Community Multiscale Air Quality Research Articles

A Nation-by-Nation Assessment of the Contribution of Southeast Asian Open Biomass Burning to PM2.5 in Thailand Using the Community Multiscale Air Quality-Integrated Source Apportionment Method Model

This study utilized the Community Multiscale Air Quality (CMAQ) model to assess the impact of open biomass burning (OBB) in Thailand and neighboring countries—Myanmar, Laos, Cambodia, and Vietnam—on the PM2.5 concentrations in the Bangkok Metropolitan Region (BMR) and Upper Northern Region of Thailand. The Upper Northern Region was further divided into the west, central, and east sub-regions (WUN, CUN, and EUN) based on geographical borders. The CMAQ model was used to simulate the spatiotemporal variations in PM2.5 over a wide domain in Asia in 2019. The Integrated Source Apportionment Method (ISAM) was utilized to quantify the contributions from OBB from each country. The results showed that OBB had a minor impact on PM2.5 in the BMR, but transboundary transport from Myanmar contributed to an increase in PM2.5 levels during the peak burning period from March to April. In contrast, OBB substantially impacted PM2.5 in the Upper Northern Region, with Myanmar being the major contributor in WUN and CUN and domestic burning being the major contributor to EUN during the peak months. Despite Laos having the highest OBB emissions, meteorological conditions caused the spread of PM2.5 eastward rather than into Thailand. These findings highlight the critical impact of regional transboundary transport and emphasize the necessity for collaborative strategies for mitigating PM2.5 pollution across Southeast Asia.

Research on Ozone Pollution Characteristics and Source Apportionment During the COVID-19 Lockdown in Jilin City in 2022

The increasing Ozone (O3) concentration in various regions of China has garnered significant attention, highlighting the need to understand the mechanisms of O3 formation. This study focuses on the source apportionment of O3 in Jilin City during and after the COVID-19 lockdown countermeasure, and also the influence of anthropogenic emissions on O3 concentration. The contributions of different O3 emission sources were quantified using the Weather Research and Forecasting Community Multi-Scale Air Quality (WRF-CMAQ) model in conjunction with the Integrated Source Apportionment Method (ISAM). The results indicate a significant increase in O3 concentrations during the lockdown in Jilin City, which were particularly characterized by long-distance transportation. Transportation is identified as the primary direct source of O3 in Jilin City, with Yongji County contributing the most among the six designated regions. This study highlights variations in the causes and sources of O3 pollution among the different regions of Jilin City. Simply controlling anthropogenic emissions is inadequate for effectively managing O3 pollution and may even worsen the situation. It is more effective to focus on controlling O3’s precursors. These findings improve the understanding of O3 pollution in Jilin City and provide valuable insights for developing O3 control policies. Similarly, this research is applicable to other countries and regions.

Evaluating long-term reductions in trace metal emissions from shipping in Shanghai

Shipping emissions are a major contributor to air pollution in coastal cities, and Shanghai Port, the busiest port in the world, handles over 40 million TEU annually. To mitigate shipping emissions, China introduced the Domestic Emission Control Area (DECA) policy in phases: DECA 1.0 in 2016 and DECA 2.0 in 2019. DECA 1.0 mandated low-sulfur fuels (0.5 %) at berth and near major ports, down from 1.5 %, and this requirement was extended to a 12-nautical-mile emission control zone in DECA 2.0. In this study, we conducted long-term online measurements of shipping emission tracer of vanadium (V) and nickel (Ni), at the Dian Shan Lake (DSL) supersite, located approximately 50 km from Shanghai waters. The observed V concentration exhibited a strong dependence on wind direction, with higher levels from spring to summer due to more frequent marine winds compared to other seasons. The long-term measurement showed a significant decrease in V concentrations, dropping from an annual mean of 7.08 ng m-³ during DECA 1.0 to 2.64 ng m-³ during DECA 2.0. The represents a year-on-year reduction of 63 % based on measurement. To remove the meteorological impact on the measured concentration, a Random Forest (RF) machine learning model and the Community Multiscale Air Quality (CMAQ 5.4) model were applied under a business-as-usual (BAU) scenario. Both models produced consistent results, showing a reduction of up to 82 % in V concentrations in April with more frequent marine winds, confirming regional air quality improvements post-DECA 2.0. Additionally, we found that Ni has other sources that require further control beyond shipping. The study highlights the importance of long-term measurements for understanding the impact of air quality policies on emission patterns.

