Retiring coal power plants can reduce air pollution and health damages. However, the spatial distribution of those impacts remains unclear due to complex power system operations and pollution chemistry and transport. Focusing on coal retirements in Pennsylvania (PA), we analyze six counterfactual scenarios for 2019 that differ in retirement targets (e.g., reducing 50% of coal-based installed capacity vs generation) and priorities (e.g., closing plants with higher cost, closer to Environmental Justice Areas, or with higher CO2 emissions). Using a power system model of the PJM Interconnection, we find that coal retirements in PA shift power generation across PA and Rest of PJM, leading to scenario-varying changes in the plant-level release of air pollutants. Considering pollution transport and the size of the exposed population, these emissions changes, in turn, give rise to a reduction of 6-136 PM2.5-attributable deaths in PJM across the six scenarios, with most reductions occurring in PA. Among our designed scenarios, those that reduce more coal power generation yield greater aggregate health benefits due to air quality improvements in PA and adjacent downwind regions. In addition, comparing across the six scenarios evaluated in this study, vulnerable populations─in both PA and Rest of PJM─benefit most in scenarios that prioritize plant closures near Environmental Justice Areas in PA. These results demonstrate the importance of considering cross-regional linkages and sociodemographics in designing equitable retirement strategies.

We investigate the performance of two widely used chemistry-transport models (CTMs) with different chemical mechanisms in reproducing the ambient maximum daily 8-h average ozone (MDA8 O3) burden over Central Europe. We explore a base case setup with boundary conditions (BC) for meteorology from the ERA-Interim reanalysis and chemical BC from CAM-Chem as well as effects of alterations in these BC based on global model fields. Our results show that changes in meteorological BC strongly affect the correlation with observations but only marginally affect the model biases, while changes in chemical BC increase model biases while correlation patterns remain largely unchanged. Furthermore, our study highlights that CTM choice (and choice of chemical mechanism) has a similar or even larger impact on MDA8 O3 levels as the impact of altered BC. In summary, our study calls for a multi-model strategy combining different CTM and BC combinations to explore the bandwidth of MDA8 O3 distributions and thus uncertainty in hindcasts and future projections, in analogy to climate studies considering ensemble simulations under the same anthropogenic emissions but with slightly different initial conditions.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11869-024-01516-3.

Poor air quality is increasingly linked to gastrointestinal diseases, suggesting a potential correlation with human intestine health. However, this relationship remains largely unexplored due to limited research. This study used a controlled mouse model exposed to cooking oil fumes (COFs) and metagenomics, transcriptomics, and metabolomics to elucidate interactions between intestine microbiota and host metabolism under environmental stress. Our findings reveal that short-term COF inhalation induces pulmonary inflammation within 3 days and leads to gastrointestinal disturbances, elucidating a pathway connecting respiratory exposure to intestinal dysfunction. The exposure intensity significantly correlates with changes in intestinal tissue integrity, microbial composition, and metabolic function. Extended exposure of 7 days disrupts intestine microbiota and alters tryptophan metabolism, with further changes observed after 14 days, highlighting an adaptive response. These results highlight the vulnerability of intestinal health to airborne pollutants and suggest a pathway through which inhaled pollutants may affect distant organ systems.

UNASSIGNED: Cigarette smoking is the major preventable cause of premature deaths in the United States. Attempting to quit smoking is an important step toward smoking cessation. Although it has been studied extensively, limited information on the association between attempts to quit smoking and neighborhood air quality problems is available. Therefore, we examined the association between attempts to quit smoking in the past year and perceived neighborhood air quality problems among adult Texans who smoke.
UNASSIGNED: In 2018, a cross-sectional multistage area probability design-based survey was administered to collect sociodemographic, behavioral, and health-related information from a representative sample of 2050 Texas residents. The current study included 486 adult respondents who reported smoking within the past 12 months. The association between attempts to quit smoking and perceived neighborhood air quality (measured by self-reported problems with neighborhood air quality) was examined using a population-weighted multivariable logistic regression analysis.
UNASSIGNED: Overall, 60.7% of the 486 respondents attempted to quit cigarette smoking. The prevalence of attempting to quit was 74.6% for those reporting perceived neighborhood air quality problems. In the multivariable analysis, a higher likelihood of attempting to quit smoking was found among individuals with perceived neighborhood air quality problems (AOR: 1.906 [1.104-3.289]) and those who were married or living as married (AOR: 1.876 [1.161-3.033]). The likelihood of attempts to quit smoking was lower among males (AOR: 0.629 [0.397-0.995]) and decreased with age (AOR: 0.968 [0.951-0.984]).
UNASSIGNED: The perceived neighborhood air quality problems were found to independently predict attempts to quit cigarette smoking in Texas. To encourage quitting smoking among individuals living in neighborhoods with poor air quality, such neighborhoods should receive tailored and evidence-based interventions to improve community education, social support, and healthcare professionals\' assistance to quit smoking.

