Air Quality Model Research Articles

Regional transport characteristics of PM2.5 pollution events in Beijing during 2018–2021

Although air pollutant emissions have sharply reduced in recent years, the occurrence of PM2.5 pollution events remains an intractable environmental problem in Beijing, and regional transport is the key influence factor. However, it has been difficult to identify regional transport characteristics and the main contributors to pollution events in recent years. In this study, the relative contribution of regional transport was quantified (61.3%) in PM2.5 pollution events during 2018-2021 by the Community Multiscale Air Quality model embedded with the Integrated Source Apportionment Model (CMAQ-ISAM). The four regions with the largest fractional contributions to Beijing for all events were Shandong (7.7%), South Hebei (7.3%), Baoding (6.2%), and Langfang (5.8%). Pollution events were classified into the following types based on regional transport directions: local, southwest (SW), southeast (SE), south-mixed (SM), and others. Based on the transport distance, the SW, SE, and SM types can be subdivided into SW-short, SW-long, SE-short, SE-long, SM-short, SM-long distance from southwest, SM-long distance from southeast, and SM-long distance from southwest and southeast. SE-long was regarded as the most important type, with the highest relative frequency (20%). The transport directions were related to the southwest wind at 925 hPa and southeast wind at 1000 hPa in the south of the Beijing–Tianjin–Hebei (BTH) region, and the distance was mainly controlled by wind strength. The wind-field difference can be attributed to the low-pressure and high-pressure systems that control the BTH region. The results suggest that regional joint pollution control should be optimized based on the transport type.

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.

Unequal Health Risks and Attributable Mortality Burden of Source-Specific PM2.5 in China.

Anthropogenic emissions, originating from human activities, stand as the primary contributors to PM2.5, which is recognized as a global health threat. The disease burden associated with PM2.5 has been extensively documented. However, the prevailing estimations have predominantly relied on PM2.5 exposure-response functions, neglecting the distinct risks posed by PM2.5 from various sources. China has experienced a significant reduction in the PM2.5 concentration due to stringent emission controls. With diverse sources and abundant mortality data, this situation provides a unique opportunity to estimate short-term source-specific attributable mortality. Our approach involves an integrated unequal health risk-oriented modeling in China, incorporating a source-oriented Community Multiscale Air Quality model, an adjustment and downscaling method for exposure measurement, a generalized linear model with random-effects meta-analysis, and premature mortality estimation. Adhering to the unequal health risk concept, we calculated the attributable mortality of multiple PM2.5 sources by determining the source risk-adjusted factor. In this study, we observed varying excess risks associated with multiple PM2.5 sources, with transportation-related PM2.5 exhibiting the most substantial association. An interquartile range increase (7.65 μg/m3) was linked to a 1.98% higher daily nonaccidental mortality. Residential use- and transportation-related PM2.5 emerged as the two principal sources of premature mortality. In 2018, a remarkable 53,381 avoiding deaths were estimated compared to 2013, and over 67% of these were attributed to reductions in coal-dependent sources. Notably, transportation-related PM2.5 emerged as the largest contributor to premature mortality in 2018. This study underscores the significance of a new source-oriented health risk assessment to support actions aimed at reducing air pollution. It strongly advocates for heightened attention to PM2.5 reductions in the transportation sector in China.

Dual roles of the inorganic aqueous phase on secondary organic aerosol growth from benzene and phenol

