Data assimilation of ambient concentrations of multiple air pollutants using an emission-concentration response modeling framework

High-spatiotemporal-resolution mapping of PM2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator

Traffic sources are a major contributor to fine particulate matter (PM2.5) pollution, with their emissions and diffusion exhibiting complex spatiotemporal patterns. Receptor models have limitations in estimating high-resolution source contributions due to insufficient observation networks of PM2.5 compositions. This study developed a source apportionment method that integrates machine learning and emission-based integrated mobile source indicator (IMSI) to rapidly and accurately estimate PM2.5 traffic source impacts with high spatiotemporal resolution in the Beijing-Tianjin-Hebei region. Firstly, we utilized multisource data and developed various machine learning models to optimize the traffic-related pollutant concentration fields simulated by a chemical transport model. Results demonstrated that the Extreme Gradient Boosting (XGBoost) model exhibited excellent prediction accuracy of nitrogen oxide (NO2), carbon oxide (CO), and elemental carbon (EC), with the cross-validated R values increasing to 0.87-0.92 and error indices decreasing by 50-67%. Furthermore, we estimated and predicted daily mappings of PM2.5 traffic source impacts using the IMSI method based on optimized concentration fields, which improved spatially resolved source contributions to PM2.5. Our findings reveal that PM2.5 traffic source impacts display significant spatial heterogeneity, and these hotspots can be precisely identified during the pollution processes with sharp changes. The evaluation results indicated that there is a good correlation (R of 0.79) between PM2.5 traffic source impacts by IMSI method and traffic source contributions apportioned by a receptor model at Beijing site. Our study provides deeper insights of estimating the spatiotemporal distribution of PM2.5 source-specific impacts especially in regions without PM2.5 compositions, which can provide more complete and timely guidance to implement precise air pollution management strategies.

Summertime Urban Ammonia Emissions May Be Substantially Underestimated in Beijing, China

Ammonia (NH3) is critical to the nitrogen cycle and PM2.5 formation, yet a great deal of uncertainty exists in its urban emission quantifications. Model-underestimated NH3 concentrations have been reported for cities, yet few studies have provided an explanation. Here, we explore reasons for severe WRF-Chem model underestimations of NH3 concentrations in Beijing in August 2018, including simulated gas-particle partitioning, meteorology, regional transport, and emissions, using spatially refined (3 km resolution) NH3 emission estimates in the agricultural sector for Beijing-Tianjin-Hebei and in the traffic sector for Beijing. We find that simulated NH3 concentrations are significantly lower than ground-based and satellite observations during August in Beijing, while wintertime underestimations are much more moderate. Further analyses and sensitivity experiments show that such discrepancies cannot be attributed to factors other than biases in NH3 emissions. Using site measurements as constraints, we estimate that both agricultural and non-agricultural NH3 emission totals in Beijing shall increase by ∼5 times to match the observations. Future research should be performed to allocate underestimations to urban fertilizer, power, traffic, or residential sources. Dense and regular urban NH3 observations are necessary to constrain and validate bottom-up inventories and NHx simulation.

Accurate prediction of air quality response to emissions for effective control policy design

Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O₃ and PM_2.5 and the emissions of their precursors. However, the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O₃ and PM_2.5, which are formed by multiple precursors under complex physical and chemical processes; this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions. Different from the traditional sensitivity analysis and source apportionment methods, the reduced form models (RFMs) based on chemical transport models (CTMs) are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost. Here we review recent approaches used in RFMs and compare their structures, advantages and disadvantages, performance and applications. In general, RFMs are classified into three types including (1) sensitivity-based models, (2) models with simplified chemistry and physical processes, and (3) statistical models, with considerable differences in principles, characteristics and application ranges. The prediction of nonlinear responses by RFMs enables more in-depth analysis, not only in terms of real-time prediction of concentrations and quantification of human exposure, health impacts and economic damage, but also in optimizing control policies. Notably, data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management. In future studies, improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses. Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes, which pose great challenges to the quantification of response relationships. Additionally, with rising requirements for fine-scale air quality management, improving the performance of urban-scale simulations is worth considering. In short, accurate predictions of the response of air quality to emissions, though challenging, holds great promise for the present as well as for future scenarios.

Mimicking atmospheric photochemical modelling with a deep neural network

Fast and accurate prediction of ambient ozone (O₃) formed from atmospheric photochemical processes is crucial for designing effective O₃ pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O₃ prediction and policy design, however, existing CTM-based approaches are computationally expensive, and resource burdens limit their usage and effectiveness in air quality management. Here we proposed a novel method (noted as DeepCTM) that using deep learning to mimic CTM simulations to improve the computational efficiency of photochemical modeling. The well-trained DeepCTM successfully reproduces CTM-simulated O₃ concentration using input features of precursor emissions, meteorological factors, and initial conditions. The advantage of the DeepCTM is its high efficiency in identifying the dominant contributors to O₃ formation and quantifying the O₃ response to variations in emissions and meteorology. The emission-meteorology-concentration linkages implied by the DeepCTM are consistent with known mechanisms of atmospheric chemistry, indicating that the DeepCTM is also scientifically reasonable. The DeepCTM application in China suggests that O₃ concentrations are strongly influenced by the initialized O₃ concentration, as well as emission and meteorological factors during daytime when O₃ is formed photochemically. The variation of meteorological factors such as short-wave radiation can also significantly modulate the O₃ chemistry. The DeepCTM developed in this study exhibits great potential for efficiently representing the complex atmospheric system and can provide policymakers with urgently needed information for designing effective control strategies to mitigate O₃ pollution.

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.

Effects of exposure to ambient air pollution on pulmonary function impairment in South Korea: Korea National Health and Nutritional Examination Survey

OBJECTIVES

This study aimed to investigate the association between pulmonary function and air pollution using 2007–2017 data from the Korea National Health and Nutrition Examination Survey, a nationwide cross-sectional representative survey.

METHODS

A total of 27,378 participants that had sampling weights from a complex sample survey were included in this study. Using the data for forced expiratory volume in 1 second and forced vital capacity, the participants with pulmonary function impairment were classified according to the criteria of restrictive lung disease and chronic obstructive pulmonary disease (COPD). Exposure to ambient air pollution was estimated using the Community Multiscale Air Quality model. Multivariate linear and logistic regression analyses with complex samples were used to determine the associations between pulmonary function and air pollution after adjusting for covariates.

RESULTS

In total, 13.2% of the participants aged >40 years had COPD, and 10.7% were classified as being in the restrictive lung disease group. According to the multivariate logistic regression model, the odds ratios for the fourth quartiles of particulate matter less than 10 μm in diameter (PM₁₀), particulate matter less than 2.5 μm in diameter (PM_2.5) and carbon monoxide (CO) with a 2-year lag period were 1.203 (95% confidence interval [CI], 1.036 to 1.396), 1.283 (95% CI, 1.101 to1.495), and 1.292 (95% CI, 1.110 to 1.504), respectively, using the restrictive lung disease group as an event after adjusting for covariates in the complex sample.

CONCLUSIONS

Long-term exposure to PM₁₀, PM_2.5, and CO was significantly associated with pulmonary function, especially restrictive lung disease.