Hazardous Air Pollutants Research Articles

Air Toxics Matter to More than Just Air.

The U.S. EPA regulates hazardous air pollution under the Clean Air Act, designating 188 substances as 'air toxics'. Despite this designation, air pollutants may partition to other environmental compartments and present risks through exposure routes other than inhalation. We use the USEtox multi-media fate model to determine which exposure routes contribute to overall intake fraction and disease risk for 60 air toxics that are present in the model. Inhalation was the dominant exposure route for intake fraction for the majority of air toxics considered, but for 13 cases (>20%), ingestion was dominant, particularly through consumption of above-ground produce. Disease risk showed similar patterns, with a contribution from inhalation of higher than 90% for approximately half of the air toxics considered and higher than 50% for another quarter, but with a dominant contribution from ingestion for the remaining quarter of substances. The results emphasize the continued need for careful communication of chemical risks that reflects complex partitioning and multiple potential exposure routes.

Source apportionment of airborne volatile organic compounds near unconventional oil and gas development

Abstract Oil and natural gas (ONG) extraction activities have been associated with the release of volatile organic compounds (VOCs), including hazardous air pollutants (HAPs) that are known to have adverse health effects and other VOCs that contribute to ozone formation. This study investigated the impact of ONG operations on HAP and other VOC concentrations during the development of multi-well ONG pads in suburban Broomfield, Colorado. Weekly VOC measurements were collected at 18 sites across the community, including locations near the well pads, in adjacent neighborhoods, and at a regional background site. The measurements, from October 2018 to December 2020, encompassed different stages of well pad development, including well drilling, hydraulic fracturing, flowback, and production. Measured concentrations were analyzed using Positive Matrix Factorization (PMF) to characterize VOC sources. Six PMF source factors were identified, including factors associated with combustion, background/biogenic sources, light alkanes, complex alkanes, drilling activities, and ONG acetylene. The factors related to local ONG activities showed clear temporal and spatial correlations with Broomfield well development activities. A benzene source apportionment analysis revealed important source contribution gradients across the community, with ONG-related sources exhibiting the largest influence at locations near the well pads, especially during pre-production activities. Weekly average benzene concentration contributions from ONG-related sources ranged from 9% to 63% at a community background site and from 18% to 89% at a neighborhood site close to a well pad.

Development of a Real-Time NOx Prediction Soft Sensor Algorithm for Power Plants Based on a Hybrid Boost Integration Model

Nitrogen oxides (NOxs) are some of the most important hazardous air pollutants from industry. In China, the annual NOx emission in the waste gas of industrial sources is about 8.957 million tons, while power plants remain the largest anthropogenic source of NOx emissions, and the precise control of NOx in power plants is crucial. However, due to inherent issues with measurement and pipelines in coal-fired power plants, there is typically a delay of about three minutes in NOx measurements, bringing mismatch between its control and measurement. Measuring delays in NOx from power plants can lead to excessive ammonia injection or failure to meet environmental standards for NOx emissions. To address the issue of NOx measurement delays, this study introduced a hybrid boosting model suitable for on-site implementation. The model could serve as a feedforward signal in SCR control, compensating for NOx measurement delays and enabling precise ammonia injection for accurate denitrification in power plants. The model combines generation mechanism and data-driven approaches, enhancing its prediction accuracy through the categorization of time-series data into linear, nonlinear, and exogenous regression components. In this study, a time-based method was proposed for analyzing the correlations between variables in denitration systems and NOx concentrations. This study also introduced a new evaluation indicator, part of R2 (PR2), which focused on the prediction effect at turning points. Finally, the proposed model was applied to actual data from a 330 MW power plant, showing excellent predictive accuracy, particularly for one-minute forecasts. For 3 min prediction, compared to predictions made by ARIMA, the R-squared (R2) and PR2 were increased by 3.6% and 30.6%, respectively, and the mean absolute error (MAE) and mean absolute percentage error (MAPE) were decreased by 9.4% and 9.1%, respectively. These results confirmed the accuracy and applicability of the integrated model for on-site implementation as a 3 min advanced prediction soft sensor in power plants.

