The increasing diurnal difference of nitrogen dioxide (NO2) has a non-negligible promoting effect on the formation of next-day ozone and fine particulate matter. Therefore, a thorough analysis of the driving mechanisms behind this diurnal variation is crucial for achieving precise pollution control. Here, we investigate the long-term evolution of NO2 diurnal differences and their meteorological contributions in the Pearl River Delta (PRD) from 2014 to 2023. Three primary clusters are identified using K-means clustering: Cluster 1 represents regional background concentrations, Cluster 2 shows significant nighttime accumulation with a large diurnal difference, and Cluster 3 shows all-day high NO2 concentrations. The increasing trend in diurnal differences was mainly driven by the rising number of days classified as Clusters 2 and 1. Anthropogenic emission contributions to NO2 concentrations in the PRD decreased by 34.2 % from 2014 to 2023, reflecting the effectiveness of emission reduction measures. However, meteorological impacts vary significantly across clusters. In Cluster 1, meteorological conditions reduced NO2 concentrations by 4.4 μg/m3-11.0 μg/m3; in Cluster 2, contributions were positive in central cities but negative in surrounding cities; and in Cluster 3, conditions increased NO2 concentrations by 9.9 μg/m3-22.0 μg/m3. Boundary layer height, temperature, and atmospheric pressure were identified as key meteorological drivers of NO2 diurnal variation, with their contributions varying significantly across clusters, between pollution and non-pollution days, and at different times of the day. This study highlights the critical role of specific meteorological patterns in amplifying NO2 diurnal variation.

Ship emissions are the main source of air pollution in coastal areas, prompting China to progressively implement the Domestic Emission Control Area (DECA) policy since 2016. In order to evaluate the effectiveness of this policy, we established a comprehensive framework combining high-resolution emission inventories, air quality modeling (WRF-CMAQ), observation-fusion (SMAT-CE), and health benefit assessments (BenMAP-CE) to quantify its impacts on Guangdong Province in 2022. The policy reduced SO2, NOx, and PM2.5 emissions from ships operating in Guangdong and its adjacent waters by 77.8%, 36.7%, and 72.0%, respectively. These reductions decrease the provincial annual mean PM2.5 by 0.37 μg/m3, while localized reductions in core port cities like Zhuhai reached 1.12 μg/m3. These reductions also induced seasonal variations in O3: widespread summer mitigation (-0.28 μg/m3) was largely counterbalanced by winter increases (+0.34 μg/m3) in the VOC-limited coastal core. Driven by the air quality improvements, the policy avoided approximately 1984 premature deaths. Health benefits were unevenly distributed, heavily favoring cities like Shenzhen due to significant air quality improvements and massive exposed populations. The fuel sulfur limit universally drove health benefits across all cities. In contrast, while NOx Tier standards and shore power adoption benefited peripheral coastal cities, their concentrated deployment in the PRD core weakened the local NO titration effect, triggering localized O3 increases under VOC-limited conditions. The continued implementation of DECA, coupled with the synergistic land-sea management strategies, will ultimately promote the simultaneous reduction of PM2.5 and O3, securing comprehensive air quality and health benefits. This study provides a scientific foundation for optimizing control strategies for sustainable coastal development.

Despite intensified mitigation efforts, ozone (O3) concentrations in the Pearl River Delta (PRD) remain volatile and prone to rebounding, posing persistent risks to public health and agricultural ecosystems. Using a 2023 baseline emission inventory and a 3% regional O3 reduction target grounded in recent volatility trends, this study constructed an optimization framework integrating a deep learning-based response surface model, a genetic algorithm, and marginal abatement cost curves to minimize total societal emission reduction costs. The optimal synergistic pathway requires average NOx and VOCs reductions of 18.9% and 20.0%, respectively, achieving a 3.6%-4.1% O3 decline at a total cost of 0.83 billion CNY, with a benefit-cost ratio (BCR) of 7.6. BCRs varied substantially across cities, ranging from 1.3 to 35.0, underscoring the need for phased, city-differentiated emission reduction policies. This pathway can avoid 1,196 premature deaths during the 2023 warm season (95% UI: 582-1943) and significantly improve spatial and socioeconomic equity, indicated by a 10% decrease in the O3 exposure Gini coefficient and a 21.4% reduction in the absolute value of the health burden concentration index. In addition, the optimal pathway reduces O3-induced crop yield losses and avoids more than 20 million CNY in direct agricultural economic losses. This study provides a quantitative scientific basis for precise and coordinated O3 control in the PRD and offers a transferable technical approach for air quality management in other key regions of China.

