Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns

A robust black carbon prediction model derived from observational datasets in the Yangtze River Delta region, China

Black carbon (BC) is a short-lived pollutant with significant environment and human health impacts. Monitoring BC is important, but its spatial coverage is limited. Therefore, predicting BC concentration is crucial in densely populated regions like the Yangtze River Delta (YRD). This study explores the machine learning (ML) models, including IAP, LASSO, RF, and SNN, to develop a robust BC prediction model for the YRD, based on BC behavior at the Dianshan Lake (DSL) site. The annual BC concentration at DSL was 1.37 μg/m3, mainly from liquid fuel combustion (68 %), biomass burning (16 %), and coal combustion contributing (16 %). The ML model was first trained with DSL data (June 2021-June 2022) and then applied to other YRD sites in four scenarios based on air mass trajectories. The SNN model had R2 values over 0.80 for all scenarios at the DSL site. The model performed better during peak traffic periods, highlighting traffic's impact on BC levels. These models were applied to the YRD sites with R2 from 0.45 to 0.84. Hangzhou (HZ, 0.75-0.81), Nanjing (NJ, 0.78-0.80), and Pudong (PD, 0.76-0.84) showed the highest results. This suggests the model effectively captures BC concentrations from sources with similar emissions and meteorological conditions.

Research on the influencing factors of PM2.5 in China at different spatial scales based on machine learning algorithm

PM2.5 pollution is one of the prominent environmental issues currently faced in China, influenced by various factors and showed significant spatial differences. In this study, the Light Gradient Boosting Machine (LightGBM) model was employed in combination with SHapley Additive exPlanation (SHAP) methods to explore the key impact factors (precursor emissions, meteorological conditions, geographical features and socioeconomic factors) on average annual PM2.5 levels from 2015 to 2022 at both city and grid levels in China. The results show that incorporating pollutant concentration into the model enhances its performance, with R2 improving significantly from 0.79 to 0.93, which underscores the importance of pollutant concentration and the outstanding predictive performance of the LightGBM algorithm. Further, after increasing the spatial resolution and applying a grid-level model, R2 further improves to 0.96 ∼ 0.99. SHAP analysis revealed that PM2.5 levels in urban areas are significantly influenced by pollutant concentration such as NO2, CO, and SO2, accounting for 49.3 % of the total impact. In contrast, the grid-based model highlights the dominant role of meteorological factors such as temperature and precipitation influencing PM2.5 levels in non-urban areas. Moreover, the model results also suggested that the PM2.5 pollution in Yangtze River Delta (YRD) and Pearl River Delta (PRD) are mainly controlled by primary emissions, while in Beijing-Tianjin-Hebei (BTH), Fenwei Plain (FWP) and Sichuan Basin (SCB), atmospheric oxidation capacity is a limiting factor. This study underscores the potential of machine learning in atmospheric pollution control and offers insights for developing more effective and region-specific PM2.5 control policies.

Widespread surface ozone reduction triggered by dust storm disturbance on ozone production and destruction chemistry

Natural dust storms are associated with changes to atmospheric photochemical processes, including changes in surface ozone, a critical global air pollutant. Here, we quantified the change in surface ozone during dust storms for regions in China by using a synthesis of measurements and modeling approaches. Our results showed that notable reductions of the average ozone concentration (2.0 to 12.2 parts per billion by volume) were observed during the 12 dust storm events from 2016 to 2023, relative to predust storm levels. The chemical interactions of dust particles with ozone production processes played crucial roles in explaining approximately 13 to 35% of the observed ozone reduction, alongside the impact of intense meteorological disturbances on transport and formation of ozone. Among these interactions, the uptake of ozone, reactive nitrogen, and hydroperoxyl radical by dust particles could substantially contribute to the ozone suppression. This study highlighted the importance of interactions between severe dust pollution and atmospheric photochemistry.

Impact of Reduced Anthropogenic Emissions Associated With COVID-19 Lockdown on PM2.5 Concentration and Canopy Urban Heat Island in Canada

Extensive lockdowns during the COVID-19 pandemic caused a remarkable decline in human activities that have influenced urban climate, especially air quality and urban heat islands. However, the impact of such changes on local climate based on long term ground-level observations has hitherto not been investigated. Using air pollution measurements for the four major Canadian metropolitan areas (Toronto, Montreal, Vancouver, and Calgary), we find that PM2.5 markedly decreased during and after lockdowns with peak reduction ranging between 42% and 53% relative to the 2000-2019 reference period. Moreover, we show a substantial decline in canopy urban heat island intensity during lockdown and in the post lockdowns periods with peak reduction ranging between 0.7°C and 1.6°C in comparison with the 20-year preceding period. The results of this study may provide insights for local policymakers to define the regulation strategies to facilitate air quality improvement in urban areas.

Global urban activity changes from COVID-19 physical distancing restrictions

During the COVID-19 pandemic changes in human activity became widespread through official policies and organically in response to the virus's transmission, which in turn, impacted the environment and the economy. The pandemic has been described as a natural experiment that tested how social and economic disruptions impacted different components of the global Earth System. To move this beyond hypotheses, locally-resolved, globally-available measures of how, where, and when human activity changed are critically needed. Here we use satellite-derived nighttime lights to quantify and map daily changes in human activity that are atypical for each urban area globally for two years after the onset of the pandemic using machine learning anomaly detectors. Metrics characterizing changes in lights from pre-COVID baseline in human settlements and quality assurance measures are reported. This dataset, TRacking Anomalous COVID-19 induced changEs in NTL (TRACE-NTL), is the first to resolve COVID-19 disruptions for all metropolitan regions globally, daily. It is suitable to support a variety of post-pandemic studies that assess how changes in human activity impact environmental systems.

Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends

Ultrafine particles (UFPs) pose a significant health risk, making comprehensive assessment essential. The influence of emission sources on particle concentrations is not only constrained by meteorological conditions but often intertwined with them, making it challenging to separate these effects. This study utilized valuable long-term particle number and size distribution (PNSD) data from 2018 to 2023 to develop a tree-based machine learning model enhanced with an interpretable component, incorporating temporal markers to characterize background or time series residuals. Our results demonstrated that, differing from PM2.5, which is significantly shaped by planetary boundary layer height, wind speed plays a crucial role in determining the particle number concentration (PNC), showing strong regional specificity. Furthermore, we systematically identified and analyzed anthropogenically influenced periodic trends. Notably, while Aitken mode observations are initially linked to traffic-related peaks, both Aitken and nucleation modes contribute to concentration peaks during rush hour periods on short-term impacts after deweather adjustment. Pollutant baseline concentrations are largely driven by human activities, with meteorological factors modulating their variability, and the secondary formation of UFPs is likely reflected in temporal residuals. This study provides a flexible framework for isolating meteorological effects, allowing more accurate assessment of anthropogenic impacts and targeted management strategies for UFP and PNC.

Ground ozone rise during the 2022 Shanghai lockdown caused by the unfavorable emission reduction ratio of nitrogen oxides and volatile organic compounds

Ground-level ozone (O3) pollution has shifted from a scientific issue to a key focus of governmental action in China. In recent years, the concentration of NO2 in Shanghai has shown a decreasing trend of 3.7% annually, but ozone concentrations have exhibited significant interannual variability, particularly with a noticeable increase in 2022. This study focuses on investigating the mechanisms behind the increase in ozone concentration during the COVID-19 pandemic control period in 2022 in Shanghai, utilizing a combination of ground observation data, observation-based models, and chemical transport models for analysis. The results indicate that during the lockdown period, the MDA8 in Shanghai increased by 17 μg/m3 compared to before, with emission-related factors contributing 65.3%, primarily due to a blanket reduction in VOCs and NOx emissions during the lockdown, with a reduction ratio close to 1:1. However, this reduction ratio and intensity are not sufficiently reasonable to alleviate ozone pollution. Meanwhile, adverse meteorological conditions further exacerbated this effect, contributing 34.7%, with temperature rise having the greatest impact. Results from the chemical transport model show that with the total reduction in NOx and VOCs emissions unchanged, the greater the reduction in VOCs emissions, the better the reduction effect on ozone pollution, reducing MDA8 O3 by approximately 10 μg/m3, especially for the control of reactive compounds such as alkenes, aromatics, and OVOCs. However, if the reduction ratio of NOx is greater than that of VOCs, ozone concentrations may not decrease but instead increase. This indicates that ozone concentration is influenced not only by the intensity of emissions reduction but also by the ratio of emissions reduction between NOx and VOCs. Our study emphasizes the critical role of carefully designed strategies, focusing on controlling the ratio of VOCs to NOx and increasing the intensity of VOCs reduction, to effectively alleviate ozone pollution in urban areas.