Heterogeneous evolution and driving forces of multiple hazardous air pollutants and GHGs emissions from China's primary aluminum industry

The booming of China's primary aluminum industry (PAI) brought substantial emissions of hazardous air pollutants (HAPs) and greenhouse gases (GHGs). By using life cycle assessment and bottom-up method, a comprehensive emission inventory for multiple typical HAPs and GHGs from China's PAI during 1990–2021 was developed and explored for the first time. Our results show that spatial-temporal emissions trends of HAPs and GHGs from PAI in China diverse significantly. The conventional atmospheric pollutants (including SO2, NOx and particulate matter (PM)), fluoride and per fluorinated compound (PFCs) had been effectively suppressed since 2007 due to the implementation of various environmental policies; while, emissions of CO, VOCs, CH4, heavy metals and CO2 had increased at different rates unexpectedly. From the spatial distribution perspective, Henan, Shanxi, Guizhou, Guangxi and Shandong dominated the emissions of PAI in China, but with consumption expansion and environmental constrains, PAI plants start to expand to northwest and southwest areas where are richer in sufficient and cheaper power resources, thus bring significant emission increasing there, particular for conventional atmospheric pollutants in northwest and CO and VOCs in southwest China. By underlying driving forces of PAI emissions, results show that end-of-pipe control measures at various stages have played different roles to reduce emissions of the concerned species at each period, but its reduction effect diminished gradually. Future reduction should seek underlying changes in production technology and energy system. Under constrains of environmental regulation and resource endowment, promoting circular economic development for PAI would be a key strategy to reduce HAPs and GHGs emissions simultaneously in PAI.

A new hybrid deep neural network for multiple sites PM2.5 forecasting

Many studies have confirmed that fine particulate matter (PM2.5) poses significant hazards to both human health and the ecological environment. Predicting future trends in PM2.5 concentrations is crucial for preventing air pollution hazards to human health. However, current machine learning-based forecasting models often focus on specific regions or sites, resulting in poor spatial generalization performance. To address this issue, this study utilizes ERA5 meteorological reanalysis data, MERRA2 assimilated aerosol optical depth (AOD) data, and ground pollutant monitoring site data to construct a three-dimensional dataset. A novel hybrid deep neural network model (D_GAT) is proposed to simultaneously forecast the future 72h PM2.5 concentration trends for all sites in China. The D_GAT model combines the advantages of a graph attention network and a long short-term memory neural network, which can effectively aggregate spatial information between sites and simulate the spatial transport process and time series change characteristics of pollutant particles. Moreover, the proposed calculation method for attention scores quantified based on the actual pollutant propagation characteristics, combined with the upper atmosphere pollution transport process represented by AOD, can better express the pollution contributions of different neighboring sites to the predicted central site. Experimental results show that, compared to four other baseline models, the novel model achieves an average R2 of 0.732 for all sites in 72-h prediction, which is the highest among the five models, and the prediction accuracy of 72h is also in the forefront compared with the current similar research. The RMSE and MAE are 14.63 μg/m3 and 9.49 μg/m3, respectively, which are 2–4 μg/m3 lower than the other four baseline models, indicating that it has higher reliability and can achieve the goal of timely prediction and early warning. Overall, the new model is an effective tool for multiple sites generalization forecasting.

The role of everyday mobility in adaptation to air pollution hazard: A mixed-method approach combining big and traditional data

The empirical study aims to examine how residents perceive and respond to air pollution in their daily lives, whether they use mobility as an adaptation strategy to avoid or mitigate their exposure, and how socioeconomic and demographic factors modify such responses in mobility. To this end, this study conducts an analysis in the city of Chengdu using a mixed-method approach combining surveys and large-scale mobile phone data. It is found that most at-risk individuals take protective measures, and some choose to change mobility patterns to protect themselves from exposure to air pollution. Regression results suggest that engagement with air quality information and the perceived effectiveness of protective measures are the most important predictors of human mobility changes in response to air pollution. The use of mobility as an adaptation strategy occurs despite the availability of in-situ strategies in general, while low-cost and effective in-situ adaptation choices and high-cost mobility strategies are considered as substitutes. Using changes in origin–destination trips in Sichuan generated from 5,393,739 cellphone users in Chengdu, this study reveals that an increase in the difference of the air quality index at origin versus at destination is associated with more trips from the origin to the destination, and travelers are more sensitive to air quality at origin that drives them to escape from the polluted areas. The findings suggest the (re)production of inequality and marginalization of some population groups in hazard adaptation.

