Long-term exposure to major constituents of fine particulate matter and neurodegenerative diseases: A population-based survey in the Pearl River Delta Region, China.

BACKGROUND: Exposure to PM2.5 has been linked to neurodegenerative diseases, with limited understanding of constituent-specific contributions. OBJECTIVES: To explore the associations between long-term exposure to PM2.5 constituents and neurodegenerative diseases. METHODS: We recruited 148,274 individuals aged ≥ 60 from four cities in the Pearl River Delta region, China (2020 to 2021). We calculated twenty-year average air pollutant concentrations (PM2.5 mass, black carbon (BC), organic matter (OM), ammonium (NH4+), nitrate (NO3-) and sulfate (SO42-)) at the individuals' home addresses. Neurodegenerative diseases were determined by self-reported doctor-diagnosed Alzheimer's disease (AD) and Parkinson's disease (PD). Generalized linear mixed models were employed to explore associations between pollutants and neurodegenerative disease prevalence. RESULTS: PM2.5 and all five constituents were significantly associated with a higher prevalence of AD and PD. The observed associations generally exhibited a non-linear pattern. For example, compared with the lowest quartile, higher quartiles of BC were associated with greater odds for AD prevalence (i.e., the adjusted odds ratios were 1.81; 95% CI, 1.45-2.27; 1.78; 95% CI, 1.37-2.32; and 1.99; 95% CI, 1.54-2.57 for the second, third, and fourth quartiles, respectively). CONCLUSIONS: Long-term exposure to PM2.5 and its constituents, particularly combustion-related BC, OM, and SO42-, was significantly associated with higher prevalence of AD and PD in Chinese individuals. ENVIRONMENTAL IMPLICATION: PM2.5 is a routinely regulated mixture of multiple hazardous constituents that can lead to diverse adverse health outcomes. However, current evidence on the specific contributions of PM2.5 constituents to health effects is scarce. This study firstly investigated the association between PM2.5 constituents and neurodegenerative diseases in the moderately to highly polluted Pearl River Delta region in China, and identified hazardous constituents within PM2.5 that have significant impacts. This study provides important implications for the development of targeted PM2.5 prevention and control policies to reduce specific hazardous PM2.5 constituents.

Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology.

Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (pm2.5) increases mortality rate. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify groups of the population that are more or less vulnerable to air pollution. In causal inference literature, the group average treatment effect (GATE) is a distinctive facet of the conditional average treatment effect. This widely employed metric serves to characterize the heterogeneity of a treatment effect based on some population characteristics. In this paper, we introduce a novel Confounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the dependent Dirichlet process to model the distribution of the potential outcomes conditionally to the covariates and the treatment levels, thus enabling us to: (i) identify heterogeneous and mutually exclusive population groups defined by similar GATEs in a data-driven way, and (ii) estimate and characterize the causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found six mutually exclusive groups where the causal effects of pm2.5 on mortality rate are heterogeneous.

Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling.

BACKGROUND: Distributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power. METHODS: Here we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks. RESULTS: The results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies. CONCLUSIONS: SB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers.

Air Pollutants and Mortality Risk in Patients with Aortic Dissection: Evidence from a Clinical Cohort, Single-Cell Sequencing, and Proteomics.

We aimed to evaluate the association between air pollutants and mortality risk in patients with acute aortic dissection (AAD) in a longitudinal cohort and to explore the potential mechanisms of adverse prognosis induced by fine particulate matter (PM2.5). Air pollutants data, including PM2.5, PM10.0, nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3), were collected from official monitoring stations, and multivariable Cox regression models were applied. Single-cell sequencing and proteomics of aortic tissue were conducted to explore the potential mechanisms. In total, 1,267 patients with AAD were included. Exposure to higher concentrations of air pollutants was independently associated with an increased mortality risk. The high-PM2.5 group carried approximately 2 times increased mortality risk. There were linear associations of PM10, NO2, CO, and SO2 exposures with long-term mortality risk. Single-cell sequencing revealed an increase in mast cells in aortic tissue in the high-PM2.5 exposure group. Enrichment analysis of the differentially expressed genes identified the inflammatory response as one of the main pathways, with IL-17 and TNF signaling pathways being among the top pathways. Analysis of proteomics also identified these pathways. This study suggests that exposure to higher PM2.5, PM10, NO2, CO, and SO2 are associated with increased mortality risk in patients with AAD. PM2.5-related activation and degranulation of mast cells may be involved in this process.

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas.

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

An umbrella review of health co-benefits from actions to reduce greenhouse gas emissions.

