An association between air pollution and mortality in six U.S. cities

Factors associated with clinical antimicrobial resistance in China: a nationwide analysis

BACKGROUND

Antimicrobial resistance (AMR) represents a critical global health threat, necessitating the identification of factors that contribute to its emergence and proliferation. We used a "One Health" perspective to evaluate the association of human and veterinary antibiotic usage, environmental factors, socio-economic factors, and health care factors with clinical AMR in China.

METHODS

We analyzed data from 31 provincial-level administrative divisions in China, encompassing 20,762,383 bacterial isolates sourced from the China Antimicrobial Resistance Surveillance System dataset between 2014 and 2022. A β regression model was used to explore the relationship of AMR with multiple variables. We also estimated the contribution of factors associated with AMR, and evaluated the avoidable risk of AMR under six different measures during 2019 according to available guidelines.

RESULTS

AMR had positive associations with human antibiotic usage, veterinary antibiotic usage, particulate matter smaller than 2.5 µm (PM2.5) level, population density, gross domestic product per capita, and length of hospital stay, and a 1 unit increase in the level of above independent variables were associated with a percentage change in the aggregate AMR of 1.8% (95% CI: 1.1, 2.5), 2.0% (95% CI: 0.6, 3.4), 0.9% (95% CI: 0.4, 1.4), 0.02% (95% CI: 0.01, 0.03), 0.5% (95% CI: 0.1, 0.8), and 8.0% (95% CI: 1.2, 15.3), respectively. AMR had negative associations with city water popularity, city greenery area per capita, and health expenditure per capita, and a 1 unit increase in the level of above independent variables were associated with a percentage change in the aggregate AMR of -4.2% (95% CI: -6.4, -1.9), -0.4% (95% CI: -0.8, -0.07), and -0.02% (95% CI: -0.04, -0.01), respectively. PM2.5 might be a major influencing factor of AMR, accounting for 13.7% of variation in aggregate AMR. During 2019, there was estimated 5.1% aggregate AMR could be attributed to PM2.5, corresponding to 25.7 thousand premature deaths, 691.8 thousand years of life lost, and 63.9 billion Chinese yuan in the whole country. Human antibiotic usage halved, veterinary antibiotic usage halved, city water popularity improved, city greenery area improved, and comprehensive measures could decrease nationwide aggregate AMR by 8.5, 0.5, 1.3, 4.4, and 17.2%, respectively.

CONCLUSIONS

The study highlights the complex and multi-dimensional nature of AMR in China and finds PM2.5 as a possible major influencing factor. Despite improvements in decreasing AMR, future initiatives should consider integrated strategies to control PM2.5 and other factors simultaneously to decrease AMR.

Air pollution, coronary artery disease, and cardiovascular events: Insights from the PROMISE trial

BACKGROUND

Air pollution is associated with mortality and major adverse cardiovascular events (MACE) in the general population. However, little is known about the relationship between air pollution and coronary artery disease (CAD) and how this relates to MACE.

METHODS

This study utilized data from the computed tomography (CT) arm of the PROMISE trial investigating symptomatic individuals with suspected CAD. We linked levels of air pollutants (PM2·5, PM10, NO2, and ozone) at U.S. zip codes of residence CT-derived CAD and adjudicated MACE (all-cause death, myocardial infarction, and hospitalization for unstable angina). Multivariable analyses were adjusted for the ASCVD risk score and socioeconomic determinants of health. Mediation analyses were used to test putative pathways.

RESULTS

In 4343 individuals (48 ​% males; age: 61 ​± ​8 years), elevated exposures to PM2.5 (≥9.4 ​μg/m3) and NO2 (≥5.3 ​ppb) were independently associated with obstructive CAD (aOR ​= ​1.23, 95%CI: 1.03-1.48, p ​= ​0.024; aOR ​= ​1.56, 95%CI: 1.02-2.40, p ​= ​0.042), while there were no significant associations with PM10 (≥15 ​μg/m3) or ozone (≥51 ​ppb). Increased PM2.5, PM10 and ozone were independently associated with MACE (aHR ​= ​1.56, 95%CI: 1.12-2.18, p ​= ​0.008; aHR ​= ​2.09, 95%CI: 1.18-3.70, p ​= ​0.011, aHR ​= ​1.96, 95%CI: 1.20-3.21, p ​= ​0.008). In the mediation analysis, obstructive CAD accounted for 9 ​% of the total effect (p ​= ​0.012) between PM2.5 and MACE.

CONCLUSION

Exposure to air pollution, particularly PM2.5, was independently associated with obstructive CAD and MACE, with obstructive CAD mediating a small but significant portion of the association between air pollution and MACE.

A high resolution multipollutant assessment of health damages due to the onroad sector in Boston, Massachusetts

Onroad vehicular emissions can adversely affect the health of people both near-road and regionally through exposure to O3, NO2, and PM2.5. While multiple studies have characterized the overall air quality and health benefits of emissions from the transportation sector, fewer studies have modeled the benefits of transportation policies at higher geographic resolution relevant to communities. We used the United States Environmental Protection Agency (U.S. EPA)'s Community Multiscale Air Quality (CMAQ) Version 5.2.1 coupled with the decoupled direct method (DDM) within a nested grid with maximum resolution of 1.33 km × 1.33 km. We predicted O3, NO2, and PM2.5 sensitivities to a large matrix of input parameters concerning five different vehicle classes, five precursors, and six subregions within the Boston metropolitan area (Massachusetts, U.S.). We used the Environmental Benefits Mapping and Analysis Program in R (BenMAPR) to estimate health impacts given concentration-response functions from epidemiological studies, focusing on premature mortalities as well as asthma exacerbations. Based upon the sensitivity matrix, for NO2 and PM2.5, NOx and directly emitted PM2.5 (PPM) have the maximum sensitivity, respectively. O3 is found to be more sensitive to VOC emissions than NOx emissions. We also found that NH3 and SO2 emissions are the next most significant contributors to PM2.5 concentrations after PPM. Our overall findings suggest that approximately 342 premature deaths (95 % CI: 200-465) occur annually in the region due to on-road emissions, with 87 % of these linked to elevated NO2 concentrations. For PM2.5 from PPM, the most densely populated subregion had damages per ton 1.2 to 1.8 times higher than for the other inner core regions and three times higher than suburban regions. Substantial variation in health damages per ton of emissions was observed across precursor pollutants, source regions, and vehicle classes, underscoring the need for targeted emission reduction strategies. This study highlights the importance of high-resolution air quality modeling to accurately capture intra-urban health impacts and inform effective policymaking.

Environmental justice beyond race: Skin tone and exposure to air pollution

Recent research, focused mostly on the United States and Western Europe, shows that marginalized communities often face greater environmental degradation. However, the ethnoracial categories used in these studies may not fully capture environmental inequality in the Global South. Moving beyond conventional ethnoracial variables, this study presents findings exploring the link between skin tone and fine particulate matter (PM2.5) exposure in Colombia. By matching household geolocations from a large-scale longitudinal survey with satellite-based PM2.5 estimates, we find that skin tone predicts both initial pollution exposure levels and their changes over time. Although average exposure levels remained stable during our study period, the environmental justice (EJ) landscape in Colombia contemporaneously underwent a complete transformation. In 2010, lighter-skinned individuals faced higher PM2.5 exposure, but darker-skinned individuals experienced steeper increases in the following years. By 2016, the EJ gap had reversed, with people with the darkest skin tones exposed to PM2.5 levels nearly one SD higher than those faced by people with the lightest skin tones. These patterns remain robust when controlling for a comprehensive set of theoretically relevant covariates, including ethnoracial self-identification and income. Disproportionate exposure to pollution from fires partially explains the observed disparities. Decomposition analysis shows that this variable, local collective action, and economic marginalization account for a sizeable share of the EJ gap. However, one-third of the gap remains unexplained by observable characteristics. With climate change intensifying fire incidence, the disproportionate disease burdens that vulnerable groups face might deepen unless policy measures are taken to reverse this trend.

Low-level PM 2.5 Exposure, Cardiovascular and Nonaccidental Mortality, and Related Health Disparities in 12 US States

BACKGROUND

Investigations into long-term fine particulate matter (PM 2.5 ) exposure's impact on nonaccidental and cardiovascular (CVD) deaths primarily involve nonrepresentative adult populations at concentrations above the new Environmental Protection Agency annual PM 2.5 standard.

METHODS

Using generalized linear models, we studied PM 2.5 exposure on rates of five mortality outcomes (all nonaccidental, CVD, myocardial infarction, stroke, and congestive heart failure) in 12 US states from 2000 to 2016. We aggregated predicted annual PM 2.5 exposures from a validated ensemble exposure model, ambient temperature from Daymet predictions, and mortality rates to all census tract-years within the states. We obtained covariates from the decennial Census and the American Community Surveys and assessed effect measure modification by race and education with stratification.