Air pollution imposes significant health and ecological damages. However, previous studies targeted on air quality improvement, focused on one of the two impacts independently of the other and fail to consider both health and ecological losses. This study establishes a dynamic multicity and multisector modeling framework by integrating emission inventories, future mitigation pathways, response surface models, and monetization methods of health and ecological benefits. Taking the Yangtze River Delta (YRD) in China as a case, we quantify the integrated benefits of air pollution control and propose coordinated emission reduction strategies of multiple species aiming at maximizing integrated benefits. Results show that from 2013 to 2020, emission reductions lowered annual economic losses by 709.5 billion CNY, with ecological benefits accounting for nearly half of health benefits. NH3 and NOx reductions were found to be particularly effective in achieving integrated benefits nowadays, with an optimal reduction ratio of approximately 0.75 across the YRD, and stronger NH3 mitigation needed in northeastern regions. Carbon neutrality pathways combined with targeted NH3 and VOCs controls yield greater equity, especially benefiting less-developed areas. On-road vehicles and agriculture are identified as key sectors for achieving overall health and ecological benefits. These findings provide a replicable model for multipollutant, multiobjective air quality management in rapidly industrializing regions worldwide.

Quantifying commercial cooking emissions is non-negligible for mitigating urban PM2.5 and O3 pollution, given their significant and spatiotemporally concentrated releases of PM2.5 and volatile organic compounds (VOCs). However, the accuracy of existing commercial cooking emission inventories remains unsatisfactory because generalized estimation parameters fail to represent the dynamic emission variations of this sector. Here, we develop a unified online-source framework (UOS) that simultaneously refines emission magnitudes and spatiotemporal allocations by integrating samplebased calibrated online oil fumes monitoring and point-of-interest data. Our framework corrected a 5.95-and 2.09-fold underestimation of VOCs and PM2.5 emissions in Guangdong Province in 2023 compared to the legacy version, in which hourly-scale emission factors significantly increased up to 159.13 g/h for VOCs and 52.47 g/h for PM2.5, respectively. Moreover, the spatial distribution was optimized to 70

Ground-level ozone (O3) remains a persistent air-quality challenge in large urbanized regions, driven by nonlinear photochemical interactions between nitrogen oxides (NOX) and volatile organic compounds (VOCs), as well as regional transport across administrative boundaries. In the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), differences in emission structures and inventory methodologies among jurisdictions, together with COVID-19-related activity disruptions in 2020, introduce substantial uncertainties into bottom-up precursor estimates and limit the reliability of O3 attribution and exposure assessment. In this study, a Polynomial Function based Response Surface Model (pf-RSM) was embedded in an emissions inversion framework to iteratively refine NOX and VOCs emissions in the GBA for 2017 and 2020. The framework produced inversion-based emission inventories that corrected systematic biases in bottom-up estimates, including a 2.9 × 104 t overestimation of NOX and a 6.1 × 104 t underestimation of VOCs in 2017. These corrections substantially improved O3 simulations, reducing normalized mean error by 19-24% and enhancing spatial agreement with observations. With the refined emission fields, O3 concentrations were quantitatively decomposed to local photochemical formation and regional transport, revealing a structural transition in the O3 formation regime: local production declined by 27-37% in inland manufacturing centers, whereas regional inflow accounted for 35-45% of total O3 in coastal and downwind cities. Emission and meteorological changes together prevented approximately 5600 premature deaths between 2017 and 2020, but substantial inter-city heterogeneity was observed, with changes in population size and structure offsetting up to 60% of the achievable health gains in several regions. Cities heavily influenced by transported pollution from upwind industrial areas experienced limited improvements despite local emission controls. Taken together, these results demonstrate that the pf-RSM approach provides a scalable, integrated framework for refining emissions, diagnosing O3 formation mechanisms, and quantifying city-specific exposure and health outcomes, thereby supporting equitable, population-focused O3 mitigation in large coastal urban regions.