Predictions of the Optical Properties of Brown Carbon Aerosol by Machine Learning with Typical Chromophores

The linkages between BrC optical properties and chemical composition remain inadequately understood, with quantified chromophores explaining less than 25% of ambient aerosol light absorption. This study characterized 38 typical chromophores in aerosols collected in Xi'an, with light absorption contributions to BrC ranging from 1.6 ± 0.3 to 5.8 ± 2.6% at 365 nm. Based on these quantified chromophores, an interpretable machine learning model and the Shapley Additive Explanation (SHAP) method were employed to explore the relationships between BrC optical properties and chemical composition. The model attained high accuracy with Pearson correlation coefficients (r) exceeding 0.93 for the absorption coefficient (Absλ) and surpassing 0.57 for mass absorption efficiency (MAEλ) of BrC. It explains more than 80% of the variance in Abs and over 50% in MAE, significantly improving the understanding of BrC light absorption. Polycyclic aromatic hydrocarbons (PAHs) and oxygenated PAHs (OPAHs) with four and five rings exhibit significant positive effects on Absλ, suggesting that similar unidentified chromophores may also notably impact BrC optical characteristics. The model based on chromophore mass concentrations further simplifies studying BrC optical characteristics. This study advances understanding of the relationship between BrC composition and optical properties and guides the investigation of unrecognized chromophores.

Distinct responses of urban and rural O3 pollution with secondary particle changes to anthropogenic emission reductions: Insights from a case study over North China

Ozone (O3) pollution with excessive near-surface O3 levels has been an important environmental issue in China, although the anthropogenic emission reductions (AER) have improved air quality since 2013. In this study, we investigated the sensitivities of atmospheric chemical environment with the urban and rural changes to the AER targeting a typical O3 pollution episode over North China in summer 2019, by conducting two WRF-Chem simulation experiments under two scenarios of anthropogenic emission inventories of years 2012 and 2019 with the meteorological conditions in the 2019 summertime O3 pollution episode for excluding the meteorological impacts on O3 pollution. The results show that the unbalanced AER aroused more serious O3 pollution in urban and rural areas. The intense NO reduction was responsible for the significant increments of urban O3, while the falling NO2 and NO synergistically devoted to the slight O3 variations in rural areas. Induced by the recent-year AER, the urban O3 production was governed by VOCs-limited and transition regime, whereas the NOx-limited regime dominated over rural areas in North China. Also, the AER reinforced the atmospheric oxidation capacity with the elevations of atmospheric oxidants O3 and ROx radicals, strengthening the chemical conversions to secondary inorganic particles. In both urban and rural areas, the sharp drop in SO2 caused a decrease in sulfate fraction, while the enhanced AOC accelerated the transformation to nitrate even when NOx was reduced. The AER induced nitrate to occupy the principal position in secondary PM2.5 in urban and rural areas. The AER promoted daytime and suppressed nighttime the nitrate production in urban areas, and more vigorous conversion of secondary aerosols were found in rural areas with much lower AOC increments. This study provides insights from a case study over North China in distinct responses of urban and rural O3 pollution with secondary particle changes to AER in urban and rural atmospheric environment changes, with implications for an effective abatement strategy on O3 pollution.

Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion

Emissions of nitrogen oxides (NOx) are a dominant contributor to ambient nitrogen dioxide (NO2) concentrations, but the quantitative relationship between them at an intracity scale remains elusive. The Chengdu 2021 FISU World University Games (July 22 to August 10, 2023) was the first world-class multisport event in China after the COVID-19 pandemic which led to a substantial decline in NOx emissions in Chengdu. This study evaluated the impact of variations in NOx emissions on NO2 concentrations at a fine spatiotemporal scale by leveraging this event-driven experiment. Based on ground-based and satellite observations, we developed a data-driven approach to estimate full-coverage hourly NO2 concentrations at 1 km resolution. Then, a random-forest-based meteorological normalization method was applied to decouple the impact of meteorological conditions on NO2 concentrations for every grid cell, the resulting data were then compared with the timely bottom-up NOx emissions. The SHapley-Additive-exPlanation (SHAP) method was employed to delineate the individual contributions of meteorological factors and various emission sources to the changes in NO2 concentrations. According to the full-coverage meteorologically normalized NO2 concentrations, a decrease in NOx emissions and favorable meteorological conditions accounted for 80 % and 20 % of the NO2 reduction, respectively, across Chengdu city during the control period. Within the strict control zone, a 30 % decrease in the meteorologically normalized NO2 concentrations was observed during the control period. The normalized NO2 concentrations demonstrated a strong correlation with NOx emissions (R = 0.96). Based on the SHAP analysis, traffic emissions accounted for 73 % of the reduction in NO2 concentrations, underscoring the significance of traffic control measures in improving air quality in urban areas. This study provides insights into the relationship between NO2 concentrations and NOx emissions using real-world data, which implies the substantial benefits of vehicle electrification for sustainable urban development.

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air quality monitoring stations in Beijing from 2015 to 2022, this study first employs a deweathering machine learning technique to decouple the confounding effects of meteorological on the air pollution. Furthermore, a detrending percentage change indictor is applied to remove the influence of seasonal variations on air pollution. The findings reveal that: (1) Human interventions are the primary drivers of changes in air pollution concentrations, whereas meteorological factors have a relatively minor impact. (2) During the COVID-19 lockdown, significant variations in air pollution levels are observed, with the effects of city lockdown ranging from a decrease of 40.11% ± 14.81% to an increase of 20.28% ± 14.36%. Notably, there is a decline in concentrations of NO2, PM2.5, CO, and PM10, while the levels of O3 and SO2 increase even during the strictest lockdown period. (3) In the year following the COVID-19 lockdown, there is a rebound in overall air pollution levels. However, by the second year, a general decline in air pollution is observed, except for O3. Therefore, it is imperative to integrate the confounding effects of meteorological factors into air quality management policies under various future scenarios: adopt high-intensity control measures for sudden air quality deteriorations, advance green recovery initiatives for long-term emission reductions, and coordinate efforts to reduce composite atmospheric pollution.

Toxic effects of trace metal(loid) mixtures on aquatic organisms

The co-occurrence of metal (loid)s in realistic aquatic environments necessitates the evaluation of their combined effects. However, the generality of the additive effect hypothesis is contentious, particularly due to metal(loid)-metal(loid) interactions. The absence of systematic evaluation approaches restricts our ability to draw overall conclusions and make reliable predictions. In this study, we reviewed 1473 effect sizes from 38 publications, and classified all responses into seven main categories (from molecular to individual levels) according to their toxicological significance. Our meta-analysis revealed that metal(loid) mixtures had significant effects on aquatic organisms (33 %, 95 % CI 28 %-39 %, P < 0.05), along with significant response heterogeneity (Qt = 690,319.62, P < 0.0001; I2 = 99.95 %). Concurrently, we developed a Random Forest machine learning model to predict adverse effects and identify key variables. These two methods demonstrated that the toxicity of metal(loid) mixtures is primarily linked to the choice of toxicity endpoints, and the characteristics of metal(loid) mixtures. Our findings underscore the potential of combining meta-analysis with machine learning, a more systematic approach, to enhance the understanding and prediction of the adverse effects of metal(loid) mixtures, and they offer guidance for risk assessment and policy-making in complex environmental scenarios.

