (YRD) region during summer. Significant underestimations of Nap and MN concentrations (by 79 % and 85 %) were observed at the Taizhou site based on the model results using the default emissions. Constrained by the observations, anthropogenic emissions of Nap and MN in the entire region were multiplied by 5 and 7, respectively, to better capture the evolution of pollutants. The average concentration of Nap reached 25 ppt (parts per trillion) in the YRD, with Nap contributing 4.1 % and 8.1 % (up to 12.6 %) of total aromatic emissions and aromatic-derived secondary organic carbon (SOC), respectively. The concentrations of 1-MN and 2-MN were relatively low, averaging 2 and 5 ppt, respectively. Together, they accounted for only 2.4 % of the aromatic-derived SOC. The impacts of Nap and MN oxidation on ozone and radicals were insignificant at regional scales but were not negligible when considering daily fluctuations in locations with high emissions of Nap and MN. This study highlights the significant roles of Nap and MN in the formation of SOA, which may pose environmental risks and result in adverse health effects.

Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (β: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (β:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.