Simultaneous capture of trace benzene and SO2 in a robust Ni(II)-pyrazolate framework

Benzene and SO2, coexisting as hazardous air pollutants in some cases, such as in coke oven emissions, have led to detrimental health and environmental effects. Physisorbents offer promise in capturing benzene and SO2, while their performance compromises at low concentration. Particularly, the simultaneous capture of trace benzene and SO2 under humid conditions is attractive but challenging. Here, we address this issue by constructing a robust pyrazolate metal-organic framework (MOF) sorbent featuring rich accessible Ni(II) sites with low affinity to water and good stability. This material achieves a high benzene uptake of 5.08 mmol g–1 at 10 Pa, surpassing previous benchmarks. More importantly, it exhibits an adsorption capacity of ~0.51 mmol g–1 for 10 ppm benzene and ~1.21 mmol g–1 for 250 ppm SO2 under 30% relative humidity. This work demonstrates that a pioneering MOF enables simultaneous capture of trace benzene and SO2, highlighting the potential of physisorbents for industrial effluent remediation, even in the presence of moisture.

Criteria, Greenhouse Gas, and Hazardous Air Pollutant Emissions Factors from Residential Cordwood and Pellet Stoves Using an Integrated Duty Cycle Test Protocol.

Air pollution from residential wood heating (RWH) presents challenges at the intersection of climate and public health. With a revised National Ambient Air Quality Standard (NAAQS, at 9 μg/m3) for particulate matter (PM) in the United States (U.S.), the Environmental Protection Agency (EPA) will likely classify new non-attainment areas due primarily to emissions from RWH. Agencies will use emissions factors (EFs) to develop attainment strategies. Many will rely on EPA modeling platforms based on data from the National Emissions Inventory (NEI). The NEI uses RWH EFs based on data from mid-1990's in-situ studies and a speciation profile from a 2001 study of fireplace emissions. The NEI does not include greenhouse gas (GHG) emissions for this sector, which plays a key role when assessing climate reduction strategies for the buildings sector. Here, we tested seven wood stoves to determine EFs, representing various vintages and control technologies, using a novel test method that reflects in-use operational settings called the Integrated Duty Cycle. The study measured multiple pollutants concurrently: criteria pollutants (particulate matter [PM], CO, and NOx), nonmethane total hydrocarbons (NMTHCs), GHGs, black carbon (eBC), brown carbon (BrC), and multiple hazardous air pollutants (HAPs). We found no significant difference in PM EFs between uncertified and non-catalytic stove technologies. RWH EF results from this study exceeded 2020 NEI RWH EFs for NMTHC and multiple HAPs. Applying our study's EFs to the 2020 NEI suggests that RWH, compared to all other sources, ranks as the 2nd largest source category of formaldehyde; the 3rd largest of benzene, 1,3-butadiene, and acrolein; and the 4th largest of Pb emissions. RWH also emits more methane compared to natural gas or oil residential heating, raising questions about substitution of wood as a climate neutral heating fuel. However, compared to uncertified stoves, pellet stove EFs (except toxic metals) were significantly lower (p < 0.01). In summary, RWH appears to be an underestimated source of PM (non-catalytic technology), methane, NMTHC, toxic metals, and other HAPs, which has important implications for climate and public health policy in the U.S. and globally.