BACKGROUND: There have been many modelled studies of potential health co-benefits from actions to reduce greenhouse gas emissions, but so far there have been no large-scale attempts to compare the magnitude of health and climate effects across sectors, countries, and study designs. METHODS: As part of the Pathfinder Initiative project an umbrella review of studies was done, and 26 previous reviews were identified with 57 primary studies included. Studies included in the review were required to have quantified changes in greenhouse gas emissions and health effects (or risk factors) from defined actions to reduce climate effects. Study data were extracted and harmonised by standardising impact measures per 100 000 of the national population (or urban population for city-level actions), averaging effects over a 1-year period and aggregating actions into their respective sectors by use of a predefined framework. FINDINGS: From 200 mitigation actions, the majority were in the agriculture, forestry, and land use sector (103 actions [52%]), followed by the transport sector (43 actions [22%]). The largest effects on greenhouse gas emissions were seen from actions in the energy sector, and these actions also had substantial health co-benefits in lower middle-income countries, although benefits were smaller in high-income settings. The greatest health benefits were seen from actions to change diets and introduce clean cookstoves. The major pathways to health were through reduced air pollution, healthier diets, and increased physical activity from switching to active travel modes. Effect sizes tended to be larger from national modelling studies and smaller from localised or implemented actions. INTERPRETATION: The potential co-benefits to health from actions to reduce climate change are large, but most evidence still comes from modelling studies and from high-income and middle-income countries. There are also major context-dependent differences in the magnitude of effects found, so actions need to be tailored to the local context and careful attention needs to be paid to potential trade-offs and spillover effects. FUNDING: The Wellcome Trust and the Oak Foundation.

Evaluation the impact of electricity consumption on China's air pollution at the provincial level.

As the world's largest electricity-consuming country, China faces the challenge of energy conservation and environmental pollution. Therefore, it is imperative that China takes decisive action to address these issues. Based on the panel data of 30 provinces (cities, districts) in China from 2011 to 2020, we use the entropy method to measure the air pollution index in different provinces, construct two fixed effects models, panel quantile model, and spatial Durbin model to empirically analyze the impact of electricity consumption on air pollution in China's provincial regions. The experimental results show that: (1) Electricity consumption has a significant positive impact on the provincial air pollution index in China and the higher the index is, the more serious the air pollution is. When the electricity consumption increases 1%, the air pollution index will increase of by 0.0649% as accompanied. (2) Through comparison of different times, we found that the degree of increase in air pollution index caused by electricity consumption would be reduced due to the improvement of environmental protection efforts. From the perspective of different geographical locations, the electricity consumption in the southeast side of the "Hu Line" has exacerbated the impact on air pollution index. (3) According to the panel quantile regression results, the marginal effect of electricity consumption on air pollution is positive. With the increase of quantiles, the impact of electricity consumption on air pollution is increasing. (4) Spatial effect analysis shows that electricity consumption has a significant positive spatial spillover effect on air pollution index. The increase in electricity consumption not only increases the air pollution index in the local region, but also leads to an increase in the air pollution index in surrounding areas. These findings contribute to the governance of air pollution and the promotion of sustainable economic, environmental and energy development.

The Associations of Prenatal Exposure to Fine Particulate Matter and Its Chemical Components with Allergic Rhinitis in Children and the Modification Effect of Polyunsaturated Fatty Acids: A Birth Cohort Study.

BACKGROUND: Polyunsaturated fatty acids (PUFAs) have been shown to protect against fine particulate matter <2.5µm in aerodynamic diameter (PM2.5)-induced hazards. However, limited evidence is available for respiratory health, particularly in pregnant women and their offspring. OBJECTIVES: We aimed to investigate the association of prenatal exposure to PM2.5 and its chemical components with allergic rhinitis (AR) in children and explore effect modification by maternal erythrocyte PUFAs. METHODS: This prospective birth cohort study involved 657 mother-child pairs from Guangzhou, China. Prenatal exposure to residential PM2.5 mass and its components [black carbon (BC), organic matter (OM), sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+)] were estimated by an established spatiotemporal model. Maternal erythrocyte PUFAs during pregnancy were measured using gas chromatography. The diagnosis of AR and report of AR symptoms in children were assessed up to 2 years of age. We used Cox regression with the quantile-based g-computation approach to assess the individual and joint effects of PM2.5 components and examine the modification effects of maternal PUFA levels. RESULTS: Approximately 5.33% and 8.07% of children had AR and related symptoms, respectively. The average concentration of prenatal PM2.5 was 35.50±5.31 µg/m3. PM2.5 was positively associated with the risk of developing AR [hazard ratio (HR)=1.85; 95% confidence interval (CI): 1.16, 2.96 per 5 µg/m3] and its symptoms (HR=1.79; 95% CI: 1.22, 2.62 per 5 µg/m3) after adjustment for confounders. Similar associations were observed between individual PM2.5 components and AR outcomes. Each quintile change in a mixture of components was associated with an adjusted HR of 3.73 (95% CI: 1.80, 7.73) and 2.69 (95% CI: 1.55, 4.67) for AR and AR symptoms, with BC accounting for the largest contribution. Higher levels of n-3 docosapentaenoic acid and lower levels of n-6 linoleic acid showed alleviating effects on AR symptoms risk associated with exposure to PM2.5 and its components. CONCLUSION: Prenatal exposure to PM2.5 and its chemical components, particularly BC, was associated with AR/symptoms in early childhood. We highlight that PUFA biomarkers could modify the adverse effects of PM2.5 on respiratory allergy. https://doi.org/10.1289/EHP13524.