Particle-ozone complex pollution under diverse synoptic weather patterns in the Yangtze River Delta region: Synergistic relationships and the effects of meteorology and chemical compositions

There is considerable academic interest in the particle-ozone synergistic relationship (PO) between fine particulate matter (PM2.5) and ozone (O3). Using various synoptic weather patterns (SWPs), we quantitatively assessed the variations in the PO, which is relevant to formulating policies aimed at controlling complex pollution in the air. First, based on one-year sampling data from March 2018 to February 2019, the SWPs classification of the Yangtze River Delta (YRD) was conducted using the sum-of-squares technique (SS). Five dominant SWPs can be found in the YRD region, including the Aleutian low under SWP1 (occurring 45 % of the year), a tropical cyclone under SWP2 (21 %), the tropical cyclone and western Pacific Subtropical High (WPSH) under SWP3 (15.4 %), the WPSH under SWP4 (6.9 %), and a continental high pressure under SWP5 (3.1 %). The phenomenon of a "seesaw" between PM2.5 and O3 concentrations exhibited significant spatial heterogeneity, which was influenced by meteorological mechanisms. Second, the multi-linear regression (MLR) model and the partial correlation (PCOR) analysis were employed to quantify the effects of dominant components and meteorological factors on the PO. Meteorological variables could collectively explain only 33.0 % of the PM2.5 variations, but 58.0 % for O3. O3 promoted each other with low concentrations of PM2.5 but was inhibited by high concentrations of PM2.5. High relative humidity (RH) was conducive to the generation of PM2.5 secondary components and enhanced the radiative effects of aerosols and the negative correlation of PO. In addition, attention should be paid to assessing the combined effects of precursor levels, weather, and chemical reactions on the particle-ozone complex pollution. The control of O3 pollutants should be intensified in summer, while the focus should be on reducing PM2.5 pollutants in winter. Prevention and control measures need to reflect the differences in weather conditions and pollution characteristics, with a focus on RH and secondary components of PM2.5.

Xerogel mesoporous materials based on ultrastable Blatter radicals for efficient sorption of nitric oxide

The reduction of hazardous nitric oxide emissions remains a significant ecological challenge. Despite the variety of possibilities, sorbents able to capture low concentrations of NO from flue gas with high selectivity are still in demand. In this work a new type of mesoporous xerogel material highly loaded with ultrastable Blatter radicals (BTR, >60 % by mass) that act as selective NO sorption sites is developed. Electron Paramagnetic Resonance (EPR) spectroscopy evidences reversible NO sorption in nanometer-scale pores of BTR-based xerogels and indicates the high NO capacity of such radical-rich sorbent. Efficient NO capture from model flue gas mixture is also evidenced in experiments with a fixed bed reactor. Such advanced properties of new materials as selectivity, strong binding with NO and an ability for mild regeneration via thermodesorption promote them for future ecological applications.

Understanding the variability of ground-level ozone and fine particulate matter over the Tibetan plateau with data-driven approach

The Tibetan Plateau, known as the "Third Pole", is susceptible to ground-level ozone (O3) and fine particulate matter (PM2.5) pollution due to its unique high-altitude environment. This study constructed random forest regression models using multi-source data from ground measurements and meteorological satellites to predict variations in ground-level O3 and PM2.5 concentrations and their influencing factors across seven major cities in the Tibetan Plateau over two-year periods. The models successfully reproduced O3 and PM2.5 levels with satisfactory R-squared values of 0.71 and 0.73, respectively. Results reveal combustion-related carbon monoxide (CO) and nitrogen dioxide (NO2) as the most substantial influences on O3 and PM2.5 concentrations. Solar radiation, geographical factors, and meteorological variables also played crucial roles in driving pollutant variations. Conversely, transport-related and human activity factors exhibited relatively lower significance. High O3 and PM2.5 pollution occurred during pre-monsoon and post-monsoon/winter seasons, driven by solar radiation and emissions, respectively. While CO consistently contributed across cities and seasons, key influencing factors varied locally. This study unveils the key driving forces governing air pollutant variations across the Tibetan Plateau, shedding light on complex atmospheric processes in this unique high-altitude region.

Machine learning-derived dose-response relationships considering interactions in mixtures: Applications to the oxidative potential of particulate matter

Conventional environmental health research is primarily focused on isolated chemical exposures, neglecting the complex interactions between multiple pollutants that may synergistically or antagonistically influence toxicity, thereby posing unexpected health risks. In this study, we address this knowledge gap by introducing an explainable machine learning (ML) approach with Feature Localized Intercept Transformed-Shapley Additive Explanations (FLIT-SHAP) designed to extract the dose-response relationships of specific pollutants in mixtures. In contrast to traditional SHAP, FLIT-SHAP can localize the global intercept to elucidate mixture effects, which is crucial for understanding the oxidative potential (OP) of ambient particulate matter (PM). Assessing multi-pollutant OP using FLIT-SHAP revealed both synergistic (55-63 %) and antagonistic (25-42 %) effects in laboratory-controlled OP data, but an antagonistic (33-66 %; lower OP) effect in ambient PM. Notably, the FLIT-SHAP approach demonstrated higher prediction accuracy (R2 = 0.99) compared to the additive model (R2 = 0.89) when evaluated against real-world PM samples. Quinones, such as phenanthrenequinone, play a more significant role in PM2.5 than previously recognized. Through this study, we highlighted the potential of FLIT-SHAP to enhance toxicity predictions and aid decision-making in the field of environmental health.

Examining trends and variability of PM2.5-associated organic and elemental carbon in the megacity of Beijing, China: Insight from decadal continuous in-situ hourly observations

Organic carbon (OC) and elemental carbon (EC) in fine particulate matter (PM2.5) play pivotal roles in impacting human health, air quality, and climate change dynamics. Long-term monitoring datasets of OC and EC in PM2.5 are indispensable for comprehending their temporal variations, spatial distribution, evolutionary patterns, and trends, as well as for assessing the effectiveness of clean air action plans. This study presents and scrutinizes a comprehensive 10-year hourly dataset of PM2.5-bound OC and EC in the megacity of Beijing, China, spanning from 2013 to 2022. Throughout the entire study period, the average concentrations of OC and EC were recorded at 8.8 ± 8.7 and 2.5 ± 3.0 μg/m3, respectively. Employing the seasonal and trend decomposition methodology, specifically the locally estimated scatter plot smoothing method combined with generalized least squares with the autoregressive moving average method, the study observed a significant decline in OC and EC concentrations, reducing by 5.8 % yr-1 and 9.9 % yr-1 at rates of 0.8 and 0.4 μg/m3 yr-1, respectively. These declining trends were consistently verified using Theil-Sen method. Notably, the winter months exhibited the most substantial declining trends, with rates of 9.3 % yr-1 for OC and 10.9 % yr-1 for EC, aligning with the positive impact of the implemented clean air action plan. Weekend spikes in OC and EC levels were attributed to factors such as traffic regulations and residential emissions. Diurnal variations showcased higher concentrations during nighttime and lower levels during daytime. Although meteorological factors demonstrated an overall positive impact with average reduction in OC and EC concentrations by 8.3 % and 8.7 %, clean air action plans including the Air Pollution Prevention and Control Action Plan (2013-2017) and the Three-Year Action Plan to Win the Blue Sky War (2018-2020) have more contributions in reducing the OC and EC concentrations with mass drop rates of 87.1 % and 89.2 % and 76.7 % and 96.7 %, respectively. Utilizing the non-parametric wind regression method, significant concentration hotspots were identified at wind speeds of ≤2 m/s, with diffuse signals recorded in the southwestern wind sectors at wind speeds of approximately 4-5 m/s. Interannual disparities in potential source regions of OC and EC were evident, with high potential source areas observed in the southern and northwestern provinces of Beijing from 2013 to 2018. In contrast, during 2019-2022, potential source areas with relatively high values of potential source contribution function were predominantly situated in the southern regions of Beijing. This analysis, grounded in observational data, provides insights into the decadal changes in the major atmospheric composition of PM2.5 and facilitates the evaluation of the efficacy of control policies, particularly relevant for developing countries.