RESULTS

For each 1-µg/m 3 increase in annual PM 2.5 , we found positive associations with all five mortality outcomes: all nonaccidental (1.08%; 95% confidence interval [CI]: 0.96%, 1.20%), all CVD (1.27%; 95% CI: 1.14%, 1.41%), myocardial infarction (1.89%; 95% CI: 1.67%, 2.11%), stroke (1.08%; 95% CI: 0.87%, 1.30%), and congestive heart failure (2.20%; 95% CI: 1.97%, 2.44%). Positive associations persisted at <8 µg/m 3 PM 2.5 levels and among populations with only under 65. In our study, race, but not education, modifies associations. High-educated Black had a 2.90% larger increased risk of CVD mortality (95% CI: 2.42%, 3.39%) compared with low-educated non-Black.

CONCLUSION

Long-term PM 2.5 exposure is associated with nonaccidental and CVD mortality in 12 states, below the new Environmental Protection Agency standard, for both low PM 2.5 regions and the general population. Vulnerability to CVD mortality persists among Black individuals regardless of education level.

Urban emissions of fine and ultrafine particulate matter in Los Angeles: Sources and variations in lung-deposited surface area

Airborne particulate matter (PM) in urban environments poses significant health risks by penetrating the respiratory system, with concern over lung-deposited surface area (LDSA) as an indicator of particle exposure. This study aimed to investigate the diurnal trends and sources of LDSA, particle number concentration (PNC), elemental carbon (EC), and organic carbon (OC) concentrations in Los Angeles across different seasons to provide a comprehensive understanding of the contributions from primary and secondary sources of ultrafine particles (UFPs). Hourly measurements of PNC and LDSA were conducted using the DiSCmini and Scanning Mobility Particle Sizer (SMPS), while OC and EC concentrations were measured using the Sunset Lab EC/OC Monitor. The results showed distinct diurnal trends in PNC and EC, with peaks occurring in the early morning and evening, which were consistent with periods of increased traffic volume. During warmer periods, a midday increase in PNC was observed, attributed to photochemical reactions. In contrast, a nighttime peak during colder months suggested the formation of secondary aerosols through aqueous-phase chemistry. Additionally, the DiSCmini consistently reported higher LDSA values than SMPS, indicating the presence of irregularly shaped UFPs, particularly during periods of heavy traffic flow. Positive Matrix Factorization (PMF) analysis identified three primary sources. Factor 1 (photochemically influenced processes), driven by secondary organic aerosol formation during warmer periods, contributed to 19% of LDSA. Factor 2, in which primarily traffic influenced emissions were the dominant contributor, accounting for 70% of LDSA and associated with high loadings of OC (61%), EC (78%), and NOx (94%). Factor 3 (aqueous phase secondary process influenced emissions) during colder months, accounted for 11% of LDSA. Both Factor 1 and 3 sources exhibited comparable contributions of OC4 (52% and 48%, respectively), underscoring their roles in secondary aerosol formation. These findings emphasize the need to address both primary and secondary emissions to mitigate health risks associated with UFP exposure.

Environmental pollutants and atherosclerosis: Epigenetic mechanisms linking genetic risk and disease

Over the past half-century, significant strides have been made to identify key risk factors, genetic mechanisms, and treatments for atherosclerosis. Yet, coronary artery disease (CAD) remains a leading global public health challenge. While the heritability of CAD is well-documented, there is increasing focus on the role of environmental exposures, such as smoking, air pollution, and heavy metals, on global CAD risk. Recent research has shed light on the interplay between genetic variation and environmental factors, offering insights into gene-environment (GxE) interactions. Moreover, emerging evidence suggests that environmental toxicants can profoundly impact the epigenome, altering gene regulation beyond the genetic sequence itself, revealing novel mechanisms underlying disease. Epigenetic changes - such as modifications in DNA methylation, chromatin structure, and non-coding RNA function - are now recognized as key molecular determinants of atherosclerosis. These observations have created a foundational paradigm that environment, genetics, and epigenetic mechanisms influence risk through a highly complex interaction regulating cellular phenotype, pathology, and disease progression. In this review, we explore the mechanisms by which environmental exposures influence the epigenome and contribute to the regulation of atherosclerotic disease. Additionally, we examine the transgenerational epigenetic effects of these exposures on disease risk. Advancing our understanding of these mechanisms is essential for informing public health strategies aimed at mitigating harmful environmental exposures and reducing the global burden of cardiovascular disease.

Investigating associations between the physical living environment and hippocampus in adulthood and older age

It is by now well known that the physical living environment has a major impact on people's life, but the neural structures involved in this relationship remain to be explored. Most studies investigating this relationship only focus on single environmental predictors. In order to understand how the multitude of factors constituting the living environment relate to brain structure we used data from the UK Biobank (n = 21,094; age Mean = 63.35 years; SD = 7.46; range = 45-82) to examine how individuals' immediate characteristics around the home address (e.g., green space; air pollution in the neighborhood) are associated with hippocampal volume, a brain region known to be highly plastic. We accounted for common demographic factors that have been shown to be associated with brain structure and known factors such as sex, income, education, and age. We made use of an analytical paradigm based on the feature importance estimation and recursive feature elimination with decision tree ensembles as well as linear regression analysis. Results identified a subset of environmental measures (e.g., pollution, green space, noise) most strongly associated with hippocampal volume across adulthood. Findings highlight the importance of the environment for individuals' brain structure.

Ambient Air Pollution and Parkinson's Disease and Alzheimer's Disease: An Updated Meta-Analysis

BACKGROUND

Previous epidemiological evidence regarding the associations between ambient air pollution and two major neurodegenerative diseases, Alzheimer's disease (AD) and Parkinson's disease (PD), remains inconclusive.

OBJECTIVE

This study aimed to evaluate the associations between long-term and short-term exposure to PM2.5 and PM10 (i.e., particulate matter with an aerodynamic diameter of, or smaller than, 2.5 μm or 10 μm), nitrogen dioxide (NO2), ozone, sulfur dioxide, and carbon monoxide and the risks of AD and PD.

METHODS

A random-effects model was used to summarize individual effect estimates in the meta-analysis. A subgroup meta-analysis was further conducted to explore the potential sources of heterogeneity.

RESULTS

In total, 42 eligible studies were included. For each 5 μg/m3 increase in long-term PM2.5 exposure, the odds ratios (ORs) were 1.16 (95% CI: 1.04, 1.30; I2 = 95%) and 1.10 (95% CI: 1.03, 1.17; I2 = 95%) for AD and PD, respectively. For each 5 μg/m3 increase in short-term PM2.5 exposure, the OR was 1.01 (95% CI: 1.002, 1.01; I2 = 77%) for PD. For each 1 ppb increase in long-term NO2 exposure, the OR was 1.01 (95% CI: 1.0002, 1.02; I2 = 79%) for PD.

CONCLUSION

Ambient air pollution, particularly PM2.5, may contribute to the increased risks of neurodegenerative diseases including AD and PD.

Assessing air quality extremes: a comparative extreme value analysis of metropolitan cities across India and the world

Air pollution is a significant global issue that impacts public health, particularly in urban areas where pollution levels often exceed safe limits. The Air Quality Index (AQI) serves as a key metric to assess the concentration of harmful pollutants such as particulate matter (PM), ozone, and nitrogen oxides. This study conducts an extreme value analysis (EVA) of AQI data from five major Indian cities-Delhi, Mumbai, Kolkata, Chennai, and Hyderabad-and eight other metropolitan cities worldwide, including Dhaka, Chengdu, and Bogota. The goal is to evaluate the probability of extreme pollution events and compare the seasonal patterns of air quality in these cities. Our findings indicate that cities like New Delhi and Dhaka consistently experience AQI levels that exceed hazardous thresholds, particularly during the winter months and festival seasons. This study provides critical insights into the air quality crisis in India and other regions, emphasizing the need for targeted policy interventions, including stricter emission regulations, adoption of cleaner energy sources, and enhanced public awareness campaigns to mitigate the effects of extreme pollution events.

Behavioral Responses to Wildfire Smoke: A Case Study in Western Montana

Although climate change is increasing wildfire and smoke events globally, public health messaging and individual access to resources for protection are limited. Individual interventions can be highly effective at reducing wildfire smoke exposure. However, studies related to individual responses to wildfire smoke are limited and demonstrate mixed protective behaviors and risk perception. Our research helps fill this gap by assessing the self-reported behavior of 20 participants during wildfire season in Western Montana from 28 June through 1 November, 2022. We also measured continuous outdoor and indoor fine particulate matter (PM2.5) concentrations at participant residencies during this time period using PurpleAir sensors (PAII-SD, PurpleAir, Inc, USA) while participants took up to 16 self-reported online weekly activity surveys. Mixed-effect Poisson regression models were used to assess associations between exposure variables and participant reported behaviors. These results were compared with end-of-study interview findings. Wildfire smoke impacted days and increased concentrations of PM2.5 were associated with decreased outdoor exercise and opening of windows for ventilation. Interview themes were congruent with the regression analysis, with the additional finding of high portable air cleaner (PAC) use among participants. Additionally, these interviews gave context to both the tradeoffs participants face when making protective decisions and the importance of personal air quality data in increasing awareness about wildfire smoke risks. Future wildfire smoke studies can build off this research by providing personally relevant air quality data and PACs to participants and by improving public health messaging to address the compounding risks of wildfire smoke exposure and heat.

