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.

This article analyses a domestic litigation matter seeking to establish accountability for air pollution-related human rights violations. It examines how the judiciary applied national and international law to dismiss the case on procedural grounds. It argues that the domestic case deserves careful reading for a number of reasons that can be distilled into two premises. Firstly, the national legal framework and its respective judicial interpretation impede access to justice for victims of state and/or corporate human rights violations. Secondly, it is essential that the state develops laws and policies in line with the United Nations Guiding Principles on Business and Human Rights, which would allow claimants to focus their argumentation on material, rather than procedural issues relevant to proving the merits of the case.

The effects of anthropogenic aerosol, solid or liquid particles suspended in the air, are the biggest contributor to uncertainty in current climate perturbations. Heavy industry sites, such as coal power plants and steel manufacturers, large sources of greenhouse gases, also emit large amounts of aerosol in a small area. This makes them ideal places to study aerosol interactions with radiation and clouds. However, existing data sets of heavy industry locations are either not public, or suffer from reporting gaps. Here, we develop a supervised deep learning algorithm to detect unreported industry sites in high-resolution satellite data, using the existing data sets for training. For the pipeline to be viable at global scale, we employ a two-step approach. The first step uses 10 m resolution data, which is scanned for potential industry sites, before using 1.2 m resolution images to confirm or reject detections. On held-out test data, the models perform well, with the lower resolution one reaching up to 94% accuracy. Deployed to a large test region, the first stage model yields many false positive detections. The second stage, higher resolution model shows promising results at filtering these out, while keeping the true positives, improving the precision to 42% overall, so that human review becomes feasible. In the deployment area, we find five new heavy industry sites which were not in the training data. This demonstrates that the approach can be used to complement existing data sets of heavy industry sites.

In addition to effects from greenhouse gases, climate change is affected by short-lived climate forcers (SLCF). These are often co-emitted with carbon dioxide, and some are regulated air pollutants. In the governance of these pollutants, established estimates of damage costs of pollution inform benefit–cost analyses. However, climate change impact of SLCFs is omitted from these estimates. The purpose of this study is to calculate economic damage costs of air pollutants’ effect on climate change and compare these with established damage costs. Focus is on European emissions governed in the EU National Emission Reduction Commitments Directive during 2020–2050. We use well-known SLCF emission metrics and multiply with literature values on social costs of methane to calculate climate damage costs of SLCFs. The results indicate that average absolute climate damage costs are highest for black carbon ($59,500/ton in 2050) and lowest for nonmethane volatile organic compounds ($661/ton). Our indicative values are likely underestimations. Indicative climate damage costs are usually lower than established damage costs, with notable exceptions. We propose that more detailed studies are necessary, and that inclusion of climate damage costs into economic valuation of SLCFs is important for future air pollution and climate benefit–cost analyses.