Impact of COVID-19 Lockdown on NO2 Pollution and the Associated Health Burden in China: A Comparison of Different Approaches

So far, a large number of studies have quantified the effect of COVID-19 lockdown measures on air quality in different countries worldwide. However, few studies have compared the influence of different approaches on the estimation results. The present study aimed to utilize a random forest machine learning approach as well as a difference-to-difference approach to explore the effect of lockdown policy on nitrogen dioxide (NO2) concentration during COVID-19 outbreak period in mainland China. Datasets from 2017 to 2019 were adopted to establish the random forest models, which were then applied to predict the NO2 concentrations in 2020, representing a scenario without the lockdown effect. The results showed that random forest models achieved remarkable predictive accuracy for predicting NO2 concentrations, with index of agreement values ranging between 0.34 and 0.76. Compared with the modelled NO2 concentrations, on average, the observed NO2 concentrations decreased by approximately 16 µg/m3 in the lockdown period in 2020. The difference-to-difference approach tended to underestimate the influence of COVID-19 lockdown measures. Due to the improvement of NO2 pollution, around 3722 non-accidental premature deaths were avoided in the studied population. The presented machine learning modelling framework has a great potential to be transferred to other short-term events with abrupt pollutant emission changes.

How international conflicts and global crises can intertwine and affect the sources and levels of air pollution in urban areas

This paper analyses the intertwined impacts of the 2018 US sanctions on Iran and the COVID-19 pandemic (as examples of unplanned international conflicts and global crises) on the source and extent of air pollution in Tehran, the capital of Iran. The impacts are parametrized using the levels of criteria air pollutants (CAP) for 5 years (2015-2020), which were previously deweathered using the promising machine learning technique of Random Forest (RF). The absolute principal component scores-multiple linear regression (APCS-MLR) method and the bivariate polar plot (BPP) technique are used here to analyze the source apportionment profile of the city for the business as usual (BAU; 2015 to 2018), sanctions (2019), and COVID-19 and sanctions (2020) intervals. The results show the severe impact of the 2018 US sanctions on Tehran's air quality (AQ); O3, NO2, CO, PM2.5, and PM10 levels increased by 117%, 55%, 20%, 35%, and 10%, respectively, while SO2 levels decreased by 30%. The sanctions also triggered a number of events, such as the disruption of the high-grade fuel supply chain and the Mazut crisis, which directly or indirectly accelerated the photochemical production of local tropospheric ozone to some extent. Sanctions also disrupted Tehran's AQ response to the pandemic, with CAP levels decreasing by only 2-7% during the pandemic. The ozone and PM10 BPPs show that the source apportionment profile of the city is dominated by local anthropogenic emission sources, especially urban transport, after the sanctions and the pandemic. Results also show that the impact of soft wars, such as the US sanctions against Iran, on urban air quality degradation is much stronger than that of hard wars, such as the Russia-Ukraine war.

Vertically resolved meteorological adjustments of aerosols and trace gases in Beijing, Taiyuan, and Hefei by using RF model

Air pollution represents a complex phenomenon defined by the presence of various gases and particulate matter, leading to intricate spatio-temporal fluctuations. This study aims to enhance our understanding of how meteorological factors influence trace gases and aerosols, exacerbating air pollution in various geographical locations, specifically in Beijing's Fengtai (BJFT), Taiyuan City (SXTY), and Hefei's Science Island (HFDP). The study employs 2D-MAX-DOAS observations and utilizes the Random Forest (RF) model to decouple the influence of meteorological conditions from pollutant data. The vertical profile of nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (HCHO), and aerosols at each study site was classified into four distinct layers, followed by conducting a meteorological decoupling analysis on each layer. This decoupling analysis demonstrates that meteorology significantly influences aerosols across all sites, with reductions ranging from 75 % to 95 % after de-weathering. SO2 shows minimal susceptibility, with the changes ranging from ±20 % to ±60 % after de-weathering. Among all sites, BJFT's pollutants exhibit less susceptibility overall, while pollutants at HFDP are more susceptible. The findings further reveal significant meteorological interventions in pollutants in surface layers (0.05 km and 0.2-0.4 km) at BJFT, with some exceptions at SXTY. However, pollutants, particularly NO2 and aerosols in higher layers (0.6-0.8 km and 1.0-1.2 km) at HFDP, also experience significant meteorological interferences. The findings at HFDP and SXTY reveal that removing meteorological influence also adjusts the profile shape of pollutants. For instance, the NO2 profile at HFDP during the winter season shifted from a bimodal to an exponential shape after de-weathering. Overall, this study sheds light on the complex interplay between meteorological factors and trace gases at various altitudes across different geographic locations, offering insights crucial for holistic and effective pollution mitigation strategies.

A CNN-SVR model for NO2 profile prediction based on MAX-DOAS observations: The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO2 in Nanjing, China

In this study, a hybrid model, the convolutional neural network-support vector regression model, was adopted to achieve prediction of the NO2 profile in Nanjing from January 2019 to March 2021. Given the sudden decline in NO2 in February 2020, the contribution of the Coronavirus Disease-19 (COVID-19) lockdown, Chinese New Year (CNY), and meteorological conditions to the reduction of NO2 was evaluated. NO2 vertical column densities (VCDs) from January to March 2020 decreased by 59.05% and 32.81%, relative to the same period in 2019 and 2021, respectively. During the period of 2020 COVID-19, the average NO2 VCDs were 50.50% and 29.96% lower than those during the pre-lockdown and post-lockdown periods, respectively. The NO2 volume mixing ratios (VMRs) during the 2020 COVID-19 lockdown significantly decreased below 400 m. The NO2 VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period. This phenomenon could be attributed to the 2020 COVID-19 lockdown. The NO2 VMRs before and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period, which further proves that the decrease in NO2 in February 2020 was attributed to the COVID-19 lockdown. Pollution source analysis of an NO2 pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was transported southwards under the action of the north wind, and the subsequent unfavorable meteorological conditions (local wind speed of < 2.0 m/sec) resulted in the accumulation of pollutants.

Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China

Ozone pollution presents a growing air quality threat in urban agglomerations in China. It remains challenge to distinguish the roles of emissions of precursors, chemical production and transportations in shaping the ground-level ozone trends, largely due to complicated interactions among these 3 major processes. This study elucidates the formation factors of ozone pollution and categorizes them into local emissions (anthropogenic and biogenic emissions), transport (precursor transport and direct transport from various regions), and meteorology. Particularly, we attribute meteorology, which affects biogenic emissions and chemical formation as well as transportation, to a perturbation term with fluctuating ranges. The Community Multiscale Air Quality (CMAQ) model was utilized to implement this framework, using the Pearl River Delta region as a case study, to simulate a severe ozone pollution episode in autumn 2019 that affected the entire country. Our findings demonstrate that the average impact of meteorological conditions changed consistently with the variation of ozone pollution levels, indicating that meteorological conditions can exert significant control over the degree of ozone pollution. As the maximum daily 8-hour average (MDA8) ozone concentrations increased from 20 % below to 30 % above the National Ambient Air Quality Standard II, contributions from emissions and precursor transport were enhanced. Concurrently, direct transport within Guangdong province rose from 13.8 % to 22.7 %, underscoring the importance of regional joint prevention and control measures under adverse weather conditions. Regarding biogenic emissions and precursor transport that cannot be directly controlled, we found that their contributions were generally greater in urban areas with high nitrogen oxides (NOx) levels, primarily due to the stronger atmospheric oxidation capacity facilitating ozone formation. Our results indicate that not only local anthropogenic emissions can be controlled in urban areas, but also the impacts of local biogenic emissions and precursor transport can be potentially regulated through reducing atmospheric oxidation capacity.