Association Between Maternal Prenatal Exposure to Household Air Pollution and Child Respiratory Health: A Systematic Review and Meta-analysis.

Maternal prenatal exposure to household air pollution (HAP) is a critical public health concern with potential long-term implications for child respiratory health. The objective of this study is to assess the level of association between prenatal household air pollution and child respiratory health, and to identify which HAP pollutants are associated with specific respiratory illnesses or symptoms and to what degree. Relevant studies were retrieved from PubMed databases up to April 27, 2010, and their reference lists were reviewed. Random effects models were applied to estimate summarized relative risks (RRs) and 95% confidence intervals (CIs). The analysis involved 11 studies comprising 387 767 mother-child pairs in total, assessing various respiratory health outcomes in children exposed to maternal prenatal HAP. Children with prenatal exposure to HAP pollutants exhibited a summary RR of 1.26 (95% CI=1.08-1.33) with moderate between-study heterogeneity (I²=49.22%) for developing respiratory illnesses. Specific associations were found between prenatal exposure to carbon monoxide (CO) (RR=1.11, 95% CI: 1.09-1.13), Nitrogen Oxides (NOx) (RR=1.46, 95% CI: 1.09-1.60), and particulate matter (PM) (RR=1.26, 95% CI: 1.2186-1.3152) and child respiratory illnesses (all had I² close to 0%, indicating no heterogeneity). Positive associations with child respiratory illnesses were also found with ultrafine particles (UFP), polycyclic aromatic hydrocarbons (PAH), and ozone (O3). However, no significant association was observed for prenatal exposure to sulfur dioxide (SO2). In summary, maternal prenatal exposure to HAP may contribute to a higher risk of child respiratory health issues, emphasizing the need for interventions to reduce this exposure during pregnancy. Targeted public health strategies such as improved ventilation, cleaner cooking technologies, and awareness campaigns should be implemented to minimize adverse respiratory effects on children.

Assessment of the health impacts of air pollution exposure in East African countries.

The health effects of air pollution remain a public concern worldwide. Using data from the Global Burden of Disease 2019 report, we statistically analyzed total mortality, disability-adjusted life years (DALY), and years of life lost (YLL) attributable to air pollution in eight East African countries between 1990 and 2019. We acquired ambient ozone (O3), PM2.5 concentrations and household air pollution (HAP) from the solid fuel from the State of Global Air report. The multilinear regression model was used to evaluate the predictability of YLLs by the air pollutants. We estimated the ratio rate for each health burden attributable to air pollution to compare the country's efforts in the reduction of air pollution health burden. This study found that the total number of deaths attributable to air pollution decreased by 14.26% for 30 years. The drop came from the reduction of 43.09% in mortality related to Lower Respiratory tract Infection (LRI). However, only five out of eight countries managed to decrease the total number of deaths attributable to air pollution with the highest decrease observed in Ethiopia (40.90%) and the highest increase in Somalia (67.49%). The linear regression model showed that HAP is the pollutant of the most concern in the region, with a 1% increase in HAP resulting in a 31.06% increase in regional YLL (R2 = 0.93; p < 0.05). With the increasing ground-level ozone, accompanied by the lack of adequate measures to reduce particulate pollutants, the health burdens attributable to air pollution are still a threat in the region.

Association between Fine Particulate Matter Exposure and Cerebrospinal Fluid Biomarkers of Alzheimer's Disease among a Cognitively Healthy Population-Based Cohort.