Identifying noise sources in exceedance-triggered audio is essential for targeted source tracing and sustainable urban social noise governance. While accurate models require massive labeled data, the acoustic complexity, high redundancy, and imbalanced class distributions of real-world recordings incur prohibitive manual annotation costs, hindering their widespread application in IoT networks. To tackle this bottleneck, we present a label-efficient active learning framework designed to minimize annotation costs by dynamically selecting the most valuable audio samples. Specifically, rather than treating uncertainty, class balance, and diversity as separate query criteria, it encodes uncertainty and dynamic class-aware learning needs into a weighted acoustic feature space, so that diversity-based selection can be performed in a unified manner. Experiments on the UrbanSound8K benchmark and a realistic exceedance-triggered monitoring dataset demonstrate consistent label-efficiency advantages over mainstream methods. Notably, our approach reaches 98% of the fully supervised upper bound on the real-world dataset while reducing the training annotation workload by 85.0% compared to random sampling. On the real-world dataset, the proposed framework yields higher F1-scores for several challenging under-represented categories and reduces the misclassification of dominant sound events relevant to social noise source tracing. Furthermore, cross-site generalization experiments reveal rapid localized adaptation to new monitoring environments, reaching the fully supervised upper bound with only 13% of the target-domain training data. Overall, this study provides a scalable and cost-effective classification framework for urban noise monitoring, offering practical support for noise regulatory authorities and city managers in more targeted noise source tracing and governance.

The global shift from air quality attainment to mitigating the health impacts of air pollution, exemplified by the 2025 World Health Assembly’s goal to halve pollution-related deaths by 2040, demands a new generation of policy tools. However, existing frameworks remain rooted in static assessments of population vulnerability, despite rapid demographic shifts and socioeconomic transformations. Here, we introduce a Dynamic Response Surface Model (DRSM) that integrates time-varying age structures, nonlinear atmospheric chemistry, and interregional pollution transport into a unified decision architecture to formulate a health-oriented air pollution control strategy. Applying the DRSM to China, it reveals that current policies fail to curb rising health burdens in most provinces, despite ongoing reductions in anthropogenic emissions. Population vulnerability will increasingly offset the health returns of emission reductions and exacerbate inter-provincial inequities until approximately 2060. To halt the deterioration of air pollution-related mortality, annual national emission reduction of at least 2.7% for PM2.5 precursors and 5.8% for ozone precursors is required. A health-optimized mitigation pathway for 2035, shifting control priorities from traditional megacity clusters toward central China, demonstrates a highly favorable benefit-cost ratio. This strategic reallocation not only improves cost-effectiveness but also corrects long-standing spatial imbalances in environmental policy. The DRSM establishes a new paradigm for air quality governance that transcends ambient pollutant metrics, redefining policy success by its capacity to deliver cost-effective and equitable improvements in global health and longevity.

High-resolution emission inventories (EIs) are critical for policymakers to develop air pollution mitigation strategies. However, traditional EIs are often outdated due to data lag. Here, we develop a novel framework for high-resolution EI inversion based on response surface model. Application to a continuous pollution episode in the Pearl River Delta (PRD) demonstrates that the posterior emissions significantly improve the simulation accuracy of NO2 and O3, revealing new spatiotemporal distribution patterns of nitrogen oxides (NOx) and O3 sensitivity regimes. The posterior NOx emissions decreased by 12.5 % compared to the prior emissions, with emissions primarily concentrated during the daytime and significantly reduced during nighttime. Besides, emissions decreased in central cities while increasing in peripheral cities, reflecting the notable impact of policies such as transportation electrification and industrial relocation. And changes in emissions notably impacted the spatial distribution of pollutants and atmospheric oxidation capacity, with the VOC-limited regime during the daytime expanding from central cities to surrounding areas. Finally, we uncover that Red Alert measures are insufficient to bring daily maximum 8-h average (MDA8) O3 concentrations into compliance during extreme weather in the PRD. The framework developed in this study provides policymakers with a robust tool to more effectively and promptly understand local emissions and implement targeted control strategies.