Abstract. Benzene, emitted from automobile exhaust and biomass burning, is ubiquitous in ambient air. Benzene is a precursor hydrocarbon (HC) that forms secondary organic aerosol (SOA), but its SOA formation mechanism is not well studied. To accurately predict the formation of benzene SOA, it is important to understand the gas mechanisms of phenol, which is one of the major products formed from the atmospheric oxidation of benzene. Laboratory data presented herein highlight the impact of the aqueous phase on SOA generated through benzene and phenol oxidation. The roles of the aqueous phase consist of (1) suppression of the aging of hydrocarbon and (2) conventional acid-catalyzed reactions in the inorganic phase. To explain this unusual effect, it is hypothesized that a persistent phenoxy radical (PPR) effectively forms via a heterogeneous reaction of phenol and phenol-related products in the presence of wet inorganic aerosol. These PPR species are capable of catalytically consuming ozone during an NOx cycle and negatively influencing SOA growth. In this study, explicit gas mechanisms were derived to produce the oxygenated products from the atmospheric oxidation of phenol or benzene. Gas mechanisms include the existing Master Chemical Mechanism (MCM v3.3.1), the reaction path for peroxy radical adducts originating from the addition of an OH radical to phenols forming low-volatility products (e.g., multi-hydroxy aromatics), and the mechanisms to form heterogeneous production of PPR. The simulated gas products were classified into volatility- and reactivity-based lumped species and incorporated into the Unified Partitioning Aerosol Reaction (UNIPAR) model that predicts SOA formation via multiphase reactions of phenol or benzene. The predictability of the UNIPAR model was examined using chamber data, which were generated for the photooxidation of phenol or benzene under controlled experimental conditions (NOx levels, humidity, and inorganic seed types). The SOA formation from both phenol and benzene still increased in the presence of wet inorganic seed because of the oligomerization of reactive organic species in the aqueous phase. However, model simulations show a significant suppression of ozone, the oxidation of phenol or benzene, and SOA growth compared with those without PPR mechanisms. The production of PPR is accelerated in the presence of acidic aerosol and this weakens SOA growth. In benzene oxidation, up to 53 % of the oxidation pathway is connected to phenol formation in the reported gas mechanism. Thus, the contribution of PPR to gas mechanisms is less than that of phenol. Overall, SOA growth in phenol or benzene is negatively related to NOx levels in the high-NOx region (HC ppbC / NOx ppb < 5). However, the simulation indicates that the significance of PPR rises with decreasing NOx levels. Hence, the influence of NOx levels on SOA formation from phenol or benzene is complex under varying temperature and seed type conditions. Adding the comprehensive reaction of phenolic compounds will improve the prediction of SOA formation from aromatic HCs due to the missing mechanisms in the current air quality model.

The various synergistic impacts of precursor emission reduction on PM2.5 and O3 in a typical industrial city with complex distributions of emissions

The synergistic responses of O3 and PM2.5 to their common precursors remain unclear within industrial cities with complex emissions. In this study, hundreds of scenarios of jointly reduced local NOx and VOCs emissions were designed along with the source apportionment techniques embedded in the Comprehensive Air quality Model with extensions (CAMx) system to explore the locally formed O3 and PM2.5 sensitivities to the reduced emissions of NOx and VOCs. The results indicate that locally formed O3 and PM2.5 are more connected to local emissions, resulting in unique formation sensitivities. Local O3 formation is usually in a transitional regime and transferred to VOC-limited condition under O3-polluted conditions due to high VOC emissions. Locally formed O3 and PM2.5 vary largely in different functional regions due to different emission feature and meteorological condition. When reducing VOCs emissions alone, an increase in PM2.5 formation could be observed due to the increase in the formation of nitrate resulting from reduced competition of NOx in O3 formation. To reduce PM2.5 and O3 concentrations simultaneously, specific ratios of NOx reduction percentage to VOC reduction percentage should be considered to different functional regions under different pollution levels. This research highlights the importance to conduct targeted sensitivity tests for emission reduction in different functional zones with complex emission features for the coordinated control of O3 and PM2.5 pollution in typical industrialized cities.

HDLP: air quality modeling with hybrid deep learning approaches and particle swam optimization

HDLP: air quality modeling with hybrid deep learning approaches and particle swam optimization

Blockchain and IoT integration for secure short-term and long-term air quality monitoring system using optimized neural network.

