Outdoor air pollution is estimated to cause a huge number of premature deaths worldwide. It catalyzes many diseases on a variety of time scales, and it has a detrimental effect on the environment. In light of these impacts, it is necessary to obtain a better understanding of the dynamics and statistics of measured air pollution concentrations, including temporal fluctuations of observed concentrations and spatial heterogeneities. Here, we present an extensive analysis for measured data from Europe. The observed probability density functions (PDFs) of air pollution concentrations depend very much on the spatial location and the pollutant substance. We analyze a large number of time series data from 3544 different European monitoring sites and show that the PDFs of nitric oxide ($ NO $), nitrogen dioxide ($ {NO}_2 $), and particulate matter ($ {PM}_{10} $ and $ {PM}_{2.5} $) concentrations generically exhibit heavy tails. These are asymptotically well approximated by $ q $-exponential distributions with a given entropic index $ q $ and width parameter $ \lambda $. We observe that the power-law parameter $ q $ and the width parameter $ \lambda $ vary widely for the different spatial locations. We present the results of our data analysis in the form of a map that shows which parameters $ q $ and $ \lambda $ are most relevant in a given region. A variety of interesting spatial patterns is observed that correlate to the properties of the geographical region. We also present results on typical time scales associated with the dynamical behavior.

Indoor air pollution is one of the leading causes of morbidity and mortality worldwide, but its sources and impacts are largely misunderstood by the public. In a randomised controlled trial including 281 households in France, we test two interventions aimed at changing indoor polluting behaviour by raising households’ awareness of health risks associated with indoor air pollution. While both generic and personalised information increased knowledge, only personalised information including social comparison feedback changed behaviour, leading to a reduction of indoor PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) emissions by 20% on average. Heterogeneous treatment effects show that this effect is concentrated on the most polluted households at baseline, for whom the reduction reaches 40%.

Atmospheric chemical reactions play an important role in air quality and climate change. While the structure and dynamics of individual chemical reactions are fairly well understood, the emergent properties of the entire atmospheric chemical system, which can involve many different species that participate in many different reactions, are not well described. In this work, we leverage graph-theoretic techniques to characterize patterns of interaction (“motifs”) in three different representations of gas-phase atmospheric chemistry, termed “chemical mechanisms.” These widely used mechanisms, the master chemical mechanism, the GEOS-Chem mechanism, and the Super-Fast mechanism, vary dramatically in scale and application, but they all generally aim to simulate the abundance and variability of chemical species in the atmosphere. This motif analysis quantifies the fundamental patterns of interaction within the mechanisms, which are directly related to their construction. For example, the gas-phase chemistry in the very small Super-Fast mechanism is entirely composed of bimolecular reactions, and its motif distribution matches that of an individual bimolecular reaction well. The larger and more complex mechanisms show emergent motif distributions that differ strongly from any specific reaction type, consistent with their complexity. The proposed motif analysis demonstrates that while these mechanisms all have a similar design goal, their higher-order structure of interactions differs strongly and thus provides a novel set of tools for exploring differences across chemical mechanisms.

This position paper highlights the dire impacts of environmental and household air pollution, which were responsible for 6.7 million deaths globally in 2019. These deaths occurred predominantly in low- and middle-income countries, with Afghanistan reporting the highest age-adjusted mortality rate. The situation worsens during large-scale disasters like earthquakes, which release more pollutants into the air, exacerbating health risks and leading to severe conditions such as pulmonary diseases. Because political factors may hinder foreign NGOs and similar organizations from providing direct support, the frequent occurrence of earthquakes in Afghanistan underscores the critical need for emergency response training for local residents. Consequently, it is essential to provide ERT training, including the proper use of protective equipment, to local populations as well as disseminating risk communication through online technologies and other appropriate means.

Coal is declining in the U.S. as part of the clean energy transition, resulting in remarkable air pollution benefits for the American public and significant costs for the industry. Using the AP3 integrated assessment model, we estimate that fewer emissions of sulfur dioxide, nitrogen oxides, and primary fine particulate matter driven by coal’s decline led to $300 billion in benefits from 2014 to 2019. Conversely, we find that job losses driven by less coal plant and mining activity resulted in $7.84 billion in foregone wages over the same timeframe. While the benefits were greatly distributed (mostly throughout the East), costs were highly concentrated in coal communities. Transferring a small fraction of the benefits to workers could cover these costs while maintaining societal net benefits. Forecasting coal fleet damages from 2020 to 2035, we find that buying out or replacing these plants would result in $589 billion in air quality benefits, which considerably outweigh the costs. The return on investment increases when policy targets the most damaging capacity, and net benefits are maximized when removing just facilities where marginal benefits exceed marginal costs. Evaluating competitive reverse auction policy designs akin to Germany’s Coal Exit Act, we find that adjusting bids based on monetary damages rather than based only on carbon dioxide emissions – the German design – provides a welfare advantage. Our benefit–cost analyses clearly support policies that drive a swift and just transition away from coal, thereby clearing the air while supporting communities needing assistance.