All-Cause and Cause-Specific Mortality Associated with Long-Term Exposure to Fine Particulate Matter in Japan: The Ibaraki Prefectural Health Study

AIMS

Long-term exposure to fine particulate matter (PM2.5) is causally associated with mortality and cardiovascular disease. However, in terms of cardiovascular cause-specific outcomes, there are fewer studies about stroke than about coronary heart disease, particularly in Asia. Furthermore, there remains uncertainty regarding the PM2.5-respiratory disease association. We examined whether long-term exposure to PM2.5 is associated with all-cause, cardiovascular and respiratory disease mortality in Japan.

METHODS

We used data of 46,974 participants (19,707 men; 27,267 women), who were enrolled in 2009 and followed up until 2019, in a community-based prospective cohort study (the second cohort of the Ibaraki Prefectural Health Study). We estimated PM2.5 concentrations using the inverse distance weighing methods based on ambient air monitoring data, and assigned each participant to administrative area level concentrations. A Cox proportional hazard model was applied to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of mortality.

RESULTS

During the average follow-up of 10 years, we confirmed 2,789 all-cause deaths. All outcomes including stroke mortality did not significantly increase as the PM2.5 concentration increased. For non-malignant respiratory disease mortality, the multivariable adjusted HR per 1 µg/m3 increase in the PM2.5 concentration was 1.09 (95% CI = 0.97-1.23).

CONCLUSIONS

In this population exposed to PM2.5 at concentrations of 8.3-13.1 µg/m3, there was no evidence that long-term exposure to PM2.5 had adverse effects on mortality. Weak evidence of positive association observed for non-malignant respiratory disease mortality needs further studies in other populations.

Reliea® combination of Codonopsis lanceolata and Chaenomeles sinensis extract alleviates airway inflammation on particulate matter 10 plus diesel exhaust particles (PM10D) ‑induced respiratory disease mouse model

Particulate matter (PM, diameter < 10 μm) and Diesel exhaust particles (DEP) exposure can cause severe respiratory disorders. This investigation explored the protective effects of Reliea® (RelA), combination of Codonopsis lanceolata and Chaenomeles sinensis extract, against airway inflammation related to PM10D exposure. RelA treatment suppressed reactive oxygen species, nitric oxide release, cytokine expression (IL-6, IL-1β, iNOS, CXCL-2, MCP-1, and TNF-α), and the related inflammatory mechanisms in PM10-induced alveolar macrophage cells. BALB/c mice were injected with PM10D via intranasal trachea three times over a period of 12 days and RelA were orally dispensed for 12 days. RelA inhibited infiltrating neutrophils, total number of immunocytes in lung and bronchoalveolar lavage fluid (BALF). RelA decreased the expression of interleukin (IL)-17, chemokine (C-X-C motif) ligand (CXCL)-1, thymus and activation-regulated chemokine, macrophage inflammatory protein-2, IL-1α, TNF-α, mucin 5AC, cyclooxygenase-2, and transient receptor potential cation channel subfamily A or V member 1 in BALF and lung, and inhibited IL-1α and macrophage marker F4/80 localization in lung of PM10D-induced mice. RelA treatment decreased serum symmetric dimethyl arginine levels. RelA restored histopathological damage via inhibition of NF-κB and MAPK pathways in the trachea and lung. Lancemaside A and protocatechuic acid as major active compounds of RelA was identified. In addition, RelA showed better expectoration through increased phenol red secretion. These results indicate that Reliea® combination of C. lanceolata and C. sinensis extract might be effective in prevention and treatment of airway inflammation and respiratory diseases.

Key drivers and source mechanisms of oxidative potential in fine particles from an industrial city of Northern China Plain

The oxidative potential (OP) of particulate matter (PM) is crucial for understanding its ability to generate reactive oxygen species. However, the major chemical drivers influencing OP still need to be better understood. This study investigated the seasonal variations of OP and identified key drivers and source mechanisms in the industrial city of Zibo, located in North China Plain. We used the XGBoost model and Positive Matrix Factorization (PMF) to identify key drivers and source mechanisms. In 2022, PM2.5 samples were collected from an urban site in Zibo, and major chemical components were analyzed. OP was quantified using the dithiothreitol (DTT) method. The results revealed that the annual average DTTv in Zibo City for 2022 was 1.1 nmol/min/m3, with the highest DTTv levels observed in autumn, followed by spring, summer, and winter. Using the XGBoost model, we identified that metal elements such as Pb, Ba, and Cu, along with water-soluble ions NO3- and SO42-, significantly contributed to DTTv. Source apportionment analysis via PMF identified five major sources of PM2.5. Throughout the study period, secondary particles were the predominant contributors to PM2.5 (49 %), while coal combustion had the lowest contribution (7 %). To further elucidate the sources of OP in PM2.5, we integrated the measured OP with source contributions derived from PMF. The findings indicated that secondary particles and industrial sources contributed the most to DTTv, accounting for 40 % and 21 %, respectively. The OP sources exhibited seasonal variations: secondary particles were the primary contributors in winter, while dust sources dominated in spring. In summer, vehicle emissions increased substantially, and industrial emissions became the major source in autumn. This study highlighted the critical drivers and source mechanisms of OP in industrial cities and would be beneficial for future air quality control and risk reduction.

Environmental PM2.5 Exposure: An Ignored Factor Associated with Blood Cadmium Level in Hemodialysis Patients

Background

The negative impacts of particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) are well known. Patients undergoing maintenance hemodialysis (HD) have significantly higher blood cadmium levels (BCLs) than healthy individuals. As elemental cadmium can be found in the PM2.5 particle fraction, we conducted this study to assess the effect of environmental PM2.5 exposure and other clinical variables on BCLs in maintenance HD patients.

Patient and Methods

 This cross-sectional study included 754 hD patients who had previously participated in a BCL study. Demographic, hematological, biochemical and dialysis-related data were collected for analysis. For each patient, the mean PM2.5 concentrations in the living environment during the previous 12 and 24 months were recorded and analyzed.

Results

Of all patients, the median BCL of was 0.36 µg/L (range: 0.21, 0.79 µg/L). The mean PM2.5 concentration was 28.45 ± 3.57 μg/m3 during the 12 months and 29.81 ± 3.47 μg/m3 during the 24 months, respectively. From a multivariate linear regression analysis, log BCL was positively associated with the mean PM2.5 concentration during the previous 12 and 24 months. In addition, log BCL was positively associated with the number of days with PM2.5 concentrations above the standard level during the previous 12 and 24 months. Moreover, according to the tertiles of days with a daily mean PM2.5 concentration above the normal limit in the previous 24 months, patients with the highest exposure days exhibited a significantly higher BCL than those in the other two patient groups.

Conclusion

Chronic environmental exposure to PM2.5 is significantly associated with BCLs in maintenance HD patients, and exposure to PM2.5-bound cadmium may contribute to the harmful effects on health in this population. Further studies are needed to confirm these observations and to explore the underlying mechanisms.

Willingness to pay to reduce mortality risks from particulate matter and value of statistical life: Evidence from stated air purifier rental choices in South Korea

This paper investigates individuals' averting behavior that utilizes a durable good, high-functioning air purifiers, to reduce risk from exposure to coarse (PM10) and fine (PM2.5) particulate matter, to estimate a value of statistical life (VSL) for use in benefit-cost analysis in South Korea. We present an interactive risk ladder, developed specifically for this study, to 1218 respondents in a national web-based contingent valuation survey to elicit their perceived risks from the exposure to PM10 and PM2.5 with and without the use of high-functioning air purifiers (i.e., averting behavior). Respondents' stated preferences for high-functioning air purifiers for both PM10 and PM2.5 were, as expected, influenced by both rental price increases and perceived risk reductions, as well as respondent's attitudes, experience, and demographics. The risk/rent trade-offs implied by contingent discrete choices suggest consumers are willing to pay (WTP) high rent premiums ($30/month for PM10 risk reductions and $62-$64/month for PM2.5 risk reductions) for small perceived risk reductions. Annualized WTP measures for perceived risk reductions of PM10 and PM2.5 imply VSL estimates in the range of $1.4-$2.5 million USD ($2018).

A forecasting tool for optimized emission control strategies to achieve short-term air quality attainment

Optimizing an emergency air pollution control strategy for haze events presents a significant challenge due to the extensive computational demands required to quantify the complex nonlinearity associated with controls on diverse air pollutants and regional sources. In this study, we developed a forecasting tool for emergency air pollution control strategies based on a predictive response surface model that quantifies PM2.5 responses to emission changes from different pollutants and regions. This tool is equipped to assess the effectiveness of emergency control measures corresponding to various air pollution alerts and to formulate an optimized control strategy aimed at specific PM2.5 targets. A case study in the Yangtze River Delta demonstrates that our tool can conduct assessments and generate optimized control strategies for the forthcoming seven to ten days within a 6-h window. Results indicate that the haze event on November 3rd, 2017, was predominantly attributable to regional transport, while the episode on November 7th-8th resulted more from local emissions. The optimized control strategy for November 3rd involves coordinated control from 17 cities along the northwest regional transport pathway, whereas 9 cities around Shanghai should implement emergency emission reductions for PM2.5 attainment in Shanghai on November 7th-8th. Additionally, the intensity of air pollution alerts is higher in the optimized strategy for November 3rd. The forecasting tool developed in this study can quickly and accurately assess the effectiveness of pollution emergency reduction plans and formulate optimal control strategies in advance, which is of great significance for enhancing the emergency response capabilities of authorities to address short-term air pollution events effectively.