Simulated Surface‐Column NO2 Connections for Satellite Applications

AbstractObservations of near‐surface NO2 show a diurnal pattern with midday minima and daily maxima in the morning and evening. These surface cycles are dependent on chemical processing, transport, and emissions. We evaluate these cycles with the United States Environmental Protection Agency (EPA) community multiscale air quality (CMAQ) model data from the EPA Air Quality Time Series (EQUATES) project, compared with ground‐based measurements from the EPA air quality system, two Pandora measurement sites, and satellite data from TROPOMI. We find that the morning vertical column density (VCD) lags surface concentrations by 1 hr on average, where this lag varies with location and day. The peak VCD can also lead the surface maximum concentration, especially in the evening, responding to transport and afternoon compression of the boundary layer. Modeled NO2 VCD is sensitive to column calculation technique. With hourly daytime satellite‐based NO2 observations newly available from the TEMPO instrument, the timing and magnitude of cycles in near‐surface NO2 versus column NO2 will help inform the utilization of hourly satellite data. This work will help inform the timing of surface‐column connections to better interpret new hourly satellite observations for health and air quality applications, including emissions characterization.

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.

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.

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.

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.

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.

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.

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.

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.

Study of condensable particulate matter from stationary combustion sources: Source profiles, emissions, and impact on ambient fine particulate matter

Although significant progress has been made in controlling emissions from stationary combustion sources in China over the past decade, understanding of condensable particulate matter (CPM) emissions from these sources and their impact on ambient PM2.5 remains limited. In this study, we established the source profiles and emission inventories of CPM from coal-fired industrial boilers (CFIBs), coal-fired power plants (CFPPs), and iron and steel industry (ISIs) for the Yangtze River Delta (YRD) region of China; furthermore, the air quality model (Community Multiscale Air Quality, CMAQ) was used to evaluate the impact of CPM emissions from these three types of stationary combustion sources on ambient PM2.5 during Feb. 2018, a month characterized by elevated PM2.5 concentrations. The results indicated that CPM emissions from these three sources in the YRD region before and after the implementation of the ultra-low emissions (ULE) policy amounted to 109,839 and 43,338 tons, respectively, with particularly high emission intensity along the Yangtze River. The implementation of CFPPs ULE policy was shown to reduce the impact of CPM emissions from these three stationary sources on monthly PM2.5 concentrations from 0.92 μg/m3 to 0.41 μg/m3 (with a maximum of 5.35 μg/m3). This reduction exceeded the 0.31 μg/m3 decrease in PM2.5 concentrations resulting from the emission reductions of conventional pollutants (FPM, SO2 and NOx). CPM emissions from these three stationary sources were found to increase the PM2.5 by 0.68 μg/m3 during pollution periods. The largest components of PM2.5 contributed by CPM emissions from stationary combustion sources were sulfate, organic carbon, and nitrate, accounting for 21.4 %, 21.1 %, and 18.2 %, respectively. Particularly, contributions from CPM emissions to PM2.5 varied by altitude, with a relatively large impact at altitudes between 220 and 460 m. Attention should be given to CPM emission control, with particular priority placed on implementing ULE measures for ISIs and CFIBs.

Monthly Characteristics and Source–Receptor Relationships of Anthropogenic Total Nitrate in Northeast Asia

The complex nonlinear characteristics of atmospheric chemistry necessitate the development of new methods for calculating source–receptor (S–R) relationships for secondary air pollutants. In this study, the monthly characteristics and S–R relationships of anthropogenic total nitrate (i.e., the sum of N from nitric acid, inorganic nitrate, and peroxyacetyl nitrate) in Northeast Asia were simulated and analyzed. The Community Multiscale Air Quality (CMAQ), Fifth-Generation NCAR/Penn State Mesoscale (MM5), and Sparse Matrix Operator Kernel Emissions (SMOKE) models were employed for air quality modeling, meteorological fields, and emissions processing, respectively. The study area encompassed Republic of Korea, Japan, and most of China. Five source/receptor regions were defined to derive the S–R relationships: three in China, one in Republic of Korea, and one in Japan. To produce data for the calculation of the S–R relationship, several experiments were conducted with a 20% reduction in NOx emission sources. As a result of the S–R relationships, China was rarely impacted by the other two countries. The total depositions in other countries were significantly dominated by China (i.e., 43.5% and 40.7% in Republic of Korea and Japan, respectively, and up to 82.3% in December for Republic of Korea).

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.

Spatiotemporally Detailed Quantification of Air Quality Benefits of Emissions Reductions-Part I: Benefit-per-Ton Estimates for Canada and the U.S.