Domestic combustion emissions pose a growing risk to public health, especially in the UK. Existing responses are polarised, with government advocating use of lower emission fuels and stoves while clean air campaigners call for blanket bans on burning. However, each approach is limited in its ability to control these emissions. An alternative can be found in the U.S.A., where \'burn alert\' systems require stove and fireplace users to avoid lighting during periods of actual or projected poor air quality. Given the effectiveness of these regimes, the current study designs and evaluates the effectiveness and acceptability of a burn alert system in the UK for the first time, drawing on the theoretical perspective of behavioural responsive regulation. Fifty participants were recruited to use the system over 2 weeks in winter. The findings illustrate that a voluntary burn alert system can dissuade burning among users. Of those in receipt of an alert, 74% reduced burning frequency or burned for a shorter duration. In total, the alert system prevented at least 178 hours of burning for this group. Qualitative findings show that the consistency of the behavioural response is influenced by technical, structural, and environmental factors, providing key insight into how UK-based burn alert systems could be modified to increase the consistency of compliance in future. The overall conclusion is that burn alerts could be introduced in the UK and beyond, as a means of reducing domestic combustion emissions and their associated public health risks.

UNASSIGNED: Wildfires are increasing in magnitude, frequency, and severity. Populations in the wildland-urban interface and in downwind communities are at increased risk of exposure to elevated concentrations of fine particulate matter (PM2.5) and other harmful components of wildfire smoke. We conducted this analysis to evaluate the use of modeled predictions of wildfire smoke to create county-level measures of smoke exposure for public health research and surveillance.
UNASSIGNED: We evaluated four years (2015-2018) of grid-based North American Mesoscale (NAM)-derived PM2.5 forecasts from the U.S. Forest Service BlueSky modeling framework with monitoring data from the Environmental Protection Agency Air Quality System (AQS), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Western Regional Climate Center (WRCC), and the Interagency Real Time Smoke Monitoring (AIRSIS) programs. To assess relationships between model-derived estimates and monitor-based observations, we assessed Spearman\'s correlations by spatial (i.e., county, level of urbanization, states in the western United States impacted by major wildfires, and climate regions) and temporal (i.e., month and wildfire activity periods) characteristics. We then generated county-level smoke estimates and examined spatial and temporal patterns in total and person-days of smoke exposure.
UNASSIGNED: Across all counties in the coterminous United States and for all days, the correlation between county-level model- and monitor-derived PM2.5 estimates was 0.14 (p < 0.001). Correlations were stronger using data from temporary monitors and for areas and days impacted by high wildfire smoke, especially in the western United States. Correlations between county-level model- and monitor-derived estimates in non-metropolitan counties, and at higher concentrations ranged from 0.25 to 0.54 (p < 0.001).
UNASSIGNED: In general, public health practitioners and health researchers need to consider the pros and cons associated with modeled data products for conducting health analyses. Our results support the use of model-derived smoke estimates to identify communities impacted by heavy smoke events, especially during emergency response and for communities located near wildfire episodes.

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo\'s geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Chinese diesel trucks are the main contributors to NOx and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment.

The photolysis of particulate nitrate (pNO3-) has been suggested to be an important source of nitrous acid (HONO) in the troposphere. However, determining the photolysis rate constant of pNO3- (jpNO3-) suffers from high uncertainty. Prior laboratory measurements of jpNO3- using aerosol filters have been complicated by the \"shadow effect\"─a phenomenon of light extinction within aerosol layers that potentially skews these measurements. We developed a method to correct the shadow effect on the photolysis rate constant of pNO3- for HONO production (jpNO3- → HONO) using aerosol filters with identical chemical compositions but different aerosol loadings. We applied the method to quantify jpNO3- → HONO over the North China Plain (NCP) during the winter haze period. After correcting for the shadow effect, the normalized average jpNO3- → HONO at 5 °C increased from 5.89 × 10-6 s-1 to 1.72 × 10-5 s-1. The jpNO3- → HONO decreased with increasing pH and nitrate proportions in PM2.5 and had no correlation with nitrate concentrations. A parametrization for jpNO3- → HONO was developed for model simulation of HONO production in NCP and similar environments.

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