Tropospheric ozone and NO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e710" altimg="si37.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mi mathvariant="normal">x</mml:mi></mml:mrow></mml:msub></mml:math>: A review of worldwide variation and meteorological influences

Nguyen, Duy-Hieu; Lin, Chitsan; Vu, Chi Thanh; Cheruiyot, Nicholas Kiprotich; Ky, Nguyen Minh; Le, Thi Hieu; Lukkhasorn, Wisanukorn; Vo, Thi-Dieu-Hien; Bui, Xuan‐Thanh

doi:10.1016/j.eti.2022.102809

Cited by 37 publications

(6 citation statements)

References 131 publications

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“…Consistent with previous studies that highlight the role of meteorology on O3 (Nguyen et al, 2022), modeling results show 225 that accumulation days are especially relevant regarding the potential impacts on health and vegetation and a deeper analysis of pollution dynamics under those conditions is of interest. Figure 6 shows the hourly evolution (3:00, 9:00; 15:00, 21:00 UTC) of surface O3 concentrations during the day 27 th (day 6 th Figure S4), along with O3, NOx and VOCs vertical concentration up to 5 km height for a NE-SW cross section, related to the dominant wind directions (the same results for a perpendicular SE-NW cross section are shown in Figure S5 in the supplement).…”

Section: Accumulation Patternsupporting

confidence: 85%

Summertime tropospheric ozone source apportionment study in Madrid (Spain)

de la Paz,

Borge,

de Andrés

et al. 2023

Preprint

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Abstract. The design of emission abatement measures to effectivly reduce high ground-level ozone (O3) concentrations in urban areas is very complex. In addition to the strongly non-linear chemistry of this secondary pollutant, precursors can be released by a variety of sources in different regions and locally produced O3 is mixed with that transported from the regional or continental scales. All of these processes depend also on the specific meteorological conditons and topography of the study area. Consequently, high-resolution comprehensive modeling tools are needed to understand the drivers of photochemical pollution and to assess the potential of local strategies to reduce adverse impacts from high tropospheric O3 levels. In this study, we apply the Integrated Source Apportionment Method (ISAM) implemented in the Community Multiscale Air Quality (CMAQv5.3.2) model to investigate the origin of summertime O3 in the Madrid region (Spain). Consistent with previous studies, our results confirm that O3 levels are dominated by non-local contributions, representing around 70 % of mean values across the region. Nonetheless, precursors emitted by local sources, mainly road traffic, play a more important role during O3 peaks, with contributions as high as 25 ppb. The potential impact of local measures is higher under unfavorable meteorological conditions associated with regional accumulation patterns. These findings suggest that this modeling system may be used in the future to simulate the potential outcomes of specific emission abatement measures to prevent high-O3 episodes in the Madrid metropolitan area.

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Section: Accumulation Patternsupporting

confidence: 85%

Summertime tropospheric ozone source apportionment study in Madrid (Spain)

de la Paz,

Borge,

de Andrés

et al. 2023

Preprint

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“…Climate change can interplay with other air contaminants, such as O 3 , NOx, and VOCs, resulting in intricate effects on air quality. Elevated temperatures can amplify O 3 formation through intensified photochemical reactions that involve NOx and VOCs [ 25 ]. Furthermore, climate change can modify the emissions and atmospheric chemistry of VOCs and NOx, influencing their concentrations and interactions with PM [ 26 ].…”

Section: Climate Change and Air Qualitymentioning

confidence: 99%

Climate change, air quality, and respiratory health: a focus on particle deposition in the lungs

Chang,

Lee,

Chang

et al. 2023

Annals of Medicine

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This review article delves into the multifaceted relationship between climate change, air quality, and respiratory health, placing a special focus on the process of particle deposition in the lungs. We discuss the capability of climate change to intensify air pollution and alter particulate matter physicochemical properties such as size, dispersion, and chemical composition. These alterations play a significant role in influencing the deposition of particles in the lungs, leading to consequential respiratory health effects. The review paper provides a broad exploration of climate change’s direct and indirect role in modifying particulate air pollution features and its interaction with other air pollutants, which may change the ability of particle deposition in the lungs. In conclusion, climate change may play an important role in regulating particle deposition in the lungs by changing physicochemistry of particulate air pollution, therefore, increasing the risk of respiratory disease development.

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“…The corresponding cyclic reactions involve , Volatile Organic Compounds (VOCs) and CO . High humidity is generally associated with cloudy sky and precipitation, resulting in weaker solar radiation, while on the other hand low humidity favors solar radiation which enhances ozone production 80 . This explains in great part the high contribution of a low humidity forecast on ozone concentration observed in Fig.…”

Section: Explaining Pollution Forecastsmentioning

confidence: 99%

Explainable sequence-to-sequence GRU neural network for pollution forecasting

Borujeni

Arras

Srinivasan

et al. 2023

Sci Rep

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The goal of pollution forecasting models is to allow the prediction and control of the air quality. Non-linear data-driven approaches based on deep neural networks have been increasingly used in such contexts showing significant improvements w.r.t. more conventional approaches like regression models and mechanistic approaches. While such deep learning models were deemed for a long time as black boxes, recent advances in eXplainable AI (XAI) allow to look through the model’s decision-making process, providing insights into decisive input features responsible for the model’s prediction. One XAI technique to explain the predictions of neural networks which was proven useful in various domains is Layer-wise Relevance Propagation (LRP). In this work, we extend the LRP technique to a sequence-to-sequence neural network model with GRU layers. The explanation heatmaps provided by LRP allow us to identify important meteorological and temporal features responsible for the accumulation of four major pollutants in the air ($$\text {PM}_{10}$$ PM 10 , $$\text {NO}_{2}$$ NO 2 , $$\text {NO}$$ NO , $$\text {O}_{3}$$ O 3 ), and our findings can be backed up with prior knowledge in environmental and pollution research. This illustrates the appropriateness of XAI for understanding pollution forecastings and opens up new avenues for controlling and mitigating the pollutants’ load in the air.

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Tropospheric ozone and NOx: A review of worldwide variation and meteorological influences

Cited by 37 publications

References 131 publications

Summertime tropospheric ozone source apportionment study in Madrid (Spain)

Summertime tropospheric ozone source apportionment study in Madrid (Spain)

Climate change, air quality, and respiratory health: a focus on particle deposition in the lungs

Explainable sequence-to-sequence GRU neural network for pollution forecasting

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