A longitudinal study of volatile organic compounds from cooking under ventilation and purification intervention: Health risk assessment and odor nuisance control

Cooking oil fumes (COFs) expose occupants to hazardous air pollutants and nuisance odors, hence the real controlling effects of different intervention methods deserve to be explored. This longitudinal study conducted year-round field measurements in 10 apartments to explore the synergistic effects of dual ventilation and purification interventions on the exposure level, odor intensity, and health risk control of gaseous organic compounds during cooking. Gas chromatography-mass spectrometry (GC‒MS) and high performance liquid chromatography (HPLC) were used for quantification of volatile organic compounds (VOCs) and carbonyls. A total of 59 VOCs were detected during cooking, and the average ΣVOC concentrations in summer, the transition seasons, and winter were 483.16 μg/m3, 233.97 μg/m3, and 282.82 μg/m3, respectively, and the proportion of aldehydes was approximately 50 %. The small temperature differences between the indoors and outdoors led to a 24 % reduction in the effective air changes per hour (ACH) in the summer compared with that in other seasons. The average estimated carcinogenic risks of formaldehyde, acetaldehyde and benzene were 9.20 × 10−5, 1.11 × 10−5, and 6.05 × 10−6, respectively, for daily cooking conditions. It is difficult to guarantee a healthy cooking environment by mechanical ventilation alone. The inhalation carcinogenic risk of these 3 hazardous VOCs was reduced by approximately 130.9 % under air purification intervention but was still greater than 1E-6. The noncarcinogenic risk of acrolein under the different intervention modes ranged from 69.8 to 84.9. Moreover, acetaldehyde and hexanal were found to be the key odorants in kitchen. When the effective exhaust air rate was greater than 12 m3/min with air purifier in operation, the average inhalation health risk was 1.05–1.78 times that before cooking, and more than half of the users would not smell obvious odors during the cooking process. This study quantifies the effectiveness of the synergetic control strategy of independent purification modules combine with kitchen mechanical ventilation, with a view to implementing a healthy & comfortable living environment.

Exploring the characteristics and source-attributed health risks associated with polycyclic aromatic hydrocarbons and metal elements in atmospheric PM2.5 during warm and cold periods in the northern metropolitan area of Taiwan

Polycyclic aromatic hydrocarbons (PAHs) and metal elements are commonly considered hazardous air pollutants due to their toxic, mutagenic, and carcinogenic properties. However, few studies have simultaneously examined their potential sources and health effects. This study aimed to quantify the PAHs and metal elements in atmospheric PM2.5, investigating their characteristics and potential sources to assess associated health risks in the northern metropolitan area of Taiwan. The measurements indicated that the mean concentrations of total PAHs and metal elements in PM2.5 were 0.97 ± 0.52 ng m−3 and 590 ± 200 ng m−3, respectively. Utilizing the positive matrix factorization profiles, the PAH pollution was classified into two sources: industrial emissions, traffic emissions, and coal combustion (69%) were the predominant sources of PAHs, with petroleum volatilization and biomass burning (31%) making a lesser contribution. Similarly, we traced metal elements to three potential sources: natural sources (48%), a combined source of industrial emissions, coal combustion, and traffic exhaust (32%), and a blend of non-exhaust emissions from traffic and waste incineration sources (20%). Results from the potential source contribution function model suggested that the emissions of PAHs and metals could be influenced by the eastern regions of China, although local sources, including waste incinerators, traffic, shipping, and harbor activities, were identified as the primary contributors. Source-attributed excess cancer risk revealed that industry, traffic, and coal combustion had the highest cancer risk posed by PAHs in the cold period (1.0 × 10−5), while the greatest cancer risk among metal elements was linked to non-exhaust emissions from traffic and waste incineration emissions (2.0 × 10−5). This research underscores the importance of considering source contributions to health risk and emission reduction when addressing PM2.5 pollution. These findings have direct implications for policymakers, providing them with valuable insights to develop strategies that protect public health from the detrimental effects of PAH and metal element exposure.