Accurate air pollution prediction is vital for residents' well-being. This research introduces a secure air quality monitoring system using neural networks and blockchain for robust analysis, precise predictions, and early pollution detection. Blockchain guarantees data integrity, security, and transparency. Goals include real-time air quality data, secure blockchain recording, and enhanced safety through informed decisions. The research integrates blockchain and IoT for short- and long-term air quality monitoring, utilizing an optimized neural network. IoT sensors collect PM2.5, PM10, CO, NO2, and SO2, processed through noise removal and normalization, with feature extraction using N-tuple contrastive learning. Predictions utilize Graph attention-based deep Residual shrinkage Network and Bidirectional long short Term Memory (GRNBTM) categorized into five levels. An adaptive bowerbird algorithm optimizes parameters, reducing computational complexity. Blockchain integration ensures secure, tamper-proof data storage with a lightweight consensus-based algorithm. The GRNBTM model's air quality monitoring performance is extensively simulated and analyzed at 30-min, 2-h, 1-day, and 1-month intervals, demonstrating superior performance over existing techniques.

Response of ozone to meteorology and atmospheric oxidation capacity in the Yangtze river Delta from 2017 to 2020

China implemented a stringent clean air initiative to improve air quality in 2013. By 2017, fine particulate matter (PM2.5) levels in the Yangtze River Delta (YRD) region dropped significantly, but ozone (O3) pollution surged, which aroused concerns about its adverse impact on crops, climate and human health. Due to the complex influences of atmospheric processes, the variation of O3 is highly nonlinear, making the control of O3 pollution highly challenging. It is urgent to deepen the understanding of the role of meteorological factors and atmospheric oxidation capacity (AOC) during O3 episodes. To provide references for O3 pollution control, the study used the Weather Research and Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) simulations in the YRD from 2017 to 2020 to analyze the causes of severe O3 pollution. Observations revealed a slight decline in heavily O3 polluted days in the YRD region and the simulation results showed that O3 pollution was more prevalent at temperatures above 25 °C and relative humidity below 80%. The increase in volatile organic compounds (VOCs), hydroxyl radical (OH) and nitrate radical (NO3) was also more conducive to the formation of O3 pollution. For areas that are primarily VOC-limited areas, such as Shanghai, a stricter policy on VOC control is needed to reduce O3 pollution more effectively.

Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires

Abstract. Extreme biomass burning (BB) events, such as those seen during the 2019–2020 Australian bushfire season, are becoming more frequent and intense with climate change. Ground-based observations of these events can provide useful information on the macro- and micro-physical properties of the plumes, but these observations are sparse, especially in regions which are at risk of intense bushfire events. Satellite observations of extreme BB events provide a unique perspective, with the newest generation of geostationary imagers, such as the Advanced Himawari Imager (AHI), observing entire continents at moderate spatial and high temporal resolution. However, current passive satellite retrieval methods struggle to capture the high values of aerosol optical thickness (AOT) seen during these BB events. Accurate retrievals are necessary for global and regional studies of shortwave radiation, air quality modelling and numerical weather prediction. To address these issues, the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm has used AHI data to measure extreme BB plumes from the 2019–2020 Australian bushfire season. The sensitivity of the retrieval to the assumed optical properties of BB plumes is explored by comparing retrieved AOT with AErosol RObotic NETwork (AERONET) level-1.5 data over the AERONET site at Tumbarumba, New South Wales, between 1 December 2019 at 00:00 UTC and 3 January 2020 at 00:00 UTC. The study shows that for AOT values > 2, the sensitivity to the assumed optical properties is substantial. The ORAC retrievals and AERONET data are compared against the Japan Aerospace Exploration Agency (JAXA) Aerosol Retrieval Product (ARP), Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue over land, MODIS MAIAC, Sentinel-3 SYN and VIIRS Deep Blue products. The comparison shows the ORAC retrieval significantly improves coverage of optically thick plumes relative to the JAXA ARP, with approximately twice as many pixels retrieved and peak retrieved AOT values 1.4 times higher than the JAXA ARP. The ORAC retrievals have accuracy scores of 0.742–0.744 compared to the values of 0.718–0.833 for the polar-orbiting satellite products, despite successfully retrieving approximately 28 times as many pixels over the study period as the most successful polar-orbiting satellite product. The AHI and MODIS satellite products are compared for three case studies covering a range of BB plumes over Australia. The results show good agreement between all products for plumes with AOT values ≤ 2. For extreme BB plumes, the ORAC retrieval finds values of AOT > 15, significantly higher than those seen in events classified as extreme by previous studies, although with high uncertainty. A combination of hard limits in the retrieval algorithms and misclassification of BB plumes as cloud prevents the JAXA and MODIS products from returning AOT values significantly greater than 5.