A novel hybrid model for air quality prediction via dimension reduction and error correction techniques

The monitoring of air pollution through the air quality index (AQI) is a fundamental tool in ensuring public health protection. Accurate prediction of air quality is necessary for the timely implementation of measures to control and manage air pollution, thereby mitigating its detrimental impact on human health. A novel hybrid prediction model is proposed, which is EMD-KMC-EC-SSA-VMD-LSTM. Raw AQI index data are decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD) method. Subsequently, sample entropy (SE) is utilized to assess the intricacy of IMFs, and K-means clustering (KMC) is used to reconstruct them into joint intrinsic mode functions (Co-IMFs). Then, the variational mode decomposition (VMD) is used to transform the complex Co-IMF0 into simpler IMFs. Long short-term memory (LSTM), optimized either by the Sparrow Search Algorithm (SSA), is applied to forecast all IMFs, generating the first prediction sequence. To further refine the forecasting, an error correction (EC) technique is adopted. The error sequence is obtained by subtracting the forecasting sequence from the raw sequence, which is then decomposed by EMD-SSA-VMD. Subsequently, SSA-LSTM is engaged to forecast the decomposed error sequence, generating the error forecasting sequence. Finally, the forecast outcomes are combined with the error predictions to generate the final AQI prediction sequence. The proposed approach undergoes validation across four urban centers and undergoes comparison against a set of eight prediction models. Experimental findings underscore the heightened precision of this hybrid forecasting model in predicting AQI metrics.

Changes in traffic-related air pollution exposures and associations with adverse birth outcomes over 20 years in Texas

BACKGROUND

Billions of dollars have been spent implementing regulations to reduce traffic-related air pollution (TRAP) from exhaust pipe emissions. However, few health studies have evaluated the change in TRAP emissions and associations with infant health outcomes. We hypothesize that the magnitude of association between vehicle exposure measures and adverse birth outcomes has decreased over time, parallelling regulatory improvements in exhaust pipe emissions.

METHODS

Using birth records in Texas from 1996 to 2016, we calculated residential exposure measures related to TRAP: nitrogen dioxide (NO2, a marker of the TRAP mixture), vehicle miles travelled within 500 m of homes (VMT500), a measure of traffic volume, and highway proximity. Using an accountability study framework, our analysis examined term birthweight, term low birthweight (TLBW) (<2500 g), preterm birth (PTB) (<37 weeks) and very preterm birth (VPTB) (<32 weeks). We implemented linear and logistic regression models to examine overall and time-stratified associations, including trends by race/ethnicity and socioeconomic groups.

RESULTS

Among exposures for 6 158 518 births, NO2 exposures decreased 59% over time but VMT500 remained relatively stable. TRAP-related exposure measures were persistently associated with harmful birth outcomes [e.g. OR1996-2016 of 1.07 (95% CI: 1.04, 1.08) for TLBW comparing the highest vs lowest NO2 quintile]. The magnitude of associations decreased for total VMT500 and TLBW (-60%, OR1996: 1.08 to OR2016: 1.03 for the highest vs lowest quintile) and PTB (-65%) and VTPT (-61%), but not for term birthweight.

CONCLUSIONS

We observed evidence of small improvements in birth outcomes associated with reductions in exhaust pipe emissions over a 20-year period in Texas.

Climate Policy Reduces Racial Disparities in Air Pollution from Transportation and Power Generation

Energy system optimization models facilitate analyses on a national or regional scale. However, understanding the impacts of climate policy on specific populations requires a much higher spatial resolution. Here, we link an energy system optimization model to an integrated assessment model via an emission downscaling algorithm, translating air pollution emissions from nine U.S. regions to U.S. counties. We simulate the impacts of six distinct policy scenarios, including a current policy and a 2050 net-zero target, on NOx, SO2, and PM2.5 emissions from on-road transportation and electricity generation. We compare different policies based on their ability to reduce emission exposure and exposure disparity across racial groups, allowing decision-makers to assess the air pollution impacts of various policy instruments more holistically. Modeled policies include a clean electricity standard, an on-road ICE vehicle ban, a carbon tax, and a scenario that reaches net-zero GHG emissions by 2050. While exposure and disparities decrease in all scenarios, our results reveal persistent disparities until at least 2040, particularly for Black non-Hispanic Americans. Our estimates of avoided deaths due to air pollution emphasize the importance of policy timing, showing that thousands of lives can be saved by taking action in the near-term.

Exposure of RAW264.7 macrophages to exhaust emissions (gases and PAH) and non-exhaust emissions (tire particles) induces additive or synergistic TNF-α production depending on the tire particle size

Road traffic is a major contributor to air pollution and consequently negatively affects human health. Car pollution originates both from exhaust emissions (EE) and non-exhaust emissions (NEE, such as tire and brake wear particles, erosion of road surfaces and resuspension of road dust). While the toxicity of EE and NEE has been characterized separately, their combined effects are poorly documented. However, we are constantly exposed to a mixture of pollutants and their interactions should not be neglected as they may significantly impact their toxicological profile resulting in additive, synergistic or antagonistic effects. To fill this gap, we investigated in vitro the combined toxicity of exhaust gases and benzo[a]pyrene (representative of EE) and tire particles (representative of NEE). Macrophages from the RAW264.7 cell line were exposed for 24 h to tire particles (TP) of variable size (6-113 µm), alone or in combination with exhaust gases (CO2, CO, NO, NO2) and benzo[a]pyrene (B[a]P) as an archetype of polycyclic aromatic hydrocarbon (PAH). The cell response was assessed in terms of cytotoxicity, proinflammatory response and oxidative stress. TP, gases and B[a]P, alone or in combination triggered neither cytotoxicity nor oxidative stress. On the contrary, a proinflammatory response was elicited with two different profiles depending on the size of the TP: TNF-α production was either slightly (with the finest TP) or strongly (with coarse TP) increased in the presence of gases and B[a]P, suggesting that the effects of TP, gases and B[a]P were either additive or synergistic, depending on TP size.

Toxicity, mutagenicity, and source identification of polycyclic aromatic hydrocarbons in ambient atmosphere and flue gas

This study aimed to assess the characteristics of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs) from various stationary and mobile emission sources in Taiwan, with a focus on source apportionment and associated health risks. The northern power plant, equipped with bag filters operating at 150 °C, had significantly lower FPM and CPM levels (0.44 and 0.13 mg/m3, respectively) compared to the central and southern power plants, which used electrostatic precipitators operating at 250 °C (FPM, 1.45-8.35 mg/m3; CPM, 2.37-3.73 mg/m3). Additionally, emissions from diesel vehicles under both idle and high-speed conditions exhibited higher FPM levels (3.46-4.67 mg/m3) than gasoline vehicles (0.19-0.40 mg/m3). In terms of PAH toxicity, diesel vehicle emissions had significantly higher BaP-TEQ (87.3 ng/m3) and BaP-MEQ (25.9 ng/m3) levels compared to power plants (BaP-TEQ, 5.49 ng/m3; BaP-MEQ, 2.65 ng/m3). The highest ambient concentrations of PM2.5, BaP-TEQ, and BaP-MEQ were recorded at traffic sites, with values of 48 ± 36 µg/m3, 0.29 ng/m3, and 0.11 ng/m3, respectively. Differences in PAH distributions between stationary and mobile sources were influenced by factors such as pollution control technologies, combustion temperatures, and fuel types. Diesel vehicle emissions were dominated by benzo[g,h,i]perylene (BghiP), indeno[1,2,3-cd]pyrene (IND), benzo[a]pyrene (BaP), and benzo[b]fluoranthene (BbF) under idle conditions, while phenanthrene (PA), pyrene (Pyr), and BghiP were prevalent under high-speed conditions. Source apportionment conducted using principal component analysis (PCA) and positive matrix factorization (PMF) identified diesel and gasoline vehicles as the dominant contributors to atmospheric PAHs in Taiwan, accounting for 38% of the total, followed by coal-fired power plants at 35%. The highest lifetime excess cancer risk (ECR) of 2.5 × 10⁻5 was observed in traffic-dense areas, emphasizing the public health implications of vehicle emissions. The study adds credibility to the source apportionment findings, and the health risk analysis highlights variations across different regions, including traffic, urban, rural, and background zones.

Estimating the effect of annual PM2·5 exposure on mortality in India: a difference-in-differences approach

BACKGROUND

In 2019, the Global Burden of Diseases, Injuries, and Risk Factors Study attributed 0·98 million deaths to ambient air pollution in India based on potentially inappropriate exposure-response functions from countries with low air pollution levels. Instead, using data from India, we investigated long-term exposure to PM2·5 and all-cause mortality with a causal inference method.

METHODS

We collected national counts of annual mortality from 2009 to 2019 from the Civil Registration System at the district level to calculate annual district-level mortality rate as our main outcome and obtained annual PM2·5 concentrations from a high-resolution spatiotemporal model. We applied an extended version of the difference-in-differences design by use of generalised additive models with quasi-Poisson distribution, including indicator variables and separate time trends for spatial administrative divisions. PM2·5 concentrations obtained at 1 km × 1 km spatial resolution across the country were used to calculate annual district-level mean PM2·5 concentrations. Similarly, we collected confounders at the district level, such as mean and SD of quarterly temperatures, gross domestic product per capita, population aged 60 years or older, clean cooking fuel usage, literacy in women, and median age. The spatial unit of analysis was administrative division.

