Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis.

Introduction: Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking. Methods: This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects. Results: Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO2 estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research. Discussion: Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.

Towards a low-carbon and beautiful world: assessing the impact of digital technology on the common benefits of pollution reduction and carbon reduction.

When ecology thrives, civilization thrives, and when ecology declines, civilization declines. Based on panel data from 30 provinces in China from 2000 to 2021, this study used marginal abatement costs to estimate the co-benefits of pollution reduction and carbon reduction. Two-way fixed effect and two-stage intermediary effect models were used to evaluate the impact of digital technology on co-benefits and its indirect channels. The results indicated that China's total carbon emissions maintained a steady growth trend, while air pollution showed a fluctuating declining trend. Reaching peak carbon neutrality calls for more innovative solutions. Under joint emission reduction efforts, the study revealed marginal abatement cost savings of 535.8 million yuan/million tons and 6216.5 million yuan/µg/m3 for carbon reduction and pollution reduction, respectively. Most importantly, the study confirmed that joint emission reduction programs can reduce environmental governance costs more than individual emission reductions can, and the co-benefits increased from 37.983 to 44.757. The co-benefits generally showed a trend of fluctuation and increases and had the characteristics of phased transformation. Intragroup differences and cross-overlapping between regions made regional differences in co-benefits obvious. The subversive, permeable, and integrated features of digital technology have resulted in the all-around transformation of the economy and society, and the new technology-economy paradigm has significantly improved co-benefits. The conclusion remains valid after robustness testing and controlling for endogeneity problems. The results of the mechanism analysis suggest that digital technology can indirectly improve synergies through the intermediary channels of fostering green technology innovation, reducing energy consumption intensity and improving the energy structure.

Spatio-temporal distribution and source contributions of the ambient pollutants in Lucknow city, India.

Clean air is imperative to the survival of all life forms on the planet. However, recent times have witnessed enormous escalation in urban pollution levels. It is therefore, incumbent upon us to decipher measures to deal with it. In perspective, the present study was carried out to assess PM10 and PM2.5 loading, metallic constituents, gaseous pollutants, source contributions, health impact and noise level of nine-locations, grouped as residential, commercial, and industrial in Lucknow city for 2019-21. Mean concentrations during pre-monsoon for PM10, PM2.5, SO2 and NO2 were: 138.2 ± 35.2, 69.1 ± 13.6, 8.5 ± 3.3 and 32.3 ± 7.4 µg/m3, respectively, whereas post-monsoon concentrations were 143.0 ± 33.3, 74.6 ± 14.5, 12.5 ± 2.1, and 35.5 ± 6.3 µg/m3, respectively. Exceedance percentage of pre-monsoon PM10 over National Ambient Air Quality Standards (NAAQS) was 38.2% while that for post-monsoon was 43.0%; whereas corresponding values for PM2.5 were 15.2% and 24.3%. Post-monsoon season showed higher particulate loading owing to wintertime inversion and high humidity conditions. Order of elements associated with PM2.5 is Co < Cd < Cr < Ni < V < Be < Mo < Mn < Ti < Cu < Pb < Se < Sr < Li < B < As < Ba < Mg < Al < Zn < Ca < Fe < K < Na and that with PM10 is Co < Cd < Ni < Cr < V < Ti < Be < Mo < Cu < Pb < Se < Sr < Li < B < As < Mn < Ba < Mg < Al < Fe < Zn < K < Na < Ca. WHO AIRQ + ascertained 1654, 144 and 1100 attributable cases per 0.1 million of population to PM10 exposure in 2019-21. Source apportionment was carried out using USEPA-PMF and resolved 6 sources with highest percent contributions including road dust re-entrainment, biomass burning and vehicular emission. It is observed that residents of Lucknow city regularly face exposure to particulate pollutants and associated constituents making it imperative to develop pollution abetment strategies.

Multi-source observations on the effect of atmospheric blocking on air quality in Istanbul: a study case.