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.

An Air Quality Model That Is Evolving with the Times

The pioneering Sulfur Transport and Deposition Model, initially designed to simulate atmospheric sulfur, continues to find new applications and value in environmental science and policymaking.

Relaxation of Spring Festival Firework Regulations Leads to a Deterioration in Air Quality.

The relaxation of restrictions on Chinese Spring Festival (SF) firework displays in certain regions has raised concerns due to intensive emissions exacerbating air quality deterioration. To evaluate the impacts of fireworks on air quality, a comparative investigation was conducted in a city between 2022 (restricted fireworks) and 2023 SF (unrestricted), utilizing high time-resolution field observations of particle chemical components and air quality model simulations. We observed two severe PM2.5 pollution episodes primarily triggered by firework emissions and exacerbated by static meteorology (contributing approximately 30%) during 2023 SF, contrasting with its absence in 2022. During firework displays, freshly emitted particles containing more primary inorganics (such as chloride and metals like Al, Mg, and Ba), elemental carbon, and organic compounds (including polycyclic aromatic hydrocarbons) were predominant; subsequently, aged particles with more secondary components became prevalent and continued to worsen air quality. The primary emissions from fireworks constituted 54% of the observed high PM2.5 during the displays, contributing a peak hourly PM2.5 concentration of 188 μg/m3 and representing over 70% of the ambient PM2.5. This study underscores that caution should be exercised when igniting substantial fireworks under stable meteorological conditions, considering both the primary and potential secondary effects.

Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms

Abstract. The microprocessor without interlocked piped stages (MIPS) and LoongArch are reduced instruction set computing (RISC) processor architectures, which have advantages in terms of energy consumption and efficiency. There are few studies on the application of MIPS and LoongArch central processing units (CPUs) in geoscientific numerical models. In this study, the Loongson 3A4000 CPU platform with the MIPS64 architecture and the Loongson 3A6000 CPU platform with the LoongArch architecture were used to establish the runtime environment for the air quality modelling system Weather Research and Forecasting–Comprehensive Air Quality Model with extensions (WRF-CAMx) in the Beijing–Tianjin–Hebei region. The results show that the relative errors for the major species (NO2, SO2, O3, CO, PNO3, and PSO4) between the MIPS and X86 benchmark platforms are within ±0.1 %. The maximum mean absolute error (MAE) of major species ranged up to 10−2 ppbV or µg m−3, the maximum root mean square error (RMSE) ranged up to 10−1 ppbV or µg m−3, and the mean absolute percentage error (MAPE) remained within 0.5 %. The CAMx takes about 195 min on the Loongson 3A4000 CPU, 71 min on the Loongson 3A6000 CPU, and 66 min on the Intel Xeon E5-2697 v4 CPU, when simulating a 24 h case with four parallel processes using MPICH. As a result, the single-core computing capability of the Loongson 3A4000 CPU for the WRF-CAMx modelling system is about one-third of the Intel Xeon E5-2697 v4 CPU, and the one of Loongson 3A6000 CPU is slightly lower than that of Intel Xeon E5-2697 v4 CPU; but, the thermal design power (TDP) of Loongson 3A4000 is 40 W, while the TDP of Loongson 3A6000 is 38 W, only about one-fourth of that of Intel Xeon E5-2697 v4, whose TDP is 145 W. The results also verify the feasibility of cross-platform porting and the scientific usability of the ported model. This study provides a technical foundation for the porting and optimization of numerical models based on MIPS, LoongArch, or other RISC platforms.

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.