Transportation control measures are widely promoted as a means of simultaneously reducing CO2 and air pollutant emissions, yet their synergistic effects remain insufficiently quantified. In this study, we develop an integrated framework that couples air quality modeling with health impact assessment to quantify the air pollution-related health and CO2 mitigation benefits of real-world transportation control measures implemented in Guangdong, China. We introduced a synergy index (SynI) to capture both the magnitude and balance of health and CO2 mitigation benefits, whereas a coordination degree (CD) captures only their relative balance. The results show that in 2030, transportation control measures are estimated to avoid 1,730 (95% confidence interval (CI): 1,482-2,001) PM2.5-, 5,364 (95% CI: 2,722-10,425) NO2- and 86 (95% CI: 45-128) O3-related premature deaths, while reducing annual CO2 emissions by 39.46 Mt yr-1. Spatially, densely populated and traffic-intensive central cities achieve the highest SynI but a lower CD, as nonlinear photochemical responses drive O3 increases that partially offset the health benefits from PM2.5 reductions in our simulations. In contrast, peripheral cities exhibit a higher CD but smaller absolute benefits. Among individual measures, improving energy efficiency is estimated to achieve the highest SynI in 18 of 21 cities but ranks poorly in CD, whereas eliminating high-emission vehicles shows the highest CD across most cities but limited SynI owing to smaller emission reductions. The energy transition performs best in Foshan, Dongguan, and Zhuhai, achieving both a high SynI and CD. This study reveals spatial variations in synergy levels and measure priorities, providing scientific support for region-specific strategies to jointly mitigate air pollution and CO2 emissions in regions undergoing rapid transportation transformation.

During the 14th Five-Year Plan (2021–2025), the Pearl River Delta (PRD) faces the dual challenges of a slowdown in PM2.5 emission reductions and persistent fluctuations in ozone (O3), raising concerns about the effectiveness and equity of current control strategies. To address this, we developed an integrated assessment framework that combines 2025 Business-as-Usual (BAU) and Policy Scenarios (PS) with the Response Surface Model (RSM) to systematically quantify the non-linear responses of PM2.5 and O3 to specific interventions, and translated these changes into health benefits and environmental justice outcomes. Our results indicated that compared with BAU, the PS reduced precursor emissions by 14–28

Climate change and air pollution control are two urgent global challenges that demand effective solutions. Cities are the fundamental units for implementing control policies of air pollutants and carbon dioxide emissions. However, research on optimized city-level pathways that maximize integrated benefits and synergies of air pollution and carbon reduction remains limited. Here, we develop a decision-making model for coordinated control of air pollutants and carbon dioxide at the city level. The model systematically evaluates the air quality-related benefits, carbon reduction benefits, and their synergies across various emission reduction measures, and uses these evaluations to construct optimized emission reduction scenario under joint air quality and carbon targets. The model is applied to Beijing, Shanghai, and Chengdu: three megacities with populations above 10 million but distinct differences in city functions, industrial structure, and resource endowments. Results show that under enhanced policy regulation, all three cities can achieve national strategy-compliant air quality improvement and carbon reduction. Structural adjustments in energy, industry, and transportation are central to all cities, but priorities vary. Beijing relies on electric vehicles and imported green power; Shanghai focuses on local green power and transportation electrification; Chengdu emphasizes dust control and promoting clean power. Across all cases, monetized benefits exceed costs, though the benefit-to-cost ratio decreases with tightened environmental targets. This research provides methodological tools for improving environmental quality and promoting low-carbon development at the city level, and offers practical references for formulating region-specific policies tailored to local conditions.

As the precursor of ozone (O3), volatile organic compounds (VOCs) largely derive from biogenic sources. However, future global changes in climate and land cover may profoundly affect O3 by enhancing biogenic VOC (BVOC) emissions, posing new challenges to public health. In this study, we developed a coupled modeling framework under Shared Socioeconomic Pathways (SSP1-2.6 and SSP2-4.5), integrating chemical transport model and health impact assessment model to assess the impacts of climate and land-use-driven BVOC emissions on O3 concentrations and human health in Guangdong Province with one of the highest BVOC emissions in China. Results indicate that BVOC emissions are projected to increase by 18.8