BACKGROUND: Epidemiological evidence suggests air pollution adversely affects cognition and increases the risk of Alzheimer's disease (AD), but little is known about the biological effects of fine particulate matter (PM2.5, particulate matter with aerodynamic diameter ≤2.5µm) on early predictors of future disease risk. OBJECTIVES: We investigated the association between 1-, 3-, and 5-y exposure to ambient and traffic-related PM2.5 and cerebrospinal fluid (CSF) biomarkers of AD. METHODS: We conducted a cross-sectional analysis using data from 1,113 cognitively healthy adults (45-75 y of age) from the Emory Healthy Brain Study in Georgia in the United States. CSF biomarker concentrations of Aß42, tTau, and pTau, were collected at enrollment (2016-2020) and analyzed with the Roche Elecsys system. Annual ambient and traffic-related residential PM2.5 concentrations were estimated at a 1-km and 250-m resolution, respectively, and computed for each participant's geocoded address, using three exposure time periods based on specimen collection date. Associations between PM2.5 and CSF biomarker concentrations, considering continuous and dichotomous (dichotomized at clinical cutoffs) outcomes, were estimated with multiple linear/logistic regression, respectively, controlling for potential confounders (age, gender, race, ethnicity, body mass index, and neighborhood socioeconomic status). RESULTS: Interquartile range (IQR; IQR=0.845) increases in 1-y [ß:-0.101; 95% confidence interval (CI): -0.18, -0.02] and 3-y (ß:-0.078; 95% CI: -0.15, -0.00) ambient PM2.5 exposures were negatively associated with Aß42 CSF concentrations. Associations between ambient PM2.5 and Aß42 were similar for 5-y estimates (ß:-0.076; 95% CI: -0.160, 0.005). Dichotomized CSF variables revealed similar associations between ambient PM2.5 and Aß42. Associations with traffic-related PM2.5 were similar but not significant. Associations between PM2.5 exposures and tTau, pTau tTau/Aß42, or pTau/Aß42 levels were mainly null. CONCLUSION: In our study, consistent trends were found between 1-y PM2.5 exposure and decreased CSF Aß42, which suggests an accumulation of amyloid plaques in the brain and an increased risk of developing AD. https://doi.org/10.1289/EHP13503.

Quantifying the impact of urban road traffic on air quality: activity pre-pandemic and during partial and full lockdowns.

The impact of partial and full COVID lockdowns in 2020 on vehicle miles traveled (VMT) in Kuwait was estimated using data extracted from the Directions API of Google Maps and a Python script running as a cronjob. This approach was validated by comparing the predictions based on the app to measuring traffic flows for 1 week across four road segments considered in this study. VMT during lockdown periods were compared to VMT for the same calendar weeks before the pandemic. NOx emissions were estimated based on VMT and were used to simulate the spatial patterns of NOx concentrations using an air quality model (AERMOD). Compared to pre-pandemic periods, VMT was reduced by up to 25.5% and 42.6% during the 2-week partial and full lockdown episodes, respectively. The largest reduction in the traffic flow rate occurred during the middle of these 2-week periods, when the traffic flow rate decreased by 35% and 49% during the partial and full lockdown periods, respectively. The AERMOD simulation results predicted a reduction in the average maximum concentration of emissions directly related to VMT across the region by up to 38%, with the maximum concentration shifting to less populous residential areas as a result of the lockdown.

Speeding Up Time: New Urinary Peptide Clock Associates Greater Air Pollution Exposures with Faster Biological Aging.

A study in Belgium supports earlier findings on associations between higher air pollution exposures and markers of faster biological aging, this time by using urinary peptide levels instead of DNA-based markers.

Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort.