Air pollution is affected by the atmospheric dynamics. This study aims to determine that air pollution concentration values in Istanbul increased significantly and reached peak values due to atmospheric blocking between the 30th of December 2022 and the 5th of January 2023. In this study, hourly pollutant data was obtained from 16 air quality monitoring stations (AQMS), the exact reanalysis data was extracted from ERA5 database, and inversion levels and meteorological and synoptic analyses were used to determine the effects of atmospheric blocking on air pollution. Also, cloud base heights and vertical visibility measurements were taken with a ceilometer. Statistical calculations and data visualizations were performed using the R and Grads program. Omega-type blocking, which started in Istanbul on December 30, 2022, had a significant impact on the 1st and 2nd of January 2023, and PM10 and PM2.5 concentration values reached their peak values at 572.8 and 254.20 µg/m3, respectively. In addition, it was found that the average concentration values in the examined period in almost all stations were higher than the averages for January and February. As a result, air quality in Istanbul was determined as "poor" between these calendar dates. It was found that the blocking did not affect the ozone (µg/m3) concentration. It was also found that the concentrations of particulate matter (PM) 10 µm or less in diameter (PM10) and PM 2.5 µm or less in diameter (PM2.5) were increased by the blocking effect in the Istanbul area. Finally, according to the data obtained using the ceilometer, cloud base heights decreased to 30 m and vertical visibility to 10 m.

Using Parametric g-Computation for Time-to-Event Data and Distributed Lag Models to Identify Critical Exposure Windows for Preterm Birth: An Illustrative Example Using PM2.5 in a Retrospective Birth Cohort Based in Eastern Massachusetts (2011-2016).

BACKGROUND: Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped. OBJECTIVES: We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach "g-survival-DLM" and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB). METHODS: We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax. RESULTS: There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5 concentration was relatively stable across pregnancy at ∼7µg/m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20. DISCUSSION: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5µm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.

Exposure to Air Pollution, Genetic Susceptibility, and Psoriasis Risk in the UK.

Importance: Psoriasis is a common autoinflammatory disease influenced by complex interactions between environmental and genetic factors. The influence of long-term air pollution exposure on psoriasis remains underexplored. Objective: To examine the association between long-term exposure to air pollution and psoriasis and the interaction between air pollution and genetic susceptibility for incident psoriasis. Design, Setting, and Participants: This prospective cohort study used data from the UK Biobank. The analysis sample included individuals who were psoriasis free at baseline and had available data on air pollution exposure. Genetic analyses were restricted to White participants. Data were analyzed between November 1 and December 10, 2023. Exposures: Exposure to nitrogen dioxide (NO2), nitrogen oxides (NOx), fine particulate matter with a diameter less than 2.5 µm (PM2.5), and particulate matter with a diameter less than 10 µm (PM10) and genetic susceptibility for psoriasis. Main Outcomes and Measures: To ascertain the association of long-term exposure to NO2, NOx, PM2.5, and PM10 with the risk of psoriasis, a Cox proportional hazards model with time-varying air pollution exposure was used. Cox models were also used to explore the potential interplay between air pollutant exposure and genetic susceptibility for the risk of psoriasis incidence. Results: A total of 474 055 individuals were included, with a mean (SD) age of 56.54 (8.09) years and 257 686 (54.36%) female participants. There were 9186 participants (1.94%) identified as Asian or Asian British, 7542 (1.59%) as Black or Black British, and 446 637 (94.22%) as White European. During a median (IQR) follow-up of 11.91 (11.21-12.59) years, 4031 incident psoriasis events were recorded. There was a positive association between the risk of psoriasis and air pollutant exposure. For every IQR increase in PM2.5, PM10, NO2, and NOx, the hazard ratios (HRs) were 1.41 (95% CI, 1.35-1.46), 1.47 (95% CI, 1.41-1.52), 1.28 (95% CI, 1.23-1.33), and 1.19 (95% CI, 1.14-1.24), respectively. When comparing individuals in the lowest exposure quartile (Q1) with those in the highest exposure quartile (Q4), the multivariate-adjusted HRs were 2.01 (95% CI, 1.83-2.20) for PM2.5, 2.21 (95% CI, 2.02-2.43) for PM10, 1.64 (95% CI, 1.49-1.80) for NO2, and 1.34 (95% CI, 1.22-1.47) for NOx. Moreover, significant interactions between air pollution and genetic predisposition for incident psoriasis were observed. In the subset of 446 637 White individuals, the findings indicated a substantial risk of psoriasis development in participants exposed to the highest quartile of air pollution levels concomitant with high genetic risk compared with those in the lowest quartile of air pollution levels with low genetic risk (PM2.5: HR, 4.11; 95% CI, 3.46-4.90; PM10: HR, 4.29; 95% CI, 3.61-5.08; NO2: HR, 2.95; 95% CI, 2.49-3.50; NOx: HR, 2.44; 95% CI, 2.08-2.87). Conclusions and Relevance: In this prospective cohort study of the association between air pollution and psoriasis, long-term exposure to air pollution was associated with increased psoriasis risk. There was an interaction between air pollution and genetic susceptibility on psoriasis risk.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

Seasonal and trend variation of methane concentration over two provinces of South Africa using Sentinel-5p data.