Characterizing the emission trends and pollution evolution patterns during the transition period following COVID-19 at an industrial megacity of central China

After the resumption of work and production following the COVID-19 pandemic, many cities entered a "transition phase", characterized by the gradual recovery of emission levels from various sources. Although the overall PM2.5 emission trends have recovered, the specific changes in different sources of PM2.5 remain unclear. Here, we investigated the changes in source contributions and the evolution pattern of pollution episodes (PE) in Wuhan during the "transition period" and compared them with the same period during the COVID-19 lockdown. We found that vehicle emissions, industrial processes, and road dust exhibited significant recoveries during the transition period, increasing by 5.4%, 4.8%, and 3.9%, respectively, during the PE. As primary emissions increased, secondary formation slightly declined, but it still played a predominant role (accounting for 39.1∼ 43.0% of secondary nitrate). The reduction in industrial activities was partially offset by residential burning. The evolution characteristics of PE exhibited significant differences between the two periods, with PM2.5 concentration persisting at a high level during the transition period. The differences in the evolution patterns of the two periods were also reflected in their change rates at each stage, which mostly depend on the pre-PE concentration level. The transition period shows a significantly higher value (8.4 μg m-3 h-1) compared with the lockdown period, almost double the amount. In addition to local emissions, regional transport should be a key consideration in pollution mitigation strategies, especially in areas adjacent to Wuhan. Our study quantifies the variations in sources between the two periods, providing valuable insights for optimizing environmental planning to achieve established goals.

Assessing emission-driven changes in health risk of source-specific PM2.5-bound heavy metals by adjusting meteorological covariates

Heavy metals (HMs) in PM2.5 gain much attention for their toxicity and carcinogenic risk. This study evaluates the health risks of PM2.5-bound HMs, focusing on how meteorological conditions affect these risks against the backdrop of PM2.5 reduction trends in China. By applying a receptor model with a meteorological normalization technique, followed by health risk assessment, this work reveals emission-driven changes in health risk of source-specific HMs in the outskirt of Tianjin during the implementation of China' second Clean Air Action (2018-2020). Sources of PM2.5-bound HMs were identified, with significant contributions from vehicular emissions (on average, 33.4 %), coal combustion (26.3 %), biomass burning (14.1 %), dust (11.7 %), industrial boilers (9.7 %), and shipping emission and sea salt (4.7 %). The source-specific emission-driven health risk can be enlarged or dwarfed by the changing meteorological conditions over time, demonstrating that the actual risks from these source emissions for a given time period may be higher or smaller than those estimated by traditional assessments. Meteorology contributed on average 56.1 % to the interannual changes in source-specific carcinogenic risk of HMs from 2018 to 2019, and 5.6 % from 2019 to 2020. For the source-specific noncarcinogenic risk changes, the contributions were 38.3 % and 46.4 % for the respective periods. Meteorology exerts a more profound impact on daily risk (short-term trends) than on annual risk (long-term trends). Such meteorological impacts differ among emission sources in both sign and magnitude. Reduced health risks of HMs were largely from targeted regulatory measures on sources. Therefore, the meteorological covariates should be considered to better evaluate the health benefits attributable to pollution control measures in health risk assessment frameworks.

A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm-"ChatGPT + ML + Environment", providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of "secondary training" for future application of "ChatGPT + ML + Environment".

Atmospheric Oxidation Capacity Elevated during 2020 Spring Lockdown in Chengdu, China: Lessons for Future Secondary Pollution Control

This study presents the measurement of photochemical precursors during the lockdown period from January 23, 2020, to March 14, 2020, in Chengdu in response to the coronavirus (COVID-19) pandemic. To derive the lockdown impact on air quality, the observations are compared to the equivalent periods in the last 2 years. An observation-based model is used to investigate the atmospheric oxidation capacity change during lockdown. OH, HO2, and RO2 concentrations are simulated, which are elevated by 42, 220, and 277%, respectively, during the lockdown period, mainly due to the reduction in nitrogen oxides (NOx). However, the radical turnover rates, i.e., OH oxidation rate L(OH) and local ozone production rate P(O3), which determine the secondary intermediates formation and O3 formation, only increase by 24 and 48%, respectively. Therefore, the oxidation capacity increases slightly during lockdown, which is partly attributed to unchanged alkene concentrations. During the lockdown, alkene ozonolysis seems to be a significant radical primary source due to the elevated O3 concentrations. This unique data set during the lockdown period highlights the importance of controlling alkene emission to mitigate secondary pollution formation in Chengdu and may also be applicable in other regions of China given an expected NOx reduction due to the rapid transformation to electrified fleets in the future.

Air pollution changes due to COVID-19 lockdowns and attributable mortality changes in four countries

COVID-19 lockdowns reduced nitrogen dioxide (NO2) and fine particulate matter (PM2.5) emissions in many countries. We aim to quantify the changes in these pollutants and to assess the attributable changes in mortality in Jiangsu, China; California, U.S.; Central-southern Italy; and Germany during COVID-19 lockdowns in early 2020. Accounting for meteorological impacts and air pollution time trends, we use a machine learning-based meteorological normalization technique and the difference-in-differences approach to quantify the changes in NO2 and PM2.5 concentrations due to lockdowns. Using region-specific estimates of the association between air pollution and mortality derived from a causal modeling approach using data from 2015 to 2019, we assess the changes in mortality attributable to the air pollution changes caused by the lockdowns in early 2020. During the lockdowns, NO2 reductions avoided 1.41 (95% empirical confidence interval [eCI]: 0.94, 1.88), 0.44 (95% eCI: 0.17, 0.71), and 4.66 (95% eCI: 2.03, 7.44) deaths per 100,000 people in Jiangsu, China; California, U.S.; and Central-southern Italy, respectively. Mortality benefits attributable to PM2.5 reductions were also significant, albeit of a smaller magnitude. For Germany, the mortality benefits attributable to NO2 changes were not significant (0.11; 95% eCI: -0.03, 0.25), and an increase in PM2.5 concentrations was associated with an increase in mortality of 0.35 (95% eCI: 0.22, 0.48) deaths per 100,000 people during the lockdown. COVID-19 lockdowns overall improved air quality and brought attributable health benefits, especially associated with NO2 improvements, with notable heterogeneity across regions. This study underscores the importance of accounting for local characteristics when policymakers adapt successful emission control strategies from other regions.

Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate

Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO3-)). The mechanism between ε(NO3-) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3-). Here we introduce a supervised machine learning approach-the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH4+, SO42-, and temperature as pivotal drivers for ε(NO3-). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH4+ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

Regional impact assessment of air quality improvement: The air quality lifecourse assessment tool (AQ-LAT) for the West Midlands combined authority (WMCA)

Poor air quality is the largest environmental health risk in England. In the West Midlands, UK, ∼2.9 million people are affected by air pollution with an average loss in life expectancy of up to 6 months. The 2021 Environment Act established a legal framework for local authorities in England to develop regional air quality plans, generating a policy need for predictive environmental impact assessment tools. In this context, we developed a novel Air Quality Lifecourse Assessment Tool (AQ-LAT) to estimate electoral ward-level impacts of PM2.5 and NO2 exposure on outcomes of interest to local authorities, namely morbidity (asthma, coronary heart disease (CHD), stroke, lung cancer), mortality, and associated healthcare costs. We apply the Tool to assess the health economic burden of air pollutant exposure and estimate benefits that would be generated by meeting WHO 2021 Global Air Quality Guidelines (AQGs) (annual average concentrations) for NO2 (10 μg/m3) and PM2.5 (5 μg/m3) in the West Midlands Combined Authority Area. All West Midlands residents live in areas which exceed WHO AQGs, with 2070 deaths, 2070 asthma diagnoses, 770 CHD diagnoses, 170 lung cancers and 650 strokes attributable to air pollution exposure annually. Reducing PM2.5 and NO2 concentrations to WHO AQGs would save 10,700 lives reducing regional mortality by 1.8%, gaining 92,000 quality-adjusted life years (QALYs), and preventing 20,500 asthma, 7400 CHD, 1400 lung cancer, and 5700 stroke diagnoses, with economic benefits of £3.2 billion over 20 years. Significantly, we estimate 30% of QALY gains relate to reduced disease burden. The AQ-LAT has major potential to be replicated across local authorities in England and applied to inform regional investment decisions.