FINDINGS

The annual median population-weighted PM2·5 was 38·9 μg/m3 (5-95th percentile 19·7-71·8 μg/m3). The full population lived in areas with PM2·5 concentrations exceeding the 5 μg/m3 annual mean recommended in the WHO guidelines, and 1·1 billion of 1·4 billion (81·9% of the total population) lived in areas above the Indian National Ambient Air Quality Standards for annual mean PM2·5 not exceeding 40 μg/m3. A 10 μg/m3 increase in annual PM2·5 concentration was associated with an 8·6% (95% CI 6·4-10·8) higher annual mortality. Based on the Indian National Ambient Air Quality Standards, a total of 3·8 million (95% CI 2·9-4·9) deaths between 2009 and 2019 were attributable to PM2·5, amounting to 5·0% (3·8-6·4) of total mortality. Based on the WHO guidelines, a total of 16·6 million (13·0-21·8) deaths were attributable to PM2·5, amounting to 24·9% (19·5-32·5) of total mortality.

INTERPRETATION

Our difference-in-differences approach allowed us to assess the full extent of registered deaths in the most populated country in the world, which has high levels of air pollution. We provide new evidence of increased mortality risk from long-term PM2·5, which emphasises the need for tighter regulatory standards to potentially substantially reduce mortality across India.

FUNDING

Swedish Research Council for Sustainable Development.

Assessment of long-term exposure to traffic-related air pollution: An exposure framework

BACKGROUND

Exposure to ambient air pollution is associated with morbidity and mortality, making it an important public health concern. Emissions from motorized traffic are a common source of air pollution but evaluating the contribution of traffic-related air pollution (TRAP) emissions to health risks is challenging because it is difficult to disentangle the contribution of individual air pollution sources to exposure contrasts in an epidemiological study.

OBJECTIVE

This paper describes a new framework to identify whether air pollution differences reflect contrasts in TRAP exposures. Because no commonly measured pollutant is entirely specific to on-road motor vehicles, this exposure framework combined information on pollutants, spatial scale (i.e., geographic extent), and exposure assessment methods and their spatial scale to determine whether the estimated effect of air pollution in a given study was related to differences in TRAP.

METHODS

The exposure framework extended beyond the near-road environment to include differences in exposure to TRAP at neighborhood resolution ( ≤ 5 km) across urban, regional, and national scales. It also embedded a stricter set of criteria to identify studies that provided the strongest evidence that exposure contrasts were related to differences in traffic emissions.

RESULTS

Application of the framework to the transparent selection of epidemiological studies for a systematic review produced insights on assessing and improving comparability of TRAP exposure measures, particularly for indirect measures such as distances from roads. It also highlighted study design challenges related to the duration of measurements and the structure of epidemiological models.

IMPACT STATEMENT

This manuscript describes a new exposure framework to identify studies of traffic-related air pollution, a case study of its application in an HEI systematic review, and its implications for exposure science and air pollution epidemiology experts. It identifies challenges and provides recommendations for the field going forward. It is important to bring this information to the attention of researchers in air pollution exposure science and epidemiology because applying the broader lessons learned will improve the conduct and reporting of studies going forward.

Short-term air pollution and greenness exposures on oxidative stress in urban and peri-urban residents in Beijing: A part of AIRLESS study

BACKGROUND

Exposure to air pollution has been associated with increased risks of cardiopulmonary diseases, cancer, and mortality, whereas residing near green spaces may reduce the risks. However, limited research explores their combined effect on oxidative stress.

METHODS

A total of 251 participants with multi-time measurements were included in the longitudinal-designed study. Personal gaseous air pollutants (CO, NO, NO2, and O3,) and particulate pollution (PM1, PM2.5, and PM10) were measured and followed in two 7-day windows while ambient exposure levels and urine samples were collected simultaneously. Participants' Normalized Difference Vegetation Index (NDVI) was estimated and used to represent greenness exposure. Urinary oxidative stress biomarkers include free malondialdehyde (MDA), total MDA, and 8-hydroxydeoxyguanosine (8-OHdG). Linear mixed-effects models were used to independently and jointly estimate the associations of greenness and air pollution with oxidative stress biomarkers.

RESULTS

We found consistent positive associations of personal ozone (O3) exposure with 8-OHdG percent changes, and this association was modified by gender and outdoor activity frequency. Consistent positive associations of personal lag 2-day carbon monoxide (CO) exposure with the percent changes of the three oxidative stress biomarkers were significant. We additionally observed that individuals who lived in greener areas had lower levels of urinary-free and total MDA. Participants in the highest NDVI tertile had 0.38 and 0.46 lower free and total MDA levels, [95 % CI: (-0.70, -0.05) and (-0.78, -0.13)], compared to the lowest NDVI tertile. There was also evidence indicating the modification effects by area, education, and outdoor activity frequency on associations between NDVI exposure and creatinine adjusted free MDA (all Pfor interaction < 0.05). Additional greenness modification effects on personal O3 exposure with urinary 8-OHdG was observed.

CONCLUSION

Our study provides biological evidence of the modification effect of the built environment on the impact of air pollution.

The last decade of air pollution epidemiology and the challenges of quantitative risk assessment

Epidemiologic research and quantitative risk assessment play a crucial role in transferring fundamental scientific knowledge to policymakers so they can take action to reduce the burden of ambient air pollution. This commentary addresses several challenges in quantitative risk assessment of air pollution that require close attention. The background to this discussion provides a summary of and conclusions from the epidemiological evidence on ambient air pollution and health outcomes accumulated since the 1990s. We focus on identifying relevant exposure-health outcome pairs, the associated concentration-response functions to be applied in a risk assessment, and several caveats in their application. We propose a structured and comprehensive framework for assessing the evidence levels associated with each exposure-health outcome pair within a health impact assessment context. Specific issues regarding the use of global or regional concentration-response functions, their shape, and the range of applicability are discussed.

Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank

Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. IMPACT: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM2.5) exposures to the UK Biobank cohort.

The construction and validity assessment of the respiratory air quality health index (AQHI) based on the analytic hierarchy process in Tianjin, China

BACKGROUND

Air quality health index (AQHI), as a developed air quality risk communication tool, has been proved to be more accurate in predicting air quality related health risks than air quality index (AQI) by previous studies. However, the standard method to construct AQHI is summing the excess risks of single-pollutant models directly, which may ignore the joint effect of air pollutant mixtures.

METHODS

In this study, a new method which could solve the aforementioned problem, Analytic hierarchy process (AHP), was introduced. Based on this method, we constructed the respiratory health related AQHI using years of life lost (YLL) as indicator of health outcome and compared its validity with AQI.

RESULTS

There was a correlation between daily AQI and AQHI in 2019 (R2 = 0.830, P < 0.01), and the chi-square test between the two excellent rates showed a statistically significant difference (χ2 = 4.156, P < 0.05). Both AQI and AQHI were correlated with the daily respiratory YLL (P < 0.01), however, the coefficient of AQHI was larger than those of AQI.

CONCLUSIONS

This study indicated that compared with AQI, the constructed AQHI based on AHP may predict the health risk of air pollution more effectively. AHP may become a new method to construct AQHI which needs to be proved by taking into consideration by more studies.

Particulate Matter-Induced Emerging Health Effects Associated with Oxidative Stress and Inflammation

Environmental pollution continues to increase with industrial development and has become a threat to human health. Atmospheric particulate matter (PM) was designated as a Group 1 carcinogen by the International Agency for Research on Cancer in 2013 and is an emerging global environmental risk factor that is a major cause of death related to cardiovascular and respiratory diseases. PM is a complex composed of highly reactive organic matter, chemicals, and metal components, which mainly cause excessive production of reactive oxygen species (ROS) that can lead to DNA and cell damage, endoplasmic reticulum stress, inflammatory responses, atherosclerosis, and airway remodeling, contributing to an increased susceptibility to and the exacerbation of various diseases and infections. PM has various effects on human health depending on the particle size, physical and chemical characteristics, source, and exposure period. PM smaller than 5 μm can penetrate and accumulate in the alveoli and circulatory system, causing harmful effects on the respiratory system, cardiovascular system, skin, and brain. In this review, we describe the relationship and mechanism of ROS-mediated cell damage, oxidative stress, and inflammatory responses caused by PM and the health effects on major organs, as well as comprehensively discuss the harmfulness of PM.

Causal analysis of air pollution mixtures: estimands, positivity, and extrapolation

Causal inference for air pollution mixtures is an increasingly important issue with appreciable challenges. When the exposure is a multivariate mixture, there are many exposure contrasts that may be of nominal interest for causal effect estimation, but the complex joint mixture distribution often renders observed data extremely limited in their ability to inform estimates of many commonly defined causal effects. We use potential outcomes to (1) define causal effects of air pollution mixtures, (2) formalize the key assumption of mixture positivity required for estimation, and (3) offer diagnostic metrics for positivity violations in the mixture setting that allow researchers to assess the extent to which data can actually support estimation of mixture effects of interest. For settings where there is limited empirical support, we redefine causal estimands that apportion causal effects according to whether they can be directly informed by observed data versus rely entirely on model extrapolation, isolating key sources of information on the causal effect of an air pollution mixture. The ideas are deployed to assess the ability of a national US data set on the chemical components of ambient particulate matter air pollution to support estimation of a variety of causal mixture effects. This article is part of a Special Collection on Environmental Epidemiology.