South Africa faces the urgency to comprehensively understand and manage its methane (CH4) emissions. The primary aim of this study is to compare CH4 concentrations between Eastern Cape and Mpumalanga regions dominated by cattle farming and coal mining industries, respectively. CH4 concentration trends were analyzed for the period 2019 to 2023 using satellite data. Trend analysis revealed significant increasing trends in CH4 concentrations in both provinces, supported by Mann-Kendall tests that rejected the null hypothesis of no trend (Eastern Cape: p-value = 8.9018e-08 and Mpumalanga: p-value = 2.4650e-10). The Eastern Cape, a leading cattle farming province, exhibited cyclical patterns and increasing CH4 concentrations, while Mpumalanga, a major coal mining province, displayed similar increasing trends with sharper concentration points. The results show seasonal variations in CH4 concentrations in the Eastern Cape and Mpumalanga provinces. High CH4 concentrations are observed in the northwestern region during the December-January-February (DJF) season, while lower concentrations are observed in the March-April-May (MAM) and June-July-August (JJA) seasons in the Eastern Cape province. In the Mpumalanga province, there is a dominance of high CH4 concentrations in southwestern regions and moderately low concentrations in the northeastern regions, observed consistently across all seasons. The study also showed an increasing CH4 concentration trend from 2019 to 2023 for both provinces. The study highlights the urgent need to address CH4 emissions from both cattle farming and coal mining activities to mitigate environmental impacts and promote sustainable development. Utilizing geographic information system (GIS) and remote sensing technologies, policymakers and stakeholders can identify and address the sources of CH4 emissions more effectively, thereby contributing to environmental conservation and sustainable resource management.

Prenatal Exposure to Ambient Air Pollution and Cerebral Palsy.

Importance: Air pollution is associated with structural brain changes, disruption of neurogenesis, and neurodevelopmental disorders. The association between prenatal exposure to ambient air pollution and risk of cerebral palsy (CP), which is the most common motor disability in childhood, has not been thoroughly investigated. Objective: To evaluate the associations between prenatal residential exposure to ambient air pollution and risk of CP among children born at term gestation in a population cohort in Ontario, Canada. Design, Setting, and Participants: Population-based cohort study in Ontario, Canada using linked, province-wide health administrative databases. Participants were singleton full term births (≥37 gestational weeks) born in Ontario hospitals between April 1, 2002, and March 31, 2017. Data were analyzed from January to December 2022. Exposures: Weekly average concentrations of ambient fine particulate matter with a diameter 2.5 µm (PM2.5) or smaller, nitrogen dioxide (NO2), and ozone (O3) during pregnancy assigned by maternal residence reported at delivery from satellite-based estimates and ground-level monitoring data. Main outcome and measures: CP cases were ascertained by a single inpatient hospitalization diagnosis or at least 2 outpatient diagnoses for children from birth to age 18 years. Results: The present study included 1 587 935 mother-child pairs who reached term gestation, among whom 3170 (0.2%) children were diagnosed with CP. The study population had a mean (SD) maternal age of 30.1 (5.6) years and 811 745 infants (51.1%) were male. A per IQR increase (2.7 µg/m3) in prenatal ambient PM2.5 concentration was associated with a cumulative hazard ratio (CHR) of 1.12 (95% CI, 1.03-1.21) for CP. The CHR in male infants (1.14; 95% CI, 1.02-1.26) was higher compared with the CHR in female infants (1.08; 95% CI, 0.96-1.22). No specific window of susceptibility was found for prenatal PM2.5 exposure and CP in the study population. No associations or windows of susceptibility were found for prenatal NO2 or O3 exposure and CP risk. Conclusions and relevance: In this large cohort study of singleton full term births in Canada, prenatal ambient PM2.5 exposure was associated with an increased risk of CP in offspring. Further studies are needed to explore this association and its potential biological pathways, which could advance the identification of environmental risk factors of CP in early life.