A modeling framework to assess fenceline monitoring and self-reported upset emissions of benzene from multiple oil refineries in Texas

Benzene as one type of hazardous air pollutants (HAPs) is produced by industrial production processes and/or emitted during upset events caused by man-made or natural accidents. Although upset emissions of benzene can be a significant contributor to the total emission, it is still challenging to quantify. This study first develops a fast modeling framework using obstacle-resolving computational fluid dynamics modeling to compare the modeled within-facility-scale passive pollutant dispersion with the observed levels based on self-reported emissions for fourteen facilities in Texas, United States. Results of numerical simulations demonstrate that neglecting the obstacle effect can underpredict (overpredict) the near-(far-)field concentrations for a low source. For a source located above obstacles, underprediction occurs at all distances. The diagnostic framework is applied to 107 self-reported upset emission events for fourteen petroleum refineries in Texas from year 2019–2022. Considering different metrics across all events, it can be concluded that the modeled concentrations based on self-reported emissions likely underpredict the observed concentration increments. Depending on the possible source height, the median factor of underprediction ranges from 3 to 95 based on the average-plume metric. The agreement between model and observation is better for events characterized by high emission amounts and rates, which also correspond to high observed concentration increments. Overall, the research highlights the importance of considering obstacles and demonstrates the potential application of the current approach as an efficient diagnostic method for self-reported upset emissions using fenceline observations of HAPs.

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.

Al3+ Impregnated Photolithograophically Fabricated Sensor Material La2-xAlxZnO4: Structural Investigation and Acute Gas Sensing Study for Hazardous Air Pollutant Gases and Humidity Assay

Al3+ Impregnated Photolithograophically Fabricated Sensor Material La2-xAlxZnO4: Structural Investigation and Acute Gas Sensing Study for Hazardous Air Pollutant Gases and Humidity Assay

Particulate Matter Capture and Air Pollution Tolerance of Six Roadside Plants in Cheongju, South Korea

Particulate matter (PM), a highly hazardous air pollutant with known adverse health effects, has been proven to be effectively mitigated by using plants. This study aimed to evaluate the capacity of various plants in capturing PM and their tolerance to air pollution. This will facilitate the selection of suitable species for roadside planting. Accordingly, this research quantified the accumulation of particulate matter in six different plant species. Four biochemical parameters [total chlorophyll, leaf pH extract, relative water content, and ascorbic acid] were evaluated to determine the Air Pollution Tolerance Index (APTI), PM accumulation results varied among the plant species. The tolerance level was categorized into four groups: tolerant, moderately tolerant, intermediate, and sensitive. Ligustrum obtusifolium demonstrated the highest PM accumulation and tolerance index values. The presence of leaf hair and roughness on leaf surfaces was observed to facilitate higher PM accumulation in certain plants. PM accumulation on leaves was significantly correlated with biochemical parameters and APTI of plants. Based on these outcomes, Ligustrum obtusifolium, Hibiscus syriacus, and Chamaecyparis obtusa were identified as suitable for roadside planting to mitigate atmospheric particulate matter.

Phytoremediating the air down under: Evaluating airborne particulate matter accumulation by 12 plant species in Australia

AbstractAtmospheric particulate matter (PM) is the most inhaled hazardous air pollutant that can cause adverse health impacts. Plants can remove such contaminants and act as biological filters through phytoremediation. In this study, we screened 12 Australian native species (two deciduous trees, three evergreen shrubs, and seven evergreen trees) growing in three regions to determine their potential in accumulating leaf surface (SPM) and in‐wax PM (WPM). Among the screened species, Lagunaria patersonia (139.22 μg cm−2) was the most effective PM accumulator, followed by Ficus obliqua (131.02 μg cm−2). L. patersonia is an Australian native tree with a dense crown that can efficiently trap PM due to air turbulence between its leaves and branches; broad leaves with a rough texture enhance the plant's ability to trap PM. On the contrary, morphological characteristics like evergreen leaves with hairy appendages may act as an efficient trap for PM in F. obliqua. Due to smoother leaves, the least effective species were F. rubignosa and Eucalyptus saligna. In addition to leaf shape, leaf structures and micromorphology influence PM accumulation. For instance, Pittosporum undulatum accumulated more PM due to its wrinkled and folded leaf structures despite a significantly lower waxes layer. The findings highlight the importance of planting efficient PM accumulator species to shield vulnerable areas from pollution and decrease human exposure to pollutants. The sink capacity of these species can be used in urban tree planning to combat air pollution and improve air quality.