Dissecting Drivers of Ozone Pollution during the 2022 Multicity Lockdowns in China Sheds Light on Future Control Direction

In 2022, many Chinese cities experienced lockdowns and heatwaves. We analyzed ground and satellite data using machine learning to elucidate chemical and meteorological drivers of changes in O3 pollution in 27 major Chinese cities during lockdowns. We found that there was an increase in O3 concentrations in 23 out of 27 cities compared with the corresponding period in 2021. Random forest modeling indicates that emission reductions in transportation and other sectors, as well as the changes in meteorology, increased the level of O3 in most cities. In cities with over 80% transportation reductions and temperature fluctuations within -2 to 2 °C, the increases in O3 concentrations were mainly attributable to reductions in nitrogen oxide (NOx) emissions. In cities that experienced heatwaves and droughts, increases in the O3 concentrations were primarily driven by increases in temperature and volatile organic compound (VOC) emissions, and reductions in NOx concentrations from ground transport were offset by increases in emissions from coal-fired power generation. Despite 3-99% reduction in passenger volume, most cities remained VOC-limited during lockdowns. These findings demonstrate that to alleviate urban O3 pollution, it will be necessary to further reduce industrial emissions along with transportation sources and to take into account the climate penalty and the impact of heatwaves on O3 pollution.

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: A case study in Taiyuan, China

Quantitative assessment of the drivers behind the variation of six criteria pollutants, namely fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM10), and carbon monoxide (CO), in the warming climate will be critical for subsequent decision-making. Here, a novel hybrid model of multi-task oriented CNN-BiLSTM-Attention was proposed and performed in Taiyuan during 2015-2020 to synchronously and quickly quantify the impact of anthropogenic and meteorological factors on the six criteria pollutants variations. Empirical results revealed the residential and transportation sectors distinctly decreased SO2 by 25 % and 22 % and CO by 12 % and 10 %. Gradual downward trends for PM2.5, PM10, and NO2 were mainly ascribed to the stringent measures implemented in transportation and power sectors as part of the Blue Sky Defense War, which were further reinforced by the COVID-19 pandemic. Nevertheless, temperature-dependent adverse meteorological effects (27 %) and anthropogenic intervention (12 %) jointly increased O3 by 39 %. The O3-driven pollution events may be inevitable or even become more prominent under climate warming. The industrial (5 %) and transportation sectors (6 %) were mainly responsible for the anthropogenic-driven increase of O3 and precursor NO2, respectively. Synergistic reduction of precursors (VOCs and NOx) from industrial and transportation sectors requires coordination with climate actions to mitigate the temperature-dependent O3-driven pollution, thereby improving regional air quality. Meanwhile, the proposed model is expected to be applied flexibly in various regions to quantify the drivers of the pollutant variations in a warming climate, with the potential to offer valuable insights for improving regional air quality in near future.

Substantially reducing global PM2.5-related deaths under SDG3.9 requires better air pollution control and healthcare

The United Nations' Sustainable Development Goal (SDG) 3.9 calls for a substantial reduction in deaths attributable to PM2.5 pollution (DAPP). However, DAPP projections vary greatly and the likelihood of meeting SDG3.9 depends on complex interactions among environmental, socio-economic, and healthcare parameters. We project potential future trends in global DAPP considering the joint effects of each driver (PM2.5 concentration, death rate of diseases, population size, and age structure) and assess the likelihood of achieving SDG3.9 under the Shared Socioeconomic Pathways (SSPs) as quantified by the Scenario Model Intercomparison Project (ScenarioMIP) framework with simulated PM2.5 concentrations from 11 models. We find that a substantial reduction in DAPP would not be achieved under all but the most optimistic scenario settings. Even the development aligned with the Sustainability scenario (SSP1-2.6), in which DAPP was reduced by 19%, still falls just short of achieving a substantial (≥20%) reduction by 2030. Meeting SDG3.9 calls for additional efforts in air pollution control and healthcare to more aggressively reduce DAPP.

Evaluating the multi-variable influence on O3, NO2, and HCHO using BRTs and RF model

This study addresses significant knowledge gaps in understanding the complex interplay between atmospheric chemistry and synoptic conditions. Using emerging machine learning techniques-Boosted Regression Trees (BRTs) and Random Forest (RF) models-we investigate the influence of synoptic conditions on pollutant levels. Several BRTs and RF models are developed to estimate surface concentrations of ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO). By considering a range of algorithmic structures and explanatory variables for each pollutant, the research aims to identify the most skillful predictive approaches and influential factors governing pollutant levels. The design seeks to highlight key determinants of concentration patterns without constraining the investigation to pre-defined model structures or explanatory variable sets. Introducing a novel methodology, Correlation Coefficient Differential Evaluation (C2DE), we quantitatively assess the influence of explanatory variables. C2DE reveals significant contributions from spatial variables (i.e., trajectory clusters at varying altitudes), formaldehyde to nitrogen dioxide ratio (FNR), and meteorological parameters. Specifically, spatial variables contribute approximately 28 % to O3 concentrations, while the FNR accounts for around 5.2-9.8 % of the overall influence. For NO2 and HCHO, spatial variables contribute around 26.5 % and 32.1 %, respectively. Moreover, when considering the combined influence of meteorological parameters, these collectively explain about 45.34 %, 35.31 %, and 45.41 % of the variations in O3, NO2, and HCHO concentrations, respectively. Thus, C2DE provides valuable insights into the relative contributions of these factors, aiding in the comprehensive evaluation of air quality dynamics. This underscores the need for a multifaceted approach to comprehending and effectively addressing air pollution before devising its control strategies.

Response of organic aerosol in Beijing to emission reductions during the XXIV Olympic Winter Games

Organic aerosol (OA) serves as a crucial component of fine particulate matter. However, the response of OA to changes in anthropogenic emissions remains unclear due to its complexity. The XXIV Olympic Winter Games (OWG) provided real atmospheric experimental conditions on studying the response of OA to substantial emission reductions in winter. Here, we explored the sources and variations of OA based on the observation of aerosol mass spectrometer (AMS) combined with positive matrix factorization (PMF) analysis in urban Beijing during the 2022 Olympic Winter Games. The influences of meteorological conditions on OA concentrations were corrected by CO and verified by deweathered model. The CO-normalized primary OA (POA) concentrations from traffic, cooking, coal and biomass burning during the OWG decreased by 39.8 %, 23.2 % and 65.0 %, respectively. Measures controlling coal and biomass burning were most effective in reducing POA during the OWG. For the CO-normalized concentration of secondary OA (SOA), aqueous-phase related oxygenated OA decreased by 51.8 % due to the lower relative humidity and emission reduction in precursors, while the less oxidized‑oxygenated OA even slightly increased as the enhanced atmospheric oxidation processes may partially offset the efficacy of emission control. Therefore, more targeted reduction of organic precursors shall be enhanced to lower atmospheric oxidation capacity and mitigate SOA pollution.

Exploring formation mechanism and source attribution of ozone during the 2019 Wuhan Military World Games: Implications for ozone control strategies

A series of emission reduction measures were conducted in Wuhan, Central China, to ensure good air quality during the 7th Military World Games (MWG) in October 2019. To better understand the implications for ozone (O3) pollution control strategies, we applied integrated analysis approaches based on the de-weathered statistical model, parameterization methods, chemical box model, and positive matrix factorization model. During the MWG, concentrations of O3, NOx, and volatile organic compound (VOCs), OFP (O3 formation potential), LOH (OH radical loss rate) were 83 µg/m3, 43 µg/m3, 26 ppbv, 188 µg/m3, and 3.9 s-1, respectively, which were 26%, 18%, 3%, 15%, and 13% lower than pre-MWG values and 6%, 39%, 30%, 33%, and 50% lower than post-MWG values, respectively. After removing meteorological influence, O3 and its precursors during the MWG decreased largely compared with post-MWG values, and only O3, NO2, and oxygenated VOCs (OVOCs) declined compared with pre-MWG values, which revealed the emission reduction measures during the MWG played an important role for O3 decline. For six VOCs sources, the mass contributions of biomass burning and solvents usage during the MWG decreased largely compared with pre-MWG values. O3 production was sensitive to VOCs and the key species were aromatics, OVOCs, and alkenes, which originated mainly from solvents usage, biomass burning, industrial-related combustion, and vehicle exhaust. Decreasing O3 concentration during the strict control was mainly caused by OVOCs reduction due to biomass burning control. Generally, the O3 abatement strategies of Wuhan should be focused on the mitigation of high-reactivity VOCs.