The impact of ambient temperature and powertrains of SUVs on the environment in Slovakia during the use phase.

This study compares different powertrains of sport utility vehicles (SUVs) with respect to ambient temperature and energy mix in Slovakia using the well-to-wheel (WTW) Life Cycle Assessment (LCA) method. Battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), and petrol and diesel vehicles were assessed and compared. The WTW study was conducted in SimaPro software assessing electricity/petrol/diesel production, transport, and use (energy conversion in the vehicle), with impact categories being climate change, particulates, NOx emissions, ionizing radiation, and fossil resource scarcity depending on the season (summer and winter). The results indicate that for Slovak conditions, BEV generally had the lowest environmental impact in both seasons studied. The only exceptions were ionizing radiation, which is clearly caused by the high share of nuclear power in the Slovak energy mix, and NOx emissions, which are caused by the combustion of biomass for electricity generation. The other impact categories were dominated by vehicles with an internal combustion engine. The results of emissions from fuel production are also given for each impact category. The transportation of fuel did not exceed the value of 1% for any impact category or for any powertrain. The conclusions of the study support the global trend in favour of vehicle electrification as an important way to reduce the negative environmental impacts of internal combustion engine vehicles in Slovakia.

An Inflammatory Question? Prenatal Air Pollution, Childhood Allergic Rhinitis, and Healthy Fats.

A study of more than 650 toddlers found that two polyunsaturated fatty acids were associated with fewer cases of allergic rhinitis (hay fever) in children who had been exposed prenatally to higher levels of PM2.5.

Effects of socioeconomic status and regional inequality on the association between PM2.5 and its components and cardiometabolic multimorbidity: A multicenter population-based survey in eastern China.

BACKGROUND: Despite evidence linking fine particulate matter (PM2.5) to cardiometabolic multimorbidity (CMM), the impact of its components remains unclear. Socioeconomic status (SES) and regional disparities may confound their association. We aim to evaluate the associations between PM2.5 components and CMM and explore how socioeconomic status and regional disparities affect these relationships. METHODS: We recruited 108,941 participants aged 35-76 years from ten cities in eastern China. Individual exposure was assessed using Tracking Air Pollution in China (TAP) data, including PM2.5 and five components: ammonium (NH4+), black carbon (BC), nitrates (NO3-), organic matter (OM), and sulfates (SO42-). Generalized linear models and quantile g-computation models were employed to quantify the effects of PM2.5 components on CMM and to identify key components. Stratified analyses were performed to investigate the modifying effect of SES and regional disparities. RESULTS: For each increase in interquartile range (IQR), BC (odds ratio [OR] 1.37, 95 % CI 1.29-1.47), OM (1.38, 1.29-1.48), NH4+ (1.31, 1.21-1.40), NO3- (1.34, 1.25-1.44), and SO42- (1.28, 1.20-1.38) were positively associated with CMM. Joint exposure to five components was significantly positively associated with CMM (OR: 1.27, 95 % CI: 1.21-1.33), with SO42- having the highest estimated weight, followed by NO3- and BC. These associations were stronger for participants from low socio-economic status and poor regions. CONCLUSION: In summary, we found a stronger hazard effect of PM2.5 and its components on CMM, compared to those suffering from CMDs, particularly among participants with low socioeconomic status and in poor regions. SO42- may be a primary contributor to the association between PM2.5 components and CMM. These findings underscore the importance of prioritizing CMM and targeting SO42-related pollution sources in health policies, particularly amid China's aging population, reducing environmental health inequalities is critical.

Assessing the spatiotemporal evolution and socioeconomic determinants of PM2.5-related premature deaths in China from 2000 to 2021.