BACKGROUND: An increasing number of studies suggest adverse effects of exposure to ambient air pollution on cognitive function, but the evidence is still limited. We investigated the associations between long-term exposure to air pollutants and cognitive function in the English Longitudinal Study of Ageing (ELSA) cohort of older adults. METHODS: Our sample included 8,883 individuals from ELSA, based on a nationally representative study of people aged ≥ 50 years, followed-up from 2002 until 2017. Exposure to air pollutants was modelled by the CMAQ-urban dispersion model and assigned to the participants' residential postcodes. Cognitive test scores of memory and executive function were collected biennially. The associations between these cognitive measures and exposure to ambient concentrations of NO2, PM10, PM2.5 and ozone were investigated using mixed-effects models adjusted for time-varying age, physical activity and smoking status, as well as baseline gender and level of education. RESULTS: Increasing long-term exposure per interquartile range (IQR) of NO2 (IQR: 13.05 µg/m3), PM10 (IQR: 3.35 µg/m3) and PM2.5 (IQR: 2.7 µg/m3) were associated with decreases in test scores of composite memory by -0.10 (95% confidence interval [CI]: -0.14, -0.07), -0.02 [-0.04, -0.01] and -0.08 [-0.11, -0.05], respectively. The same increases in NO2, PM10 and PM2.5 were associated with decreases in executive function score of -0.31 [-0.38, -0.23], -0.05 [-0.08, -0.02] and -0.16 [-0.22, -0.10], respectively. The association with ozone was inverse across both tests. Similar results were reported for the London-dwelling sub-sample of participants. CONCLUSIONS: The present study was based on a long follow-up with several repeated measurements per cohort participant and long-term air pollution exposure assessment at a fine spatial scale. Increasing long-term exposure to NO2, PM10 and PM2.5 was associated with a decrease in cognitive function in older adults in England. This evidence can inform policies related to modifiable environmental exposures linked to cognitive decline.

[Spatial and Temporal Distribution Characteristics of Ozone Concentration and Health Benefit Assessment in the Beijing-Tianjin-Hebei Region from 2015 to 2020].

To evaluate the spatial and temporal distribution characteristics of ambient ozone (O3) in the Beijing-Tianjin-Hebei (BTH) Region, the land use regression (LUR) model and random forest (RF) model were used to simulate the ambient O3 concentration from 2015 to 2020. Meanwhile, all-cause, cardiovascular, and respiratory mortalities as well as economic losses attributed to O3 were also estimated. The results showed that upward trends with fluctuation were observed for ambient O3 concentration, mortalities, and economic losses attributable to O3 exposure in the BTH Region from 2015 to 2020. The areas with high O3 concentration and great changes were concentrated in the central and southwestern regions, whereas the concentration in the northern region was low, and the change degree was small. The spatial distribution of the mortalities was also consistent with the spatial distribution of O3 concentration. From 2015 to 2020, the economic losses regarding all-cause mortality and cardiovascular mortality increased in 13 cities of the BTH Region, whereas the economic losses of respiratory mortality decreased in 4 cities in the BTH Region. The results indicated that the priority areas for O3 control were not uniform. Specifically, Beijing, Tianjin, Hengshui, and Xingtai were vital areas for O3 pollution control in the BTH Region. Differentiated control measures should be adopted based on the characteristics of these target areas to decline O3 concentration and reduce health impacts and economic losses associated with O3 exposure.

Associations between long-term exposure to air pollution and kidney function utilizing electronic healthcare records: a cross-sectional study.

BACKGROUND: Chronic kidney disease (CKD) affects more than 38 million people in the United States, predominantly those over 65 years of age. While CKD etiology is complex, recent research suggests associations with environmental exposures. METHODS: Our primary objective is to examine creatinine-based estimated glomerular filtration rate (eGFRcr) and diagnosis of CKD and potential associations with fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) using a random sample of North Carolina electronic healthcare records (EHRs) from 2004 to 2016. We estimated eGFRcr using the serum creatinine-based 2021 CKD-EPI equation. PM2.5 and NO2 data come from a hybrid model using 1 km2 grids and O3 data from 12 km2 CMAQ grids. Exposure concentrations were 1-year averages. We used linear mixed models to estimate eGFRcr per IQR increase of pollutants. We used multiple logistic regression to estimate associations between pollutants and first appearance of CKD. We adjusted for patient sex, race, age, comorbidities, temporality, and 2010 census block group variables. RESULTS: We found 44,872 serum creatinine measurements among 7,722 patients. An IQR increase in PM2.5 was associated with a 1.63 mL/min/1.73m2 (95% CI: -1.96, -1.31) reduction in eGFRcr, with O3 and NO2 showing positive associations. There were 1,015 patients identified with CKD through e-phenotyping and ICD codes. None of the environmental exposures were positively associated with a first-time measure of eGFRcr < 60 mL/min/1.73m2. NO2 was inversely associated with a first-time diagnosis of CKD with aOR of 0.77 (95% CI: 0.66, 0.90). CONCLUSIONS: One-year average PM2.5 was associated with reduced eGFRcr, while O3 and NO2 were inversely associated. Neither PM2.5 or O3 were associated with a first-time identification of CKD, NO2 was inversely associated. We recommend future research examining the relationship between air pollution and impaired renal function.

Sustained Reductions of Bay Area CO2 Emissions 2018-2022.

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.