The U.S. EPA's Community Multiscale Air Quality (CMAQ)-adjoint model is used to map monetized health benefits (defined here as benefits of reduced mortality from chronic PM2.5 exposure) in the form of benefits per ton (of emissions reduced) for the U.S. and Canada for NOx, SO2, ammonia, and primary PM2.5 emissions. The adjoint model provides benefits per ton (BPTs) that are location-specific and applicable to various sectors. BPTs show significant variability across locations, such that only 20% of primary PM2.5 emissions in each country makes up more than half of its burden. The greatest benefits in terms of BPTs are for primary PM2.5 reductions, followed by ammonia. Seasonal differences in benefits vary by pollutant: while PM2.5 benefits remain high across seasons, BPTs for reducing ammonia are much higher in the winter due to the increased ammonium nitrate formation efficiency. Based on our location-specific BPTs, we estimate a total of 91,000 U.S. premature mortalities attributable to natural and anthropogenic emissions.

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.

Spatiotemporally Detailed Quantification of Air Quality Benefits of Emissions-Part II: Sensitivity to Study Parameters and Assumptions.

Adjoint modeling, using U.S. EPA's Community Multiscale Air Quality (CMAQ), has been performed to provide location-specific monetized health benefits from the controls of primary PM2.5 and PM2.5 precursors (NO x , SO2, and NH3) across North America. Source-to-health benefit relationships are quantified using a benefit-per-ton (BPT) metric, accounting for the impacts on premature mortality due to long-term exposure to fine particulate matter. In the base analysis, the approach used a 12 km resolution, four 2-week episodes chosen to capture annual responses, emissions for 2016, and the Global Exposure Mortality Model (GEMM) to link exposures to premature mortality. Here, we investigate the impacts those choices have on results using a range of sensitivity analyses. The choice of four representative episodes led to relatively little bias and error. Finer model resolution, investigated by comparing 36, 12, 4, and 1 km simulations over two urban areas, tended to increase BPT estimates, though the impact was inconsistent between different regions. While BPTs and burden estimates were consistent across resolutions over New York City, they sharply increased for Los Angeles, particularly for NOx and ammonia, leading to 90% increase in burden estimates at 1 km resolution. We find that, for primary PM2.5 emissions, better resolved population distribution is the main contributing factor to higher BPTs, but for secondary precursor emissions (ammonia and NOx), higher model resolution that avoids dilution in coarser grids is more important. Changing emissions from 2016 to 2001 and 2028 resulted in fairly consistent primary PM2.5 BPTs but impacted the BPTs for NOx and ammonia more significantly due to changes in SO2 emissions. We found that BPTs tend to stabilize, as emission changes in 2028 lead to a lower deviation from 2016 BPTs compared to changes from the 2001 episode. The role of the epidemiological model also led to relatively modest uncertainties, 15-30% depending on the species, even when different shapes of concentration-response functions were employed. We found the impact of the choice of CRF to be larger or comparable in size to the reported epidemiological model uncertainties for log-linear CRFs. The adjoining approach proved robust to modeling choices in providing BPT estimates that are highly granular across locations and emitted species.

Enhanced urban PM2.5 prediction: Applying quadtree division and time-series transformer with WRF-chem

Accurately predicting air quality levels in urban landscapes is a key environmental and public health challenge due to the complex interactions between pollutant emissions, atmospheric chemistry, and meteorological conditions. Chemical transport models such as Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting Modeling with Chemistry (WRF-Chem) provide fundamental insights into the behavior of atmospheric pollutants but face limitations in spatial resolution and prediction accuracy. Machine learning combined with low-cost sensors can improve prediction accuracy compared to numerical models, but uneven sensor distribution may result in biased prediction results. To address these limitations, this study introduces an innovative framework that leverages a quadtree space division to refine the numerical simulations of air pollutants into higher spatial resolutions by dynamically adjusting grid sizes based on the distribution of increased sensor deployments. Using a variable-resolution grid, the study first aggregates variables into a grid and then calculates spatial dependence based on grid cells. The deep-learning-based Time Series Transformer model is then used to perform detailed temporal predictions of PM2.5 concentration across the entire grid. The spatial and temporal modeling framework is designed to use the past 48 h' aggregated data, including meteorological variables from WRF-Chem numerical simulation and PM2.5 concentrations from ground monitoring stations and sensors, to predict future 48-h’ PM2.5 concentrations and tested across entire grids within Los Angeles County from January to June 2020. The results show that the performance of variable resolution grids is better than that of fixed grids regarding four metrics: minimum resolution, overall spatiotemporal RMSE, average bias, and prediction coverage. Among them, the variable resolution grids with up to 4 sensors showed the best performance, with an overall spatiotemporal RMSE of 1.12 μg/m3, which was about 40% lower than 2-sensor and 8-sensor variable resolution grids, a significant reduction of 55% compared to a 3 km fixed grid configuration. Future work will involve additional relevant factors such as emission sources and sinks to improve prediction accuracy.

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.