China's swift socioeconomic development has led to extremely severe ambient PM2.5 levels, the associated negative health outcomes of which include premature death. However, a comprehensive explanation of the socioeconomic mechanism contributing to PM2.5-related premature deaths has not yet to be fully elucidated through long-term spatial panel data. Here, we employed a global exposure mortality model (GEMM) and the system generalized method of moments (Sys-GMM) to examine the primary determinants contributing to premature deaths in Chinese provinces from 2000 to 2021. We found that in the research period, premature deaths in China increased by 46 %, reaching 1.87 million, a figure that decreased somewhat after the COVID-19 outbreak. 62 thousand premature deaths were avoided in 2020 and 2021 compared to 2019, primarily due to the decline in PM2.5 concentrations. Premature deaths have increased across all provinces, particularly in North China, and a discernible spatial agglomeration effect was observed, highlighting effects on nearby provinces. The findings also underscored the significance of determinants such as urbanization, import and export trade, and energy consumption in exacerbating premature deaths, while energy intensity exerted a mitigating influence. Importantly, a U-shaped relationship between premature deaths and economic development was unveiled for the first time, implying the need for vigilance regarding potential health impact deterioration and the implementation of countermeasures as the per capita GDP increases in China. Our findings deserve attention from policymakers as they shed fresh insights into atmospheric control and Health China action.

Residential air pollution, greenspace, and adverse mental health outcomes in the U.S. Gulf Long-term Follow-up Study.

Air pollution and greenness are environmental determinants of mental health, though existing evidence typically considers each exposure in isolation. We evaluated relationships between co-occurring air pollution and greenspace levels and depression and anxiety. We estimated cross-sectional associations among 9015 Gulf Long-term Follow-up Study participants living in the southeastern U.S. who completed the Patient Health Questionnaire-9 (depression: score ≥ 10) and Generalized Anxiety Disorder Questionnaire-7 (anxiety: score ≥ 10). Participant residential addresses were linked to annual average concentrations of particulate matter (1 km PM2.5) and nitrogen dioxide (1 km NO2), as well as satellite-based greenness (2 km Enhanced Vegetation Index (EVI)). We used adjusted log-binomial regression to estimate prevalence ratios (PR) and 95 % confidence intervals (CI) for associations between exposures (quartiles) and depression and anxiety. In mutually adjusted models (simultaneously modeling PM2.5, NO2, and EVI), the highest quartile of PM2.5 was associated with increased prevalence of depression (PR = 1.17, 95 % CI: 1.06-1.29), whereas the highest quartile of greenness was inversely associated with depression (PR = 0.89, 95 % CI: 0.80-0.99). Joint exposure to greenness mitigated the impact of PM2.5 on depression (PRPM only = 1.20, 95 % CI: 1.06-1.36; PRPM+green = 0.98, 95 % CI: 0.83-1.16) and anxiety (PRPM only = 1.10, 95 % CI: 1.00-1.22; PRPM+green = 0.95, 95 % CI: 0.83-1.09) overall and in subgroup analyses. Observed associations were stronger in urbanized areas and among nonwhite participants, and varied by neighborhood deprivation. NO2 exposure was not independently associated with depression or anxiety in this population. Relationships between PM2.5, greenness, and depression were strongest in the presence of characteristics that are highly correlated with lower socioeconomic status, underscoring the need to consider mental health as an environmental justice issue.

Effects of industrial pollution and ambient air temperature on larval performance and population dynamics of Eriocrania leafminers (Lepidoptera).

Pollution is an integral part of global environmental change, yet the combined and interactive effects of pollution and climate on terrestrial ecosystems remain inadequately understood. This study aims to explore whether pollution alters the impacts of ambient air temperature on the population dynamics of herbivorous insects. Between 1995 and 2005, we studied populations of two closely related moths, Eriocrania semipurpurella and E. sangii, at eight sites located 1 to 64 km from a large copper­nickel smelter in Monchegorsk, Russia. We found that pollution and temperature influence the performance of Eriocrania larvae mining in the leaves of mountain birch, Betula pubescens var. pumila, through multiple pathways. This is evident from the unconsistent changes observed in larval and frass weight, mine area, and leaf size. We found increases in both leaf quality and larval weight with decreasing pollution levels at both spatial and temporal scales and attributed these to the impact of sulphur dioxide, rather than trace elements (nickel and copper). The quality of birch leaves increased with spring (May) temperatures, enabling Eriocrania larvae to achieve greater weight while consuming less biomass. During the larval growth period (early June to early July), Eriocrania larvae increased their consumption with rising temperatures, presumably to compensate for increased metabolic expenses. Contrary to our expectations, the per capita rate of population change did not correlate with larval weight and did not vary along the pollution gradient. Nevertheless, we detected interactive effects of pollution and climate on the rate of population change. This rate decreased with rising winter temperatures in slightly polluted and unpolluted sites but remained unchanged in heavily polluted sites. We conclude that pollution disrupts mechanisms regulating the natural population dynamics of Eriocrania moths.