Significant impact of urban tree biogenic emissions on air quality estimated by a bottom-up inventory and chemistry transport modeling

Abstract. Biogenic volatile organic compounds (BVOCs) are emitted by vegetation and react with other compounds to form ozone and secondary organic matter (OM). In regional air quality models, biogenic emissions are often calculated using a plant functional type approach, which depends on the land use category. However, over cities, the land use is urban, so trees and their emissions are not represented. Here, we develop a bottom-up inventory of urban tree biogenic emissions in which the location of trees and their characteristics are derived from the tree database of the Paris city combined with allometric equations. Biogenic emissions are then computed for each tree based on their leaf dry biomass, tree-species-dependent emission factors, and activity factors representing the effects of light and temperature. Emissions are integrated in WRF-CHIMERE air quality simulations performed over June–July 2022. Over Paris city, the urban tree emissions have a significant impact on OM, inducing an average increase in the OM of about 5 %, reaching 14 % locally during the heatwaves. Ozone concentrations increase by 1.0 % on average and by 2.4 % during heatwaves, with a local increase of up to 6 %. The concentration increase remains spatially localized over Paris, extending to the Paris suburbs in the case of ozone during heatwaves. The inclusion of urban tree emissions improves the estimation of OM concentrations compared to in situ measurements, but they are still underestimated as trees are still missing from the inventory. OM concentrations are sensitive to terpene emissions, highlighting the importance of favoring urban tree species with low-terpene emissions.

Impact of air quality model settings for the evaluation of emission reduction strategies to curb air pollution

For air quality management, while numerical tools are mainly evaluated to assess their performances on absolute concentrations, this study assesses the impact of their settings on the robustness of model responses to emission reduction strategies for the main criteria pollutants. The effect of the spatial resolution and chemistry schemes is investigated. We show that whereas the spatial resolution is not a crucial setting (except for NO2), the chemistry scheme has more impact, particularly when assessing hourly values of the absolute potential of concentrations. The analysis of model responses under the various configurations triggered an analysis of the impact of using online models, like WRF-chem or WRF-CHIMERE, which accounts for the impact of aerosol concentrations on meteorology. This study informs the air quality modeling community on what extent some model settings can affect the expected model responses to emission changes. We suggest to not activate online effects when analyzing the effect of an emission reduction strategy to avoid any confusion in the interpretation of results even if an online simulation should represent better the reality.

Bayesian inference-based estimation of hourly primary and secondary organic carbon in suburban Hong Kong: multi-temporal-scale variations and evolution characteristics during PM2.5 episodes

Abstract. Observation-based data of primary and secondary organic carbon in ambient particulate matter (PM) are essential for model evaluation, climate and air quality research, health effect assessments, and mitigation policy development. Since there are no direct measurement tools available to quantify primary organic (POC) and secondary organic carbon (SOC) as separate quantities, their estimation relies on inference approaches using relevant measurable PM constituents. In this study, we measured hourly carbonaceous components and major ions in PM2.5 for a year and a half in suburban Hong Kong from July 2020 to December 2021. We differentiated POC and SOC using a novel Bayesian inference approach. The hourly POC and SOC data allowed us to examine temporal characteristics varying from diurnal and weekly patterns to seasonal variations, as well as their evolution characteristics during individual PM2.5 episodes. A total of 65 city-wide PM2.5 episodes were identified throughout the entire study period, with SOC contributions during individual episodes varying from 10 % to 66 %. In summertime typhoon episodes, elevated SOC levels were observed during daytime hours, and high temperature and NOx levels were identified as significant factors contributing to episodic SOC formation. Winter haze episodes exhibited high SOC levels, likely due to persistent influences from regional transport originating from the northern region to the sampling site. Enhanced SOC formation was observed with increase in the nocturnal NO3 radical (indicated by the surrogate quantity of [NO2][O3]) and under conditions characterized by high water content and strong acidity. These results suggest that both NO3 chemistry and acid-catalyzed aqueous-phase reactions likely make notable contributions to SOC formation during winter haze episodes. The methodology employed in this study for estimating POC and SOC provides practical guidance for other locations with similar monitoring capabilities in place. The availability of hourly POC and SOC data is invaluable for evaluating and improving atmospheric models, as well as understanding the evolution processes of PM pollution episodes. This, in turn, leads to more accurate model predictions and a better understanding of the contributing sources and processes.