Solar photovoltaic power is essential for achieving carbon neutrality, yet its output is highly sensitive to changes in air quality under future emission pathways. We integrate energy–economy, coupled meteorology–chemistry, and photovoltaic performance models to quantify how alternative carbon-neutrality pathways influence solar power generation via air quality changes in China. Here we show that a pathway emphasizing renewable-energy deployment yields the greatest air-quality improvements, boosting annual photovoltaic power generation by 57,526 ± 10,314 gigawatt-hours (GWh) relative to business-as-usual, equivalent to economic gains of US$5.18 ± 0.93 billion by 2060. These gains are driven primarily by aerosol–cloud interactions rather than aerosol–radiation interactions. In contrast, pathways relying on biomass or carbon capture achieve only about one-third of these gains. The largest gains occur in eastern and southern China, where electricity demand is highest. These findings highlight the importance of co-optimizing decarbonization and air-pollution mitigation to maximize the renewable energy benefits of carbon-neutrality strategies. This study reveals that the choice of carbon-neutrality pathway can determine the future solar energy gains from air-quality improvements. Renewable-focused strategies deliver the largest photovoltaic benefits, with aerosol–cloud interactions driving most of the enhancement.

Ultrafine particles (UFPs) pose elevated health risks, yet high-resolution exposure models remain limited in rapidly urbanizing megacities of developing countries. This study developed a seasonal land-use regression (LUR) model for UFPs in Guangzhou, China, using a dense monitoring network of 96 sites to capture spatial and temporal variations. Particle number concentrations (30-120 nm) were measured across dry and wet seasons (2021-2022) using portable monitors, with predictor variables derived from transportation networks, land use, socioeconomic factors, emission inventories, and points of interest across buffer zones (100-1000 m). A supervised forward stepwise regression identified key predictors, including trunk road length within 100 m, restaurant density within 100 m, industrial land area, and green spaces. The final model explained 76 % of spatial variability (adjusted R2 = 0.76, RMSE = 2784 particles/cm3), validated by leave-one-out cross-validation (LOOCV R2 = 0.69). Results highlighted vehicular emissions and cooking activities as dominant contributors, with trunk roads and restaurants explaining 52.4 % of variability. Seasonal analysis revealed higher UFPs concentrations during the dry season, linked to lower humidity and reduced dispersion. The 1-km resolution exposure map identified urban hotspots near major roads and commercial zones. This study establishes the first high-resolution LUR model for UFPs in the Pearl River Delta region, offering a transferable framework for exposure assessment in megacities and supporting targeted mitigation strategies and epidemiological research on UFPs-related health impacts.

Wind and solar power are widely regarded as key pillars of clean transition owing to their promising development potential. Systematically evaluating the environmental benefits of wind and solar power deployments is essential as it can enhance their social acceptance and support sustainable development. In this study, we first evaluated emissions reduction driven by wind and solar power deployments in 2022 in Guangdong Province, China's most populous province with the highest electricity demand. Then, with atmospheric transport model, we assessed impacts on air quality and CO2 concentrations due to emissions reduction. Finally, we monetized environmental co-benefits by quantifying health benefits and carbon reduction benefits. Our results indicated wind and solar power deployments significantly reduced air pollutants and CO2 emission from the power sector, with the extent of reductions jointly determined by regional renewable generation and the emission intensity of displaced thermal power. Emissions reduction contributed to improved air quality and lower CO2 concentrations, with provincial average CAQI and surface-level CO2 concentrations decreasing by 0.84 % and 0.53 ppmv, respectively. PM2.5 and O3 pollution mitigation avoided 388 (95 % CI 290-485) premature deaths, delivering $268 million health benefits. In parallel, CO2 emissions were reduced by 2.53 × 104 kt, yielding $141 million carbon reduction benefits. Altogether, these environmental co-benefits were equivalent to $12.07/MWh, covering 16-45 % of the LCOE for wind and solar power, which underlined the out-of-market societal benefits. This study offered valuable insights for policymakers to aim at optimizing clean transition policies for achieving integrated environmental, health, and climate objectives.

Understanding the characteristics of O3 precursor contributions over multiple years is crucial for designing effective O3 control strategies over the Pearl River Delta (PRD) region of China. In this study, a deep learning-based response surface model (DeepRSM) was developed and applied over the PRD (DeepRSM-PRD) to identify and quantify the main features of O3 regimes and regional contributions in the core PRD over multiple years (2019–2021). The Out-of-Sample (OOS) validation results indicated that DeepRSM-PRD effectively predicted the nonlinear response of O3 to emission controls, maintaining validity across non-training periods. Our study revealed that O3 generation was sensitive to volatile organic compounds (VOC) in the core PRD in 2019, with nitrogen oxides (NOx)-limited regimes emerging in most major cities in 2020 and 2021. Further investigation into source contributions showed that in our model domain, O3 formation in central cities of the PRD was primarily driven by local contributions and was susceptible to influence from nearby cities. With small emission reductions, VOC contributions predominantly drive O3 production in Guangzhou and Shenzhen. However, NOx emissions were identified as the primary contributors in all central city receptors when anthropogenic emissions were removed, sharing 59.5