Lung Cancer and Air Quality in a Large Urban County in the United States.

Lung cancer is the leading cancer-related killer in the United States. The incidence varies geographically and may be affected by environmental pollutants. Our goal was to determine associations within time series for specific air pollutants and lung cancer cases over a 33-year period in Wayne County, Michigan, controlling for population change. Lung cancer data for Wayne County were queried from the Michigan Cancer Registry from 1985 to 2018. Air pollutant data were obtained from the United States Environmental Protection Agency from 1980 to 2018. Autoregressive distributed lag (ARDL) models were estimated to investigate time lags in years between specific air pollution levels and lung cancer development. A total of 58,866 cases of lung cancer were identified. The mean age was 67.8 years. Females accounted for 53 percent of all cases in 2018 compared to 44 percent in 1985. Three major clusters of lung cancer incidence were detected with the most intense clusters in downtown Detroit and the heavily industrialized downriver area. Sulfur dioxide (SO2) had the strongest statistically significant relationship with lung cancer, showing both short- and long-term effects (lag range, 1-15 years). Particulate matter (PM2.5) (lag range, 1-3 years) and nitrogen dioxide (NO2) (lag range, 2-4 years) had more immediate effects on lung cancer development compared to carbon monoxide (CO) (lag range, 5-6 years), hazardous air pollutants (HAPs) (lag range, 9 years) and lead (Pb) (lag range, 10-12 years), which had more long-term effects on lung cancer development. Areas with poor air quality may benefit from targeted interventions for lung cancer screening and reductions in environmental pollution.

Downstream natural gas composition across U.S. and Canada: implications for indoor methane leaks and hazardous air pollutant exposures

Previous research has shown that natural gas (NG) leaks from residential appliances are common, affecting greenhouse gas emission inventories and indoor air quality. To study these implications, we collected and analyzed 587 unburned NG samples from 481 residences over 17 North American cities for hydrocarbons, hazardous air pollutants, and organosulfur odorants. Nearly all (97% of) gas samples contained benzene (between-city mean: 2335 ppbv [95% CI: 2104, 2607]) with substantial variability between cities. Vancouver, Los Angeles, Calgary, and Denver had at least 2x higher mean benzene concentrations than other cities sampled, with Vancouver exhibiting a nearly 50x greater mean benzene level than the lowest-concentration city (Boston). We estimate that current U.S. and Canadian emissions inventories are missing an additional 25 000 [95% CI: 19 000, 34 000] and 4000 [95% CI: 3700, 5200] lbs benzene yr−1 through downstream NG leakage, respectively. Concentrations of odorants added for leak detection varied substantially across cities, indicating a lack of standardization. Houston, for instance, had 5x higher mean tert-butyl mercaptan levels than Toronto. Using these odorant measurements, we found that methane emissions as high as 0.0080–0.28 g h−1 and indoor benzene enhancements 0.0096–0.11 ppbv could go undetected by persons with an average sense of smell, with large uncertainties driven by smelling sensitivity, gas composition, and household conditions. We also observed larger leaks (>10 ppm ambient methane) in ∼4% of surveyed homes, confirming that indoor leakage occurs at varying degrees despite the presence of odorants. Overall, our results illustrate the importance of downstream NG composition to understand potential emissions, exposures, and odor-mediated leak detection levels. Given methane’s global warming potency, benzene’s toxicity, and wide variation in smelling abilities, our findings highlight the deficiencies regarding the sole reliance on odorization to alert and protect all occupants from indoor leaks.