Impacts of ambient air quality on acute asthma hospital admissions during the COVID-19 pandemic in Oxford City, UK: a time-series study

OBJECTIVES

The study aims to investigate the short-term associations between exposure to ambient air pollution (nitrogen dioxide (NO2), particulate matter pollution-particles with diameter<2.5 µm (PM2.5) and PM10) and incidence of asthma hospital admissions among adults, in Oxford, UK.

DESIGN

Retrospective time-series study.

SETTING

Oxford City (postcode areas OX1-OX4), UK.

PARTICIPANTS

Adult population living within the postcode areas OX1-OX4 in Oxford, UK from 1 January 2015 to 31 December 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

Hourly NO2, PM2.5 and PM10 concentrations and meteorological data for the period 1 January 2015 to 31 December 2020 were analysed and used as exposures. We used Poisson linear regression analysis to identify independent associations between air pollutant concentrations and asthma admissions rate among the adult study population, using both single (NO2, PM2.5, PM10) and multipollutant (NO2 and PM2.5, NO2 and PM10) models, where they adjustment for temperature and relative humidity.

RESULTS

The overall 5-year average asthma admissions rate was 78 per 100 000 population during the study period. The annual average rate decreased to 46 per 100 000 population during 2020 (incidence rate ratio 0.58, 95% CI 0.42 to 0.81, p<0.001) compared to the prepandemic years (2015-2019). In single-pollutant analysis, we observed a significantly increased risk of asthma admission associated with each 1 μg/m3 increase in monthly concentrations of NO2 4% (95% CI 1.009% to 1.072%), PM2.5 3% (95% CI 1.006% to 1.052%) and PM10 1.8% (95% CI 0.999% to 1.038%). However, in the multipollutant regression model, the effect of each individual pollutant was attenuated.

CONCLUSIONS

Ambient NO2 and PM2.5 air pollution exposure increased the risk of asthma admissions in this urban setting. Improvements in air quality during COVID-19 lockdown periods may have contributed to a substantially reduced acute asthma disease burden. Large-scale measures to improve air quality have potential to protect vulnerable people living with chronic asthma in urban areas.

In situ biomass burning enhanced the contribution of biogenic sources to sulfate aerosol in subtropical cities

Sulfurous gases released by biogenic sources play a key role in the global sulfur cycle. However, the contribution of biogenic sources to sulfate aerosol in the urban atmosphere has received little attention. Emission sources and formation process of sulfate in Guangzhou, a subtropical mega-city in China, were clarified using multiple methods, including isotope tracers and chemical markers. The δ18O of sulfate suggested that secondary sulfate was the dominant component (84 %) of sulfate aerosol, which mainly formed by transition metal ion (TMI) catalyzed oxidation (31 %) and OH radical oxidation (30 %). The factors driving secondary sulfate formation were revealed using a tree boosting model, which suggested that NH3, temperature, and oxidants were the most important factors. The δ34S of sulfate indicated that biogenic sources accounted for annual average of 26.0 % of the sulfate, which increased to 30.4 % in winter monsoon period. Rice straw burning enhanced sulfate formation by promoting the release of reduced sulfur from soil, which is rapidly converted into sulfate under a subtropical urban atmosphere with high concentration of NH3 and oxidants. This study revealed the important influence of rice straw burning on biogenic sulfur emission during the rice harvest, thereby providing insight into the sulfur cycle and regional air pollution.

Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs

The impact of measures to restrict population mobility during the COVID-19 pandemic on atmospheric concentrations of polycyclic aromatic hydrocarbons (PAH) and brominated flame retardants (BFRs) is poorly understood. This study analyses the effects of meteorological parameters and mobility restrictions during the COVID-19 pandemic on concentrations of PAH and BFRs at the University of Birmingham in the UK utilising a neural network (self-organising maps, SOM). Air sampling was performed using Polyurethane Foam (PUF) disk passive samplers between October 2019 and January 2021. Data on concentrations of PAH and BFRs were analysed using SOM and Spearman's rank correlation. Data on meteorological parameters (air temperature, wind, and relative humidity) and mobility restrictions during the pandemic were included in the analysis. Decabromodiphenyl ether (BDE-209) was the most abundant polybrominated diphenyl ether (PBDE) (23-91% Σ7PBDEs) but was detected at lower absolute concentrations (4.2-35.0 pg m-3) than in previous investigations in Birmingham. Air samples were clustered in five groups based on SOM analysis and the effects of meteorology and pandemic-related restrictions on population mobility could be visualised. Concentrations of most PAH decreased during the early stages of the pandemic when mobility was most restricted. SOM analysis also helped to identify the important influence of wind speed on contaminant concentrations, contributing to reduce the concentration of all analysed pollutants. In contrast, concentrations of most PBDEs remained similar or increased during the first COVID-19 lockdown which was attributed to their primarily indoor sources that were either unaffected or increased during lockdown.

Air pollution benefits from reduced on-road activity due to COVID-19 in the United States

On-road transportation is one of the largest contributors to air pollution in the United States. The COVID-19 pandemic provided the unintended experiment of reduced on-road emissions' impacts on air pollution due to lockdowns across the United States. Studies have quantified on-road transportation's impact on fine particulate matter (PM2.5)-attributable and ozone (O3)-attributable adverse health outcomes in the United States, and other studies have quantified air pollution-attributable health outcome reductions due to COVID-19-related lockdowns. We aim to quantify the PM2.5-attributable, O3-attributable, and nitrogen dioxide (NO2)-attributable adverse health outcomes from traffic emissions as well as the air pollution benefits due to reduced on-road activity during the pandemic in 2020. We estimate 79,400 (95% CI 46,100-121,000) premature mortalities each year due to on-road-attributable PM2.5, O3, and NO2. We further break down the impacts by pollutant and vehicle types (passenger [PAS] vs. freight [FRT] vehicles). We estimate PAS vehicles to be responsible for 63% of total impacts and FRT vehicles 37%. Nitrogen oxide (NOX) emissions from these vehicles are responsible for 78% of total impacts as it is a precursor for PM2.5 and O3. Utilizing annual vehicle miles traveled reductions in 2020, we estimate that 9,300 (5,500-14,000) deaths from air pollution were avoided in 2020 due to the state-specific reductions in on-road activity across the continental United States. By quantifying the air pollution public health benefits from lockdown-related reductions in on-road emissions, the results from this study stress the need for continued emission mitigation policies, like the U.S. Environmental Protection Agency's (EPA) recently proposed NOX standards for heavy-duty vehicles, to mitigate on-road transportation's public health impact.

Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022

Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr-1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr-1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018-2020 compared to that during 2014-2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.

Revisiting the dynamics of gaseous ammonia and ammonium aerosols during the COVID-19 lockdown in urban Beijing using machine learning models

The concentration of atmospheric ammonia (NH3) in urban Beijing substantially decreased during the COVID-19 lockdown (24 January to 3 March 2020), likely due to the reduced human activities. However, quantifying the impact of anthropogenic interventions on NH3 dynamics is challenging, as both meteorology and chemistry mask the real changes in observed NH3 concentrations. Here, we applied machine learning techniques based on random forest models to decouple the impacts of meteorology and emission changes on the gaseous NH3 and ammonium aerosol (NH4+) concentrations in Beijing during the lockdown. Our results showed that the meteorological conditions were unfavorable during the lockdown and tended to cause an increase of 8.4 % in the NH3 concentration. In addition, significant reductions in NOx and SO2 emissions could also elevate NH3 concentrations by favoring NH3 gas-phase partitioning. However, the observed NH3 concentration significantly decreased by 35.9 % during the lockdown, indicating a significant reduction in emissions or enhanced chemical sinks. Rapid gas-to-particle conversion was indeed found during the lockdown. Thus, the observed reduced NH3 concentrations could be partially explained by the enhanced transformation into NH4+. Therefore, the sum of NH3 and NH4+ (collectively, NHx) is a more reliable tracer than NH3 or NH4+ alone to estimate the changes in NH3 emissions. Compared to that under the scenario without lockdowns, the NHx concentration decreased by 26.4 %. We considered that this decrease represents the real decrease in NH3 emissions in Beijing due to the lockdown measures, which was less of a decrease than that based on NH3 only (35.9 %). This study highlights the importance of considering chemical sinks in the atmosphere when applying machine learning techniques to link the concentrations of reactive species with their emissions.

Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020

Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 μg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 μg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.

Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.

Attribution of Air Quality Benefits to Clean Winter Heating Policies in China: Combining Machine Learning with Causal Inference

Heating is a major source of air pollution. To improve air quality, a range of clean heating policies were implemented in China over the past decade. Here, we evaluated the impacts of winter heating and clean heating policies on air quality in China using a novel, observation-based causal inference approach. During 2015-2021, winter heating causally increased annual PM2.5, daily maximum 8-h average O3, and SO2 by 4.6, 2.5, and 2.3 μg m-3, respectively. From 2015 to 2021, the impacts of winter heating on PM2.5 in Beijing and surrounding cities (i.e., "2 + 26" cities) decreased by 5.9 μg m-3 (41.3%), whereas that in other northern cities only decreased by 1.2 μg m-3 (12.9%). This demonstrates the effectiveness of stricter clean heating policies on PM2.5 in "2 + 26" cities. Overall, clean heating policies caused the annual PM2.5 in mainland China to reduce by 1.9 μg m-3 from 2015 to 2021, potentially avoiding 23,556 premature deaths in 2021.

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London

Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.

Impacts of net zero policies on air quality in a metropolitan area of the United Kingdom: Towards world health organization air quality guidelines

Climate change and air pollution are closely interlinked since carbon dioxide and air pollutants are co-emitted from fossil fuel combustion. Net Zero (NZ) policies aiming to reduce carbon emissions will likely bring co-benefits in air quality and associated health. However, it is unknown whether regional NZ policies alone will be sufficient to reduce air pollutant levels to meet the latest 2021 World Health Organisation (WHO) guidelines. Here, we carried out high resolution air quality modelling for in the West Midlands region, a typical metropolitan area in the UK, to quantify the effects of different NZ policies on air quality. Results show that NZ policies will significantly improve air quality in the West Midlands, with up to 6 μg m-3 (21%) reduction in annual mean NO2 (mostly through the electrification of vehicle fleet, EV) and up to 1.4 μg m-3 (12%) reduction in annual mean PM2.5 projected for 2030 relative to levels under a "business as usual" (BAU) scenario. Under BAU, 2030 PM2.5 concentrations in most wards would be below 10 μg m-3 whilst under the Net Zero scenario, those in all wards would be below 10 μg m-3. This means that the ward averages in the West Midlands would meet the UK PM2.5 of 10 μg m-3target a decade early under the Net Zero scenario. However, no ward-level-averaged annual mean PM2.concentrations meet the 2021 WHO Air Quality guideline level of 5 μg m-3 under any scenario. Similarly for NO2 only 18 wards (8% of the region's population) are predicted to have NO2 concentrations below the 2021 WHO guideline level (10 μg m-3). Decarbonisation policies linked to Net Zero deliver substantial regional air quality benefits, but are not in isolation sufficient to deliver clean air with air pollutant levels low enough to meet the 2021 WHO guidelines.

Hindered visibility improvement despite marked reduction in anthropogenic emissions in a megacity of southwestern China: An interplay between enhanced secondary inorganics formation and hygroscopic growth at prevailing high RH conditions

The PM2.5-bound visibility improvement remains challenging in China despite vigorous control on anthropogenic emissions in recent years. One critical issue could exist in the distinct physicochemical properties especially of secondary aerosol components. Taken the COVID-19 lockdown as an extreme case, we focus on the relationship between visibility, emission cuts, and secondary formation of inorganics with changing optical and hygroscopic behaviors in Chongqing, a representative city characterized with humid weather and poor diffusion conditions in Sichuan Basin, southwest of China. It is found that the increased secondary aerosol abundance (e.g., PM2.5/CO and PM2.5/PM10 as a proxy) with enhanced atmospheric oxidative capacity (e.g., O3/Ox, Ox = O3 + NO2), combined with insignificant meteorological dilution effect, might partly offset the benefit on the improved visibility from substantial reduction in anthropogenic emissions during the COVID-19 lockdown. This is in line with the efficient oxidation rates of sulfur and nitrogen (i.e., SOR, NOR), increasing more significantly with PM2.5 and relative humidity (RH) in comparison to O3/Ox. The resulted larger fraction of nitrate and sulfate (i.e., fSNA) would promote the optical enhancement (i.e., f(RH)) and mass extinction efficiency (MEE) of PM2.5, especially under highly humid conditions (e.g., RH > 80 %, with approximately half of the occurrence frequency). This could further facilitate secondary aerosol formation via aqueous-phase reaction and heterogeneous oxidation, likely due to enhanced water uptake and enlarged size/surface area upon hydration. In combination of gradually increased atmospheric oxidative capacity, this positive feedback would in turn inhibit the visibility improvement particularly at high RH environment. Considering the current air pollution complex status over China, further work on the formation mechanisms of major secondary species (e.g., sulfate, nitrate, and secondary organics), size-resolved chemical and hygroscopic properties, together with their interactions are highly recommended. Our results are hoping to assist in the atmospheric pollution complex mitigation and prevention in China.

Spatial heterogeneity in global atmospheric CO during the COVID-19 lockdown: Implications for global and regional air quality policies

The COVID-19 lockdown (LD) provided a unique opportunity to examine the changes in regional and global air quality. Changes in the atmospheric carbon monoxide (CO) during LD warrant a thorough analysis as CO is a major air pollutant that affects human health, ecosystem and climate. Our analysis reveals a decrease of 5-10% in the CO column during LD (April-May 2020) compared to the pre-lockdown (PreLD, March 2020) periods in regions with high anthropogenic activity, such as East China (EC), Indo-Gangetic Plain (IGP), North America, parts of Europe and Russia. However, this reduction did not occur in the regions of frequent and intense wildfires and agricultural waste burning (AWB). We find high heterogeneity in the CO column distributions, from regional to city scales during the LD period. To determine the sources of CO emissions during LD, we examined the ratios of nitrogen dioxide (NO2), sulfur dioxide (SO2) to CO for major cities in the world. This facilitated the identification of contributions from different sources; including vehicles, industries and biomass burning during LD. The comparison between CO levels during the LD and PreLD periods indicates a notable reduction in the global tropospheric CO, but no significant change in the stratosphere. It is found that CO emissions decreased during LD in the hotspot regions, but rebounded after the LD restrictions were lifted. This study, therefore, highlights the importance of policy decisions and their implementations in the global and regional scales to improve the air quality, and thus to protect public health and environment.

Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach

Over the past few years, surface ozone (O3) pollution has dominated China's air pollution as particulate matter has decreased. In Beijing, the annual average concentrations of ground-level O3 from 2015 to 2020 regularly increased from 57.32 to 62.72 μg/m3, showing a change of almost 9.4%, with a 1.6% per year increase. The meteorological factors are the primary influencer of elevated O3 levels; however, their importance and heterogeneity of variables remain rarely understood. In this study, we used 13 meteorological factors and 6 air quality (AQ) parameters to estimate their influencing score using the random forest (RF) algorithm to explain and predict ambient O3. Among the meteorological variables and overall, both land surface temperature and temperature at 2 m from the surface emerged as the most influential factors, while NO2 stood out as the highest influencing factor from the AQ parameters. Indeed, it is crucial and imperative to reduce the temperature caused by climate change in order to effectively control ambient O3 levels in Beijing. Overall, meteorological factors alone exhibited a higher coefficient of determination (R2) value of 0.80, compared with AQ variables of 0.58, for the post-lockdown period. In addition, we calculated the number of days O3 concentration levels exceeded the WHO standard and newly proposed peak-season maximum daily 8-h average (MDA8) O3 guideline for Beijing. The exceedance number of days from the WHO standard of MDA8 ambient O3 was observed to be the highest in June, and each studied year crossed peak season guidelines by almost 2 times margin. This study demonstrates the contributions of meteorological variables and AQ parameters in surging ambient O3 and highlights the importance of future research toward devising an optimum strategy to combat growing O3 pollution in urban areas.