The Association between Exposure to Fine Particulate Matter and MRI-Assessed Locus Coeruleus Integrity in the Vietnam Era Twin Study of Aging (VETSA).

BACKGROUND: Increased exposure to ambient air pollution, especially fine particulate matter ≤2.5µm (PM2.5) is associated with poorer brain health and increased risk for Alzheimer's disease (AD) and related dementias. The locus coeruleus (LC), located in the brainstem, is one of the earliest regions affected by tau pathology seen in AD. Its diffuse projections throughout the brain include afferents to olfactory areas that are hypothesized conduits of cerebral particle deposition. Additionally, extensive contact of the LC with the cerebrovascular system may present an additional route of exposure to environmental toxicants. OBJECTIVE: Our aim was to investigate if exposure to PM2.5 was associated with LC integrity in a nationwide sample of men in early old age, potentially representing one pathway through which air pollution can contribute to increased risk for AD dementia. METHODS: We examined the relationship between PM2.5 and in vivo magnetic resonance imaging (MRI) estimates of LC structural integrity indexed by contrast to noise ratio (LCCNR) in 381 men [mean age=67.3; standard deviation (SD)=2.6] from the Vietnam Era Twin Study of Aging (VETSA). Exposure to PM2.5 was taken as a 3-year average over the most recent period for which data were available (average of 5.6 years prior to the MRI scan). We focused on LCCNR in the rostral-middle portion of LC due to its stronger associations with aging and AD than the caudal LC. Associations between PM2.5 exposures and LC integrity were tested using linear mixed effects models adjusted for age, scanner, education, household income, and interval between exposure and MRI. A co-twin control analysis was also performed to investigate whether associations remained after controlling for genetic confounding and rearing environment. RESULTS: Multiple linear regressions revealed a significant association between PM2.5 and rostral-middle LCCNR (ß=-0.16; p=0.02), whereby higher exposure to PM2.5 was associated with lower LCCNR. A co-twin control analysis found that, within monozygotic pairs, individuals with higher PM2.5 exposure showed lower LCCNR (ß=-0.11; p=0.02), indicating associations were not driven by genetic or shared environmental confounds. There were no associations between PM2.5 and caudal LCCNR or hippocampal volume, suggesting a degree of specificity to the rostral-middle portion of the LC. DISCUSSION: Given previous findings that loss of LC integrity is associated with increased accumulation of AD-related amyloid and tau pathology, impacts on LC integrity may represent a potential pathway through which exposure to air pollution increases AD risk. https://doi.org/10.1289/EHP14344.

Association between Personal Abiotic Airborne Exposures and Body Composition Changes among Healthy Adults (60-69 Years Old): A Combined Exposome-Wide and Lipidome Mediation Approach from the China BAPE Study.

BACKGROUND: Evidence suggested that abiotic airborne exposures may be associated with changes in body composition. However, more evidence is needed to identify key pollutants linked to adverse health effects and their underlying biomolecular mechanisms, particularly in sensitive older adults. OBJECTIVES: Our research aimed to systematically assess the relationship between abiotic airborne exposures and changes in body composition among healthy older adults, as well as the potential mediating mechanisms through the serum lipidome. METHODS: From September 2018 to January 2019, we conducted a monthly survey among 76 healthy adults (60-69 years old) in the China Biomarkers of Air Pollutant Exposure (BAPE) study, measuring their personal exposures to 632 abiotic airborne pollutions using MicroPEM and the Fresh Air wristband, 18 body composition indicators from the InBody 770 device, and lipidomics from venous blood samples. We used an exposome-wide association study (ExWAS) and deletion/substitution/addition (DSA) model to unravel complex associations between exposure to contaminant mixtures and body composition, a Bayesian kernel machine regression (BKMR) model to assess the overall effect of key exposures on body composition, and mediation analysis to identify lipid intermediators. RESULTS: The ExWAS and DSA model identified that 2,4,5-T methyl ester (2,4,5-TME), 9,10-Anthracenedione (ATQ), 4b,8-dimethyl-2-isopropylphenanthrene, and 4b,5,6,7,8,8a,9,10-octahydro-(DMIP) were associated with increased body fat mass (BFM), fat mass indicators (FMI), percent body fat (PBF), and visceral fat area (VFA) in healthy older adults [Bonferroni-Hochberg false discovery rate (FDRBH)<0.05]. The BKMR model demonstrated a positive correlation between contaminants (anthracene, ATQ, copaene, di-epi-α-cedrene, and DMIP) with VFA. Mediation analysis revealed that phosphatidylcholine [PC, PC(16:1e/18:1), PC(16:2e/18:0)] and sphingolipid [SM, SM(d18:2/24:1)] mediated a significant portion, ranging from 12.27% to 26.03% (p-value <0.05), of the observed increase in VFA. DISCUSSION: Based on the evidence from multiple model results, ATQ and DMIP were statistically significantly associated with the increased VFA levels of healthy older adults, potentially regulated through lipid intermediators. These findings may have important implications for identifying potentially harmful environmental chemicals and developing targeted strategies for the control and prevention of chronic diseases in the future, particularly as the global population is rapidly aging. https://doi.org/10.1289/EHP13865.