An experiment was conducted on the development of Hypogymnia physodes (L.) Nyl. from soredia to branched lobes near the copper smelter in the Middle Urals 10 years after emission cessation. Soredia were cultivated in fir-spruce forests in heavily polluted and unpolluted areas. In both areas, soredia development was examined on fir bark collected from both the heavily polluted and unpolluted areas. The probability of lobe formation was lower in the polluted area even when soredia were cultivated on bark from the unpolluted area. Bark from the polluted area negatively impacted the success of soredia development, irrespective of the cultivation area. The lowest success of early development occurred when soredia were cultivated on polluted bark in the heavily polluted area, where the already low probability of lobe formation was accompanied by high mortality of the developed lobes. This study underscores the enduring impact of industrial pollution on H. physodes development, highlighting the need for ongoing environmental restoration and monitoring efforts in post-industrial areas to support biodiversity conservation.

This study investigates the effect of political misinformation as propaganda on the legitimacy of the Chinese government. A survey experiment (n = 2,236) was conducted to analyse the effect of positively spun misinformation on citizens’ perceptions and support for the government on two pivotal issues: the economy and air pollution. Results show that spreading positively spun misinformation is beneficial to the regime, as it leads to positive perceptions of the issue and increased support for the government. Interestingly, even when misinformation is exposed through credibly sourced corrections, trust and support for the government remain as high or higher than for the control groups. These effects are significant and hold constant in two issue types, underscoring the strategic value of disseminating positively spun yet false information in China. These findings have rich implications for studies of misinformation and fact-checking in general and China's information politics in particular.

On 9 March 2023, the Court of Justice (Second Chamber) delivered a preliminary ruling about the coordination of two European Union measures against air pollution: the Industrial Emissions Directive and the Ambient Air Quality Directive. Upon assessment, the Court reinforced the mandatory nature of the air quality limit values vis-à-vis possible derogations foreseen in the Industrial Emissions Directive. In this case, both AG Kokott and the Second Chamber affirmed the primacy of the air quality standards. However, this Case Note finds that slightly different reasoning between the AG Opinion and the final Judgment reveals differing underlying approaches. Whilst the AG focuses on air quality plans and leaves more room for discretion to national authorities, the final Judgment anchors the coordination of the two Directives to strict and objective pollution limit values, further strengthening the Ambient Air Quality Directive as an effective instrument of environmental protection litigation.

Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Accurate short-term ozone forecasts may allow improved policy actions to reduce the risk to human health. However, forecasting surface ozone is a difficult problem as its concentrations are controlled by a number of physical and chemical processes that act on varying timescales. We implement a state-of-the-art transformer-based model, the temporal fusion transformer, trained on observational data from three European countries. In four-day forecasts of daily maximum 8-hour ozone (DMA8), our novel approach is highly skillful (MAE = 4.9 ppb, coefficient of determination $ {\mathrm{R}}^2=0.81 $) and generalizes well to data from 13 other European countries unseen during training (MAE = 5.0 ppb, $ {\mathrm{R}}^2=0.78 $). The model outperforms other machine learning models on our data (ridge regression, random forests, and long short-term memory networks) and compares favorably to the performance of other published deep learning architectures tested on different data. Furthermore, we illustrate that the model pays attention to physical variables known to control ozone concentrations and that the attention mechanism allows the model to use the most relevant days of past ozone concentrations to make accurate forecasts on test data. The skillful performance of the model, particularly in generalizing to unseen European countries, suggests that machine learning methods may provide a computationally cheap approach for accurate air quality forecasting across Europe.

From 2013 to 2015, China gradually established nationwide air quality monitoring stations and began to release real-time air pollution information to the public. We exploit step-by-step environmental regulations across cities to identify the effects of information disclosure on air pollution. We find that information disclosure significantly decreases the concentrations of PM2.5 and PM10. Through mechanism analysis, we find that information disclosure raises the level of government awareness, increases the amount of investments in air pollution prevention and control, stimulates green innovation, and forces heavily polluting enterprises to shut down. Additionally, we find evidence that the effectiveness of information disclosure varies across cities.

Exposure to industrial pollutants is a potential risk factor not fully explored in ASD with regression (ASD+R). We studied geographical collocation patterns of industrial air chemical emissions and the location of homes of children with ASD+R at different exposure times, compared with ASD cases without regression (ASD−R). Fifteen of 111 emitted chemicals collocated with ASD+R, and 65 with ASD−R. ASD+R collocated more strongly with different neurotoxicants/immunotoxicants a year before diagnosis, whereas ASD−R were moderately collocated with chemicals across all exposure periods. This preliminary exploratory analysis of differences in exposure patterns raises a question regarding potential pathophysiological differences between the conditions.