The impact of internal climate variability on OH trends between 2005 and 2014

The hydroxyl radical (OH) lies at the nexus of climate and air quality as the primary oxidant for both reactive greenhouse gases and many hazardous air pollutants. To better understand the role of climate variability on spatiotemporal patterns of OH, we utilize a 13-member ensemble of the Community Earth System Model version 2-Whole Atmosphere Community Climate Model version 6 (CESM2-WACCM6), a fully coupled chemistry-climate model, spanning the years 1950–2014. Ensemble members vary only in their initial conditions of the climate state in 1950. We focus on the final decade of the simulation, 2005–2014, when prior studies disagree on the signs of the global OH trends. The ensemble mean global airmass-weighted mean tropospheric column OH ( ΩTOH ), which is an estimate of the forced signal, increases by 0.06%/year between 2005 and 2014 while regional ΩTOH trends range from −0.56%/year over Southern Europe to +0.64%/year over South America. We show that ten-year ΩTOH trends are strongly affected by internal climate variability, as the spread of ΩTOH trends across the ensemble varies between 0.23%/year in Asia and 1.53%/year in South America. We train a fully connected neural network to emulate the ΩTOH simulated by the CESM2-WACCM6 model and combine it with satellite observations to interpret the role of OH chemical proxies. While the OH chemical proxies are subject to internal variability, the impact of internal variability on ΩTOH trends is primarily due to the meteorological parameters except for South America. Forced trends in global mean ΩTOH do not unambiguously emerge from trends driven by internal variability over the 2005–2014 period. The observation-constrained ΩTOH presents opposite trends due to climate variability, resulting in varying conclusions on the attribution of OH to CH4 trends.

Unrecognized volatile and semi-volatile organic compounds from brake wear.

Motor vehicles are among the major sources of pollutants and greenhouse gases in urban areas and a transition to "zero emission vehicles" is underway worldwide. However, emissions associated with brake and tire wear will remain. We show here that previously unrecognized volatile and semi-volatile organic compounds, which have a similarity to biomass burning emissions are emitted during braking. These include greenhouse gases or, these classified as Hazardous Air Pollutants, as well as nitrogen-containing organics, nitrogen oxides and ammonia. The distribution and reactivity of these gaseous emissions are such that they can react in air to form ozone and other secondary pollutants with adverse health and climate consequences. Some of the compounds may prove to be unique markers of brake emissions. At higher temperatures, nucleation and growth of nanoparticles is also observed. Regions with high traffic, which are often disadvantaged communities, as well as commuters can be impacted by these emissions even after combustion-powered vehicles are phased out.

Emission of volatile organic compounds during open fire cooking with wood biomass: Traditional three-stone open fire vs. gasifier cooking stove in rural Kenya

Cooking with wood biomass fuels releases hazardous air pollutants, including volatile organic compounds (VOCs), that often disproportionally affect women and children. This study, conducted in Kwale and Siaya counties in Kenya, employed thermal desorption gas chromatography – mass spectrometry to analyse VOC emissions from cooking with a wood biomass three-stone open fire vs. top-lit updraft gasifier stove. In kitchens with adequate ventilation, total VOC levels increased from 35–252 μg∙m−3 before cooking to 2235–5371 μg∙m−3 during open fire cooking, whereas use of a gasifier stove resulted in reduced emissions from cooking by 48–77 % (506–2778 μg∙m−3). However, in kitchens with poor ventilation, there was only a moderate difference in total VOC levels between the two methods of cooking (9034–9378 μg∙m−3 vs. 6727–8201 μg∙m−3 for the three-stone open fire vs. gasifier stove, respectively). Using a non-target screening approach revealed significantly increased levels of VOCs, particularly benzenoids, oxygenated and heterocyclic compounds, when cooking with the traditional open fire, especially in closed kitchens, highlighting the effects of poor ventilation. Key hazardous VOCs included benzene, naphthalene, phenols and furans, suggesting potential health risks from cooking. In kitchens with good ventilation, use of the gasifier stove markedly reduced emissions of these priority toxic VOCs compared to cooking with an open fire. Thus, substituting open fires with gasifier stoves could help to improve household air quality and alleviate health risks. The study revealed that VOCs were present prior to cooking, possibly originating from previously cooked food (buildup) or the outside environment. VOC emissions were also exacerbated by reduced air flow in high humidity during rainfall, suggesting an area for further research. The findings underscore the importance of adopting cleaner cooking technologies and enhancing kitchen ventilation to mitigate the impacts of VOCs in developing countries.