Cooking emissions are a significant source of PM2.5, posing considerable public health risks due to their high toxicity and proximity to densely populated areas. Despite their importance, there is currently a lack of an accurate, long-term, high-resolution national cooking emission inventory in China, primarily due to the challenges of obtaining high-quality activity-level data over extended periods at fine spatial scales. Here, we address these limitations by leveraging advanced machine learning techniques to predict activity levels and further estimate emissions. Specifically, we develop an ensemble model of machine learning algorithms - random forest (RF), eXtreme gradient boosting (XGBoost), multilayer perceptron neural network (MLP), and deep neural networks (DNNs) - to accurately predict cooking activity levels across Chinese counties based on statistical indicators related to population, economy, and the catering industry. The ensemble machine learning model demonstrates exceptional generalization and transferability (R-2= 0.892-0.989), outperforming traditional statistical models and individual machine learning models. Unlike previous inventories that rely on simplistic proxy data such as population for calculation and downscaling, our inventory precisely calculates county-level cooking emissions, providing more accurate emission estimates and spatial distributions. Furthermore, we incorporate critical but previously missing toxic pollutants, such as ultrafine particles (UFPs) and polycyclic aromatic hydrocarbons (PAHs), into the national cooking emission inventory. Therefore, we develop China's first county-level cooking emission inventory, spanning 1990 to 2021, with high spatial resolution and wide pollutant coverage. According to our inventory, in 2021, China's total cooking emissions of organics in the full volatility range, PM2.5, UFPs, and PAHs are 997, 408 kt, 6.50 x 10(25) particles, and 15.8 kt, respectively. From 1990 to 2021, emissions of these pollutants increased by over 65 %, and their spatiotemporal trends were affected to varying degrees by external factors, such as population migration, economic development, pollution control policies, and the pandemic in different periods. We further analyze the contribution patterns of key driving factors, such as urbanization rate, population, and pollution control, to emission changes. Notably, driver analysis reveals that existing control measures are insufficient to curb the rapid growth of emissions, necessitating enhanced controls. Regarding control strategies, our county-level inventory finds that 62.3 % of China's organic emissions are concentrated in 30 % of the counties, which are densely populated and occupy only 14.4 % of the national land area. Therefore, prioritizing control of these areas will be an efficient and targeted strategy. Our research provides crucial data and insights for understanding the impact of cooking emissions on air pollution and health, aiding in policy development. Our long-term, high-resolution emission datasets are publicly available at https://doi.org/10.6084/m9.figshare.26085487 (Li et al., 2025).

Optimizing an emergency air pollution control strategy for haze events presents a significant challenge due to the extensive computational demands required to quantify the complex nonlinearity associated with controls on diverse air pollutants and regional sources. In this study, we developed a forecasting tool for emergency air pollution control strategies based on a predictive response surface model that quantifies PM2.5 responses to emission changes from different pollutants and regions. This tool is equipped to assess the effectiveness of emergency control measures corresponding to various air pollution alerts and to formulate an optimized control strategy aimed at specific PM2.5 targets. A case study in the Yangtze River Delta demonstrates that our tool can conduct assessments and generate optimized control strategies for the forthcoming seven to ten days within a 6-h window. Results indicate that the haze event on November 3rd, 2017, was predominantly attributable to regional transport, while the episode on November 7th-8th resulted more from local emissions. The optimized control strategy for November 3rd involves coordinated control from 17 cities along the northwest regional transport pathway, whereas 9 cities around Shanghai should implement emergency emission reductions for PM2.5 attainment in Shanghai on November 7th-8th. Additionally, the intensity of air pollution alerts is higher in the optimized strategy for November 3rd. The forecasting tool developed in this study can quickly and accurately assess the effectiveness of pollution emergency reduction plans and formulate optimal control strategies in advance, which is of great significance for enhancing the emergency response capabilities of authorities to address short-term air pollution events effectively.