A PM2.5 pollution-level adaptive air filtration system based on elastic filters for reducing energy consumption

Exacerbated by human activities and natural events, air pollution poses severe health risks, requiring effective control measures to ensure healthy living environments. Traditional filtration systems that employ high-efficiency particulate air (HEPA) filters are capable of effectively removing particulate matter (PM) in indoor environments. However, these systems often work without considering the fluctuations in air pollution levels, leading to high energy consumption. This study proposed a novel PM2.5 pollution-level adaptive air filtration system that combined elastic thermoplastic polyurethane (TPU) filters and an Internet of Things (IoT) system. The developed system can effectively adjust its filtration performance (i.e., pressure drop and PM2.5 filtration efficiency) in response to real-time air quality conditions by mechanically altering the structures of TPU filters. Furthermore, while operating in varied pollution conditions, the proposed system demonstrated remarkable reductions in pressure drop without notably compromising the pollution control capability. Finally, the energy consumption of the pollution-level adaptive air filtration system was estimated when applied in mechanical ventilation systems in different cities (Hong Kong, Beijing, and Xi'an) with various pollution conditions. The results revealed that, compared to a traditional fixed system, the annual energy consumption could be reduced by up to ∼26.4 % in Hong Kong.

Association of Ambient Air Pollution Exposure With Incident Glaucoma: 12-Year Evidence From the UK Biobank Cohort

Purpose

Glaucoma is the leading cause of irreversible blindness worldwide. Despite growing concerns about air quality and its impact on ocular health, there remains a knowledge gap regarding the long-term association between air pollution and glaucoma risk. This study investigates the relationship between exposure to ambient air pollution and incidence of glaucoma.

Methods

In this prospective study, we used land use regression models to estimate levels of various air pollutants, including fine particulate matter (PM2.5), PM2.5 absorbance, PM2.5-10, PM10, nitrogen dioxide (NO2), and nitrogen oxides (NOx). Incidents of glaucoma were ascertained through routinely collected hospital admission records. Multivariate Cox proportional hazards models were used to examine the associations between air pollution exposure and glaucoma incidence, adjusting for potential confounding sociodemographic, physical, and lifestyle factors.

Results

Data from 481,113 participants were included. Over a median follow-up of 12.8 years, 9224 incident cases of glaucoma were identified. In the maximally adjusted model, per interquartile range increase in PM2.5 was associated with a 3% greater risk of developing glaucoma (hazard ratio [HR] = 1.03, 95% confidence interval [CI] = 1.00 to 1.06, P = 0.048). Participants in the highest quartile had a 10% increased risk of developing glaucoma compared to those in the lowest quartile (HR = 1.10, 95% CI = 1.03 to 1.17, P = 0.005).

Conclusions

Higher levels of exposure to ambient air pollutants, particularly PM2.5, are associated with an increased risk of developing glaucoma. These results highlight the potential public health impact of ambient air pollution on glaucoma risk and underscore the urgent need for further research into targeted environmental interventions in this domain.

Air pollution and incident sarcoidosis in central Pennsylvania

Sarcoidosis is a chronic granulomatous disease predominantly affecting the lungs and inducing significant morbidity and elevated mortality rate. The etiology of the disease is unknown but may involve exposure to an antigenic agent and subsequent inflammatory response resulting in granuloma formation. Various environmental and occupational risk factors have been suggested by previous observations, such as moldy environments, insecticides, and bird breeding. Our study investigated the association of air pollution with diagnosis of sarcoidosis using a case-control design. Penn State Health electronic medical records from 2005 to 2018 were examined for adult patients with (cases) and without (controls) an International Classification of Disease (ICD)-9 or -10 code for sarcoidosis. Patient addresses were geocoded and 24-hr residential-level air pollution concentrations were estimated using spatio-temporal models of particulate matter <2.5 μm (PM2.5), ozone, and PM2.5 elemental carbon (EC) and moving averages calculated. In total, 877 cases and 34,510 controls were identified. Logistic regression analysis did not identify significant associations between sarcoidosis incidence and air pollution exposure estimates. However, the odds ratio (OR) for EC for exposures occurring 7-10 years prior did approach statistical significance, and ORs exhibited an increasing trend for longer averaging periods. Data suggested a latency period of more than 6 years for PM2.5 and EC for reasons that are unclear. Overall, results for PM2.5 and EC suggest that long-term exposure to traffic-related air pollution may contribute to the development of sarcoidosis and emphasize the need for additional research and, if the present findings are substantiated, for public health interventions addressing air quality as well as increasing disease surveillance in areas with a large burden of PM2.5 and EC.

Indigenized Characterization Factors for Health Damage Due to Ambient PM2.5 in Life Cycle Impact Assessment in China

Life cycle assessment (LCA) is a broadly used method for quantifying environmental impacts, and life cycle impact assessment (LCIA) is an important step as well as a major source of uncertainties in LCA. Characterization factors (CFs) are pivotal elements in LCIA models. In China, the health loss due to ambient PM2.5 is an important aspect of LCIA results, which, however, is generally assessed by adopting CFs developed by global models and there remains a need to integrate localized considerations and the latest information for more precise applications in China. In this study, we developed indigenized CFs for LCIA of health damage due to ambient PM2.5 in China by coupling the atmospheric chemical transport model GEOS-Chem, exposure-response model GEMM containing Chinese cohort studies, and the latest local data. Results show that CFs of four major PM2.5 precursors all exhibit significant interregional variation and monthly differences in China. Our results were generally an order of magnitude higher and show disparate spatial distribution compared to CFs currently in use, suggesting that the health damage due to ambient PM2.5 was underestimated in LCIA in China, and indigenized CFs need to be adopted for more accurate results in LCIA and LCA studies.

YouTube as a Source of Information on Air Pollution: Significance for Community Health

Air pollution is a pervasive global public health threat. The purpose of this study was to assess the content of 100 widely viewed English language YouTube videos on air pollution using the search term 'air pollution.' Content categories were created using comprehensive fact sheets and were coded as being present or not in each video. Cumulatively, the 100 videos sampled received 32,826,294 views and 303,692 likes, with averages of 328,263 and 3,068 respectively. The majority of videos (n = 72) were created by broadcast or internet-based news, 12 of the videos were created by professionals, 7 were created by a consumer and 9 were created by others. Three kinds of content were featured in a majority (> 50%) of the videos: "sources of pollution," "environmental impacts," and "solutions offered" and the videos covering each of these topics collectively garnered more than 26 million views and 249,000 likes. Independent one-tailed t-tests (α = 0.05) showed an association between covering sources of pollution and the number of views and likes. For both, videos featuring this content had a higher average number of views (467,391.08 vs. 80,924.03, p = .0383) and likes (4,450.78 vs. 647.03, p = .0383). Additionally, videos showing environmental impacts received more views than those that did not (547,901.49 vs. 80,585.43, p = .0373). This research can serve as a starting point to describe information being conveyed about an important global public health problem. Future research is needed to improve understanding about ways to utilize YouTube and other social media for public health education and advocacy to increase consumers' awareness and facilitate the informed decision-making process.

Electronic Spectrum of α-Hydrofulvenyl Radical (C6H7), and a Simple and Accurate Recipe for Predicting Adiabatic Ionization Energies of Resonance-Stabilized Hydrocarbon Radicals

Using a combination of resonant two-photon two-color ionization (R2C2PI) and laser-induced fluorescence/dispersed fluorescence spectroscopy, we have examined the A ~  2A″ ← X ~  2A″ transition of the resonance-stabilized α-hydrofulvenyl radical, produced from methylcyclopentadiene dimer in a jet-cooled discharge. Like the related 1,4-pentadienyl and cyclohexadienyl radicals, the α-hydrofulvenyl Ã-state lifetime is orders of magnitude shorter than the predicted f-value implies, indicative of rapid nonradiative decay. The transition is fully allowed by symmetry but considerably weakened by transition moment interference. Intensity borrowing among a' modes brings about static (i.e., Condon) and vibronic (i.e., Herzberg-Teller) moments of similar size, the result being a spectrum substantially less origin-dominated than is usually observed for extensively delocalized radicals. Twenty A ~ -state modes and twelve X ~ -state modes are identified with high confidence and assignments for several others are suggested. In addition, from a series of two-color appearance potential scans with the A ~ -state zero-point level serving as an intermediate, we obtain a field-free adiabatic ionization energy (AIE) of 7.012(1) eV. For a set of 21 resonance-stabilized radicals bearing 5 to 11 carbon atoms, it emerges that the field-free AIE obtained by R2C2PI methods under jet-cooled conditions lies very close to the average of B3LYP/6-311G++(d,p) (with harmonic zero-point energy) and CBS-QB3 0 K calculations, with a mean absolute deviation of only 0.010(7) eV (approximately 1 kJ/mol). On average, this represents a nearly 10-fold improvement in accuracy over CBS-QB3 predictions for the same set of radicals.