MEIAT-CMAQ: A modular emission inventory allocation tool for Community Multiscale Air Quality Model

The Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model (MEIAT-CMAQ) refines emission inventories by providing detailed spatial (horizontal and vertical), temporal, and species allocations, enhancing the accuracy of CMAQ performance. Its efficient algorithm and modular design offer flexibility for managing both gridded and tabulated inventories, widely used in various sectors. In addition, the shapefiles with specific shapes supported by MEIAT-CMAQ can address the allocation challenges in transportation emissions. The evaluation of MEIAT-CMAQ, using model-ready inventories before (BASE scenario), and after allocation without (EXPR scenario) or with (EXPR-V scenario) vertical allocation, demonstrates significant improvements in the mean bias (MB) of gaseous pollutants (O₃, NO₂, CO). In both the EXPR and EXPR-V scenarios, the MB for O3 exhibits notable enhancements, with respective improvements of 5.7% and 26.9%. For NO₂, corresponding MB improvements are even more pronounced, reaching 27.6% and 61.7% in the EXPR and EXPR-V scenarios, respectively. Likewise, enhancements are observed in the MBs of CO, demonstrating increases of 8.4% and 45.2% in the EXPR and EXPR-V scenarios, respectively. Moreover, with regard to spatial accuracy, the incorporation of the MEIAT-CMAQ model yields significant improvements. Specifically, in the EXPR scenarios, spatial accuracy for O3 and NO2 demonstrates respective enhancements of 13.5% and 9.5%. Furthermore, the inclusion of vertical allocation leads to additional enhancements in CO, NO₂, and PM2.5, resulting in improvements of 17.6%, 16.6%, and 23.2%, respectively. MEIAT-CMAQ provides an efficient method for transforming coarse-resolution emission inventories into high-resolution files directly useable in the model, offering enhanced flexibility for users to select any period for generating model-ready emission files. This capability provides substantial technical support for automating processes within business departments and significantly improves the performance of high-resolution modeling and forecasting.

Research on CC-SSBLS Model-Based Air Quality Index Prediction

Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset’s data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model’s stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data’s outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m−3 and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results.

Machine Learning Classification of Air Quality Monitoring Stations to Achieve Ambient NO2 Objectives Using Emission Scenarios and Chemical Transport Model

Exceeding the latest Canadian Ambient Air Quality Standards (CAAQS) for NO2 concentration in Canadian cities motivates discussions on new policies and directives. Advanced air quality modeling and machine learning clustering algorithms integrating the Weather Research Forecast (WRF) and Community Multiscale Air Quality (CMAQ) models were used to classify air quality monitoring (AQM) stations based on pollution concentration modeling results. The sensitivity of ambient NO2 to the primary anthropogenic emission sources was investigated in Alberta, Canada. Emissions from two main sources, upstream oil and gas (UOG) and transportation sources, have been identified as the reason Alberta fails to meet the newly adopted NO2 CAAQS objectives. The air quality model was validated with ground-level observation data and the atmospheric model accurately replicates spatiotemporal NO2 variations. Despite contributing 62% of Alberta’s total NOx emissions, UOG influences ambient NO2 concentrations modestly in urban areas (<10%) but significantly affects rural regions. In contrast, transportation emission sources, responsible for 23% of NOx emissions, dominate ambient NO2 levels (up to 63%) in large cities. The discrepancy of emission contribution and ground-level concentrations, obtained from the chemical transport model, was resolved using a k-prototypes clustering algorithm to propose a new approach for categorizing AQM stations which led to improving the conventional classifications. The new approach considered the sensitivity of NO2 to emission reduction scenarios and provided an improved classification to be used for emission reduction interventions. Based on the updated classification, one set of AQM stations clearly showed sensitivity to NO2 emission reduction in the transportation sector despite their lower contributions to overall emissions. These stations were categorized as population exposure stations in large cities.