Comparative analysis of real-world vehicular emissions from BS-IV and BS-VI cars in India.

This study investigates real-world carbon dioxides (CO2) and nitrogen oxides (NOx) emissions from diesel (Bharat Stage-IV (BS-IV)) and petrol/gasoline (BS-IV and BS-VI) cars in Indian driving conditions using a portable emission measurement system (PEMS). The paired sample t-test revealed a significant difference ( p < 0.05) in NOx and CO2 emissions among the three types of cars, except for CO2 emissions ( p > 0.05) between BS-IV petrol and BS-VI petrol cars. The highest NOx emission rates were observed in all car types during acceleration (> 1 m/s2) and deceleration (- 2 m/s2). CO2 emission rates were also high during acceleration (> 1 m/s2) for all car types. At low speeds (around 20 kmph), all car types had low emissions of CO2 and NOx, with acceleration and deceleration rates ranging from - 0.5 to 0.5 m/s2. BS-IV diesel cars emit significantly higher NOx emissions compared to petrol cars, especially at vehicle-specific power (VSP) bin 0 (deceleration to idling mode) and during VSP bin 7 (acceleration mode). BS-IV diesel cars emit 228% and 530% higher NOx emissions than BS-IV and BS-VI petrol cars at VSP bins 0 and 7, respectively. CO2 emissions from BS-VI petrol cars were 10% lower than those from BS-IV petrol cars across all VSP bins, indicating moderate reductions. Furthermore, diesel cars emit 140% less CO2 emissions than petrol cars across various VSP bins. The findings highlight the need for cleaner technologies and responsible driving practices to address vehicular emission concerns.

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.

This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory diseases and the levels of PM10 and SO2 pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is to clarify the impact of air pollution on the well-being of the general population, specifically focusing on respiratory ailments. An ANN called a multilayer perceptron (MLP) was used. The network was trained using the Levenberg-Marquardt (LM) backpropagation algorithm. The data revealed a substantial increase in hospital admissions for upper respiratory tract diseases, amounting to a total of 11,746 cases. There were clear seasonal fluctuations, with fall having the highest number of cases of bronchitis (N = 181), sinusitis (N = 83), and upper respiratory infections (N = 194). The study also found demographic differences, with females and people aged 18 to 65 years having greater admission rates. The performance of the ANN model, measured using R2 values, demonstrated a high level of predictive accuracy. Specifically, the R2 value was 0.91675 during training, 0.99182 during testing, and 0.95287 for validating the prediction of asthma. The comparative analysis revealed that the ANN-MLP model provided the most optimal result. The results emphasize the effectiveness of ANNs in representing the complex relationships between air quality, climatic conditions, and respiratory health. The results offer crucial insights for formulating focused healthcare policies and treatments to alleviate the detrimental impact of air pollution and meteorological factors.

Boletim mensal VigiAR setembro/ outubro de 2018: volume 2

O Programa em Saúde Ambiental relacionado a populações expostas à poluição do ar do Município de São Paulo (VIGIAR) tem por objetivo desenvolver ações de vigilância em saúde ambiental, para populações expostas aos poluentes atmosféricos, de forma a orientar medidas de prevenção, promoção da saúde e de atenção integral, conforme preconizado pelo Sistema Único de Saúde (SUS).