Effect of PM2.5 on burden of mortality from non-communicable diseases in northern Thailand

Background

Particulate pollution, especially PM2.5from biomass burning, affects public and human health in northern Thailand during the dry season. Therefore, PM2.5exposure increases non-communicable disease incidence and mortality. This study examined the relationship between PM2.5and NCD mortality, including heart disease, hypertension, chronic lung disease, stroke, and diabetes, in northern Thailand during 2017-2021.

Methods

The analysis utilized accurate PM2.5data from the MERRA2 reanalysis, along with ground-based PM2.5measurements from the Pollution Control Department and mortality data from the Division of Non-Communicable Disease, Thailand. The cross-correlation and spearman coefficient were utilized for the time-lag, and direction of the relationship between PM2.5and mortality from NCDs, respectively. The Hazard Quotient (HQ) was used to quantify the health risk of PM2.5to people in northern Thailand.

Results

High PM2.5 risk was observed in March, with peak PM2.5concentration reaching 100 µg/m3, with maximum HQ values of 1.78 ± 0.13 to 4.25 ± 0.35 and 1.45 ± 0.11 to 3.46 ± 0.29 for males and females, respectively. Hypertension significantly correlated with PM2.5levels, followed by chronic lung disease and diabetes. The cross-correlation analysis showed a strong relationship between hypertansion mortality and PM2.5at a two-year time lag in Chiang Mai (0.73) (CI [-0.43-0.98], p-value of 0.0270) and a modest relationship with chronic lung disease at Lampang (0.33) (a four-year time lag). The results from spearman correlation analysis showed that PM2.5concentrations were associated with diabetes mortality in Chiang Mai, with a coefficient of 0.9 (CI [0.09-0.99], p-value of 0.03704). Lampang and Phayao had significant associations between PM2.5 and heart disease, with coefficients of 0.97 (CI [0.66-0.99], p-value of 0.0048) and 0.90 (CI [0.09-0.99], p-value of 0.0374), respectively, whereas Phrae had a high coefficient of 0.99 on stroke.

Elucidating gas-surface interactions relevant to atmospheric particle growth using combined temperature programmed desorption and temperature-dependent uptake

Understanding growth mechanisms for particles in air is fundamental to developing a predictive capability for their impacts on human health, visibility, and climate. In the case of highly viscous semi-solid or solid particles, the likelihood of impinging gases being taken up to grow the particle will be influenced by the initial uptake coefficient and by the residence time of the adsorbed gas on the surface. Here, a new approach that combines Knudsen cell capabilities for gas uptake measurements with temperature programmed desorption (TPD) for binding energy measurements of gases is described. The application of this unique capability to the uptake of organic gases on silica demonstrates its utility and the combination of thermodynamic and kinetic data that can be obtained. Lower limits to the initial net uptake coefficients at 170 K are (3.0 ± 0.6) × 10-3, (4.9 ± 0.6) × 10-3 and (4.3 ± 0.8) × 10-3 for benzene, 1-chloropentane, and methanol, respectively, and are reported here for the first time. The uptake data demonstrated that the ideal gas lattice model was appropriate, which informed the analysis of the TPD data. From the thermal desorption measurements, desorption energies of 34.6 ± 2.5, 45.8 ± 5.5, and 40.0 ± 5.6 kJ mol-1 (errors are 1σ) are obtained for benzene, 1-chloropentane, and methanol, respectively, and show good agreement with previously reported measurements. A multiphase kinetics model was applied to quantify uptake, desorption, and diffusion through the particle multilayers and hence extract desorption kinetics. Implications for uptake of organics on silica surfaces in the atmosphere and the utility of this system for determining relationships between residence times of organic gases and particle surfaces of varying composition are discussed in the context of developing quantitative predictions for growth of aerosol particles in air.

Household air pollution disparities between socioeconomic groups in Chicago

Purpose: To assess household air pollution levels in urban Chicago households and examine how socioeconomic factors influence these levels. Methods: We deployed wireless air monitoring devices to 244 households in a diverse population in Chicago to continuously record household fine particulate matter (PM2.5) concentration. We calculated hourly average PM2.5 concentration in a 24-hour cycle. Four factors-race, household income, area deprivation, and exposure to smoking-were considered in this study. Results: A total of 93085 h of exposure data were recorded. The average household PM2.5 concentration was 43.8 μg m-3. We observed a significant difference in the average household PM2.5 concentrations between Black/African American and non-Black/African American households (46.3 versus 31.6 μg m-3), between high-income and low-income households (18.2 versus 52.5 μg m-3), and between smoking and non-smoking households (69.7 versus 29.0 μg m-3). However, no significant difference was observed between households in less and more deprived areas (43.7 versus 43.0 μg m-3). Implications: Household air pollution levels in Chicago households are much higher than the recommended level, challenging the hypothesis that household air quality is adequate for populations in high income nations. Our results indicate that it is the personal characteristics of participants, rather than the macro environments, that lead to observed differences in household air pollution.

Hyper-local to regional exposure contrast of source-resolved PM2.5 components across the contiguous United States: implications for health assessment

BACKGROUND

Improved understanding of sources and processes that drive exposure contrast of fine particulate matter (PM2.5) is essential for designing and interpreting epidemiological study outcomes.

OBJECTIVE

We investigate the contribution of various sources and processes to PM2.5 exposure contrasts at different spatial scales across the continental United States.

METHODS

We consider three cases: exposure contrast within a metro area, nationwide exposure contrast with high spatial resolution, and nationwide exposure contrast with low spatial resolution. Using national empirical model estimates of source- and chemically specific PM2.5 concentration predictions, we quantified the contribution of various sources and processes to PM2.5 exposure contrasts in these three cases.

RESULTS

At the metro level (i.e., metropolitan statistical area; MSA), exposure contrasts of PM2.5 vary between -1.8 to 1.4 µg m-3 relative to the MSA-mean with about 50% of within-MSA exposure contrast of PM2.5 caused by cooking and mobile source primary PM2.5. For the national exposure contrast at low-resolution (i.e., using MSA-average mean concentrations), exposure contrasts (relative to the national mean: -3.9 to 3.2 µg m-3) are larger than within an MSA with ~80% of the variation due to secondary PM2.5. National exposure contrast at high resolution (census block) has the largest absolute range (relative to the national mean: -4.7 to 3.7 µg m-3) due to both regional and intra-urban contributions; on average, 65% of the national exposure contrast is due to secondary PM2.5 with the remaining from the primary PM2.5 (cooking and mobile source 26%, other 9%).

IMPACT

Our study provides a comprehensive analysis of the sources and processes that contribute to exposure contrasts of PM2.5 across different geographic areas in the US. For the first time on a national scale, we used high spatial resolution source-specific exposure estimates to identify the primary contributors to PM2.5 exposure contrasts. The study also highlights the advantages of different study designs for investigating the health impacts of specific PM2.5 components. The findings provide novel insights that can inform public health policies aimed at reducing PM2.5 exposure and advance the understanding of the epidemiological study outcomes.

Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (β: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (β:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Recent advances in mass spectrometry techniques for atmospheric chemistry research on molecular-level

The Earth's atmosphere is composed of an enormous variety of chemical species associated with trace gases and aerosol particles whose composition and chemistry have critical impacts on the Earth's climate, air quality, and human health. Mass spectrometry analysis as a powerful and popular analytical technique has been widely developed and applied in atmospheric chemistry for decades. Mass spectrometry allows for effective detection, identification, and quantification of a broad range of organic and inorganic chemical species with high sensitivity and resolution. In this review, we summarize recently developed mass spectrometry techniques, methods, and applications in atmospheric chemistry research in the past several years on molecular-level. Specifically, new developments of ion-molecule reactors, various soft ionization methods, and unique coupling with separation techniques are highlighted. The new mass spectrometry applications in laboratory studies and field measurements focused on improving the detection limits for traditional and emerging volatile organic compounds, characterizing multiphase highly oxygenated molecules, and monitoring particle bulk and surface compositions.

Assessing and anticipating environmental challenges in Lahore, Pakistan: future implications of air pollution on sustainable development and environmental governance

Urban environment and air quality are changing primarily due to land use land cover (LULC) changes, economic activity, and urbanization. Air pollution has been increasingly acknowledged as a major issue for cities due to its extensive effects on health and well-being. As the second most populous city in the country, Lahore faces alarming levels of air pollutants, which induced this study to focus on the pervasive issue of air pollution in Lahore. For this, the study collected air pollutants data from the Environmental Protection Department of Punjab and analyzed them using the ARIMA model. In the research results, both the observed data and predictive models uncovered concerning trends in pollutant concentrations, ultimately portraying a concerning picture for air quality management. Carbon monoxide (CO) levels show a consistent rise, surpassing Pakistan's environmental standards by 2025. Similarly, nitrogen dioxide (NO2) concentrations escalate, exceeding prescribed standards. Ground-level ozone (O3) also demonstrates a substantial increase, surpassing standards by 2025. Both PM2.5 and PM10 exhibit marked upward trends, projected to exceed recommended limits, particularly PM10 throughout the study year. The Air Quality Index exhibits an observable upward trend, fluctuating between 70 and 442 from 2015 to 2020. Similarly, a positive correlation was found between population growth and land use conversion into residential areas. Projections suggest a continuous increase, potentially hitting a severe level of 500 during winter by 2025. These findings point to an impending air pollution crisis, demanding urgent action to address the hazardous situation in the city. The study recommends that urban air pollution should be reduced, and the negative health effects of air pollution should be minimized using vegetation barriers, screens, and greening initiatives. Strict regulations and monitoring initiatives need to be put in place in big cities to monitor pollution and vegetation.

Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods

BACKGROUND

Lung disease is a severe problem in the United States. Despite the decreasing rates of cigarette smoking, chronic obstructive pulmonary disease (COPD) continues to be a health burden in the United States. In this paper, we focus on COPD in the United States from 2016 to 2019.

OBJECTIVE

We gathered a diverse set of non-personally identifiable information from public data sources to better understand and predict COPD rates at the core-based statistical area (CBSA) level in the United States. Our objective was to compare linear models with machine learning models to obtain the most accurate and interpretable model of COPD.

METHODS

We integrated non-personally identifiable information from multiple Centers for Disease Control and Prevention sources and used them to analyze COPD with different types of methods. We included cigarette smoking, a well-known contributing factor, and race/ethnicity because health disparities among different races and ethnicities in the United States are also well known. The models also included the air quality index, education, employment, and economic variables. We fitted models with both multiple linear regression and machine learning methods.

RESULTS

The most accurate multiple linear regression model has variance explained of 81.1%, mean absolute error of 0.591, and symmetric mean absolute percentage error of 9.666. The most accurate machine learning model has variance explained of 85.7%, mean absolute error of 0.456, and symmetric mean absolute percentage error of 6.956. Overall, cigarette smoking and household income are the strongest predictor variables. Moderately strong predictors include education level and unemployment level, as well as American Indian or Alaska Native, Black, and Hispanic population percentages, all measured at the CBSA level.

CONCLUSIONS

This research highlights the importance of using diverse data sources as well as multiple methods to understand and predict COPD. The most accurate model was a gradient boosted tree, which captured nonlinearities in a model whose accuracy is superior to the best multiple linear regression. Our interpretable models suggest ways that individual predictor variables can be used in tailored interventions aimed at decreasing COPD rates in specific demographic and ethnographic communities. Gaps in understanding the health impacts of poor air quality, particularly in relation to climate change, suggest a need for further research to design interventions and improve public health.

Monitoring and Ensuring Worker Health in Controlled Environments Using Economical Particle Sensors

Nowadays, indoor air quality monitoring has become an issue of great importance, especially in industrial spaces and laboratories where materials are handled that may release particles into the air that are harmful to health. This study focuses on the monitoring of air quality and particle concentration using low-cost sensors (LCSs). To carry out this work, particulate matter (PM) monitoring sensors were used, in controlled conditions, specifically focusing on particle classifications with PM2.5 and PM10 diameters: the Nova SDS011, the Sensirion SEN54, the DFRobot SEN0460, and the Sensirion SPS30, for which an adapted environmental chamber was built, and gaged using the Temtop M2000 2nd as a reference sensor (SRef). The main objective was to preliminarily assess the performance of the sensors, to select the most suitable ones for future research and their possible use in different work environments. The monitoring of PM2.5 and PM10 particles is essential to ensure the health of workers and avoid possible illnesses. This study is based on the comparison of the selected LCS with the SRef and the results of the comparison based on statistics. The results showed variations in the precision and accuracy of the LCS as opposed to the SRef. Additionally, it was found that the Sensirion SEN54 was the most suitable and valuable tool to be used to maintain a safe working environment and would contribute significantly to the protection of the workers' health.

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Plasma proteome mediate the impact of PM2.5 on stroke: A 2-step Mendelian randomization study

The objectives of this study were to measure the mediation effect of plasma proteins and to clarify their mediating role in the relationship between stroke risk and particulate matter 2.5 (PM2.5) exposure. The possible mediating role of plasma proteins on the causative link between PM2.5 exposure and stroke incidence were examined using a two-step Mendelian randomization (MR) approach based on two-sample Mendelian randomization (TSMR). The findings revealed a significant positive causal relationship between PM2.5 exposure and stroke, with an inverse variance weighted odds ratio of 1.219 (95 % CI: 1.002 - 1.482, P < 0.05). Additionally, a positive causal association was identified between PM2.5 exposure and several plasma proteins, including FAM134B, SAP, ITGB7, Elafin, and DCLK3. Among these, FAM134B, ITGB7, Elafin, and DCLK3 also demonstrated a positive causal association with stroke, whereas only SAP was found to be negatively causally associated with stroke. Remarkably, four plasma proteins, namely DCLK3, FAM134B, Elafin, and ITGB7, were identified as mediators, accounting for substantial proportions (14.5 %, 13.6 %, 11.1 %, and 9.9 %) of the causal association between PM2.5 and stroke. These results remained robust across various sensitivity analyses. Consequently, the study highlights the significant and independent impact of PM2.5 on stroke risk and identifies specific plasma proteins as potential targets for preventive interventions against PM2.5-induced stroke.

Urban spatial structure and air quality in the United States: Evidence from a longitudinal approach

Previous studies on the relationship between urban form and air quality: (1) report mixed results among specific aspects of urban spatial structure (e.g., urban expansion, form, or shape) and (2) use primarily cross-sectional approaches with a single year of data. This study takes advantage of a multi-decade, longitudinal approach to investigate the impact of urban spatial structure on population-weighted concentrations of PM2.5 and NO2. Based on fixed-effect regression models for 481 urban areas in the United States spanning from 1990 to 2015, we found significant associations between various aspects of urban spatial structure and air quality after controlling for meteorological and socio-economic factors. Our results show that population density, compact urban form, circularity, and green space are associated with lower concentrations. Conversely, higher rates of urban expansion, industrial area, and polycentricity are associated with higher concentrations. For large cities (total population: 180,262,404), we found that increasing key factors from each urban spatial structure category (i.e., greenness, population density, compactness, circularity) by a modest 10% results in 10,387 (12,376) fewer deaths for PM2.5 (NO2). We recommend that policymakers adopt comprehensive strategies to increase population density, compactness, and green spaces while slowing urban expansion to reduce the health burden of air quality in US cities.

Ultrafine Particles and Hospital Visits for Chronic Lower Respiratory Diseases in New York State

Rationale: Exposure to particulate matter is associated with various adverse health outcomes. Ultrafine particles (UFPs; diameter <0.1 μm) are a unique public health challenge because of their size. However, limited studies have examined their impacts on human health, especially across seasons and demographic characteristics. Objectives: To evaluate the effect of UFP exposure on the risk of visiting the emergency department (ED) for a chronic lower respiratory disease (CLRD) in New York State in 2013-2018. Methods: We used a case-crossover design and conditional logistic regression to estimate how UFP exposure led to CLRD-related ED visits. GEOS-Chem Advanced Particle Microphysics, a state-of-the-art chemical transport model with a size-resolved particle microphysics model, generated air pollution simulation data. We then matched UFP exposure estimates to geocoded health records for asthma, bronchiectasis, chronic bronchitis, emphysema, unspecified bronchitis, and other chronic airway obstructions in New York State from 2013 through 2018. In addition, we assessed interactions with age, ethnicity, race, sex, meteorological factors, and season. Results: Each 1-(interquartile range [IQR]) increase in UFP exposure led to a 0.37% increased risk of a respiratory-related ED visit on lag 0-0, or the day of the ED visits, (95% confidence interval [CI], 0.23-0.52%) and a 1.81% increase on lag 0-6, or 6 days before the ED visit, (95% CI, 1.58-2.03%). The highest risk was in the emphysema subtype (lag 0-5, 4.18%; 95% CI, 0.16-8.37%), followed by asthma (lag 0-6, 2.00%), chronic bronchitis (lag 0-6, 1.78%), other chronic airway obstructions (lag 0-6, 1.60%), and unspecified bronchitis (lag 0-6, 1.49%). We also found significant interactions between UFP health impacts and season (Fall, 3.29%), temperature (<90th percentile, 2.27%), relative humidity (>90th percentile, 4.63%), age (children aged <18 yr, 3.19%), and sex (men, 2.06%) on lag 0-6. Conclusions: In this study, UFP exposure increased CLRD-related ED visits across all seasons and demographic characteristics, yet these associations varied according to various factors, which requires more research.

Pollution risk and life insurance decisions: Microgeographic evidence from the United Kingdom

Recent research documents that exposure to air pollution can trigger various behavioral reactions. This article presents novel empirical evidence on the causal effect of pollution risk on life insurance decisions. We create a unique dataset by linking microgeographic air quality information to the confidential UK Wealth and Assets Survey. We identify an inverse N-shape relationship between pollution risk and life insurance adoption by exploiting the orthogonal variations in meteorological conditions. Over a given range above a threshold of exposure, rising pollution is associated with rising demand for life insurance, whereas at lower than the threshold levels of pollution, higher exposure risk reduces demand for insurance. Our findings indicate-for the first time-a nonlinear relationship between local pollution risk and life insurance demand.