Elevated PM2 Research Articles

The Effects of High Particulate Matter Levels on Platelet Recovery in Patients Receiving Prophylactic Platelet Transfusion.

Exposure to fine particulate matter, particularly PM2.5, has been associated with increased platelet activation and cardiovascular risks. However, its effect on platelet recovery after transfusion remains unclear. This study aims to assess the influence of PM2.5 exposure on platelet recovery in patients with hematologic malignancies receiving prophylactic platelet transfusions. We conducted a cross-sectional study involving 66 patients with hematologic malignancies who developed chemotherapy-induced thrombocytopenia and received prophylactic platelet transfusions between January and December 2021. A total of 191 transfusion events were analyzed. Platelet increment and corrected count increment (CCI) were measured one hour post-transfusion. Transfusions were categorized based on mean PM2.5 levels one day prior to platelet collection: the control group (< 37.5μg/m³) and the case group (≥ 37.5μg/m³). Multivariate analyses were used to adjust for potential confounders. No significant differences were observed in platelet increment (p = 0.128) or CCI (p = 0.828) between the PM2.5 exposure groups. Correlation analyses showed no significant association between PM2.5 levels and platelet increment (r = 0.0565, p = 0.437) or CCI (r = 0.0370, p = 0.614). These findings suggest that exposure to elevated PM2.5 levels one day before donation does not significantly impair platelet recovery. Short-term exposure to elevated PM2.5 levels does not significantly affect platelet recovery in patients receiving prophylactic platelet transfusions. These results provide important reassurance regarding the immediate effects of air pollution on transfusion outcomes, while highlighting the need for further research into potential long-term impacts.

Life-course exposure to air pollution and the risk of dementia in the Lothian Birth Cohort 1936.

Air pollution in later life has been associated with dementia; however, limited research has investigated the association between air pollution across the life course, either at specific life periods or cumulatively. The project investigates the association of air pollution with dementia via a life-course epidemiological approach. Participants of the Lothian Birth Cohort, born in 1936, provided lifetime residential history in 2014. Participant's air pollution exposure for time periods 1935, 1950, 1970, 1980, 1990, 2001, and 2007 was modeled using an atmospheric chemistry transport model. Lifetime cumulative exposures were calculated as time-weighted mean exposure. Of 572 participants, 67 developed all-cause dementia [35 with Alzheimer's dementia (AD)] by wave 5 (~82 years). Cox proportional hazards and competing risk models assessed the association between all-cause dementia and AD with particulate matter (diameter of ≤2.5 µm) PM2.5 and nitrogen dioxide (NO2) exposure at specific life periods and cumulatively. False discovery rate (FDR) correction was applied for multiple testing. The mean follow-up was 11.26 years. One standard deviation (SD) higher exposure to air pollution in 1935 (PM2.5 = 14.03 μg/m3, NO2 = 5.35 μg/m3) was positively linked but not statistically significant to all-cause dementia [PM2.5 hazard ratio (HR) = 1.16, 95% confidence interval (CI) = 0.90, 1.49; NO2 HR = 1.13, 95% CI = 0.88, 1.47] and AD (PM2.5 HR = 1.38, 95% CI = 1.00, 1.91; NO2 HR = 1.35, 95% CI = 0.92, 1.99). In the competing risk model, one SD elevated PM2.5 exposure (1.12 μg/m3) in 1990 was inversely associated with dementia (subdistribution HR = 0.82, 95% CI = 0.67, 0.99) at P = 0.034 but not after FDR correction (P FDR = 0.442). Higher cumulative PM2.5 per one SD was associated with an increased risk of all-cause dementia and AD for all accumulation models except for the early-life model. The in-utero and early-life exposure to PM2.5 and NO2 was associated with higher AD and all-cause dementia risk, suggesting a sensitive/critical period. Cumulative exposure to PM2.5 across the life course was associated with higher dementia risk. Midlife PM2.5 exposure's negative association with all-cause dementia risk may stem from unaddressed confounders or bias.

Indoor air quality and human health risk assessment in selected high-altitude villages of Gilgit-Baltistan, Pakistan.

This research marks the inaugural endeavor in Gilgit-Baltistan (GB) to identify the primary sources of household energy and indoor air pollutants (IAPs) during the winter and additionally, to evaluate the health impacts associated with IAPs within specific high-altitude communities in Gilgit-Baltistan, Pakistan. Using the convenience sampling method, 20 households were continuously monitored to assess IAPs based on standards time-weighted average. The study found that 90% of the population relied primarily on animal dung as their main energy source, with wood, agricultural residues, electricity, and gas as other sources. The average levels of PM2.5 were five times greater, and CO levels were three times higher than the National Environmental Quality Standards (NEQS). Among the samples examined, 65% of homes were found to have inadequate ventilation and did not comply with ASHRAE standards for living rooms. Households using animal dung and wood as fuel showed elevated PM2.5 and CO levels. Health data indicated increased winter illness, with high rates of respiratory and cardiovascular issues such as morning cough (17%), eye irritation (15%), bronchitis (14%), wheezing (13%), chest tightness (12%), heart disease (11%), morning phlegm (10%), and shortness of breath (8%). The findings indicate that socioeconomic and geographic factors play a significant role in choosing solid fuels. Recommendations include raising awareness of stove maintenance and the harmful impacts of IAPs and proper ventilation, promoting cleaner fuels, and upgrading heating systems. The government should provide health screenings and subsidies for cleaner energy such as hydropower, LPG, and solar power, reducing reliance on dung and wood, and improving health in high-altitude communities.

Comparing indoor and outdoor temperature and air pollution at an urban cooling center: a multiyear case study

Abstract Anthropogenic climate change and urbanization have resulted in increasing needs to provide public health protection from the hazards of elevated PM2.5, elevated ozone and extreme heat (or cold) to the public. This is particularly urgent issue for sensitive groups that are most subject to these hazards, such as the elderly, children, health compromised, and homeless populations. In this study, we compare the indoor versus outdoor temperature and air pollution levels between 17 May 2022 and 31 October 2023 in Salt Lake City, Utah at an older (Northwest Recreation Center) and newer building (Northwest Community Center). Differences between indoor and outdoor ozone were pronounced, with mean indoor ozone being generally ~40% of outdoor values. Indoor PM2.5 concentrations compared to outdoor show seasonal variability and were up to 200% higher during wildfires compared to indoor concentration during wintertime inversion events. We found that the older building was less protective against ozone and wilfire PM2.5 pollution than the newer building. Future research could focus on characterizing and quantifying the impact of building age, insulation, and mechanical ventilation on indoor pollutant migration and separate these factors from human activity.

Preexisting multimorbidity predicts greater mortality risks related to long-term PM2.5 exposure.

Long-term health risk assessments related to ambient fine particulate matter (PM2.5) exposure have been more limited to general population but not towards individuals suffering from multimorbidity. While both multimorbidity and PM2.5 are independently linked to elevated mortality risk, their combined effects and interactions remain practically unexplored. A cross-cohort analysis was undertaken on data from 3 prospective cohorts, initially enrolling 869038 adults aged ≥18 years followed up during 2005-2022. Multimorbidity was identified at baseline surveys through a list of nine common chronic conditions. Cox proportional hazards models were utilized to quantify the associations of long-term PM2.5 exposure with all-cause, cardiovascular, and respiratory mortality among individuals with and without multimorbidity. Joint effects and interactions between baseline multimorbidity and PM2.5 level on the additive and multiplicative scales were examined. Risk differences of PM2.5-induced mortality were analyzed stratified by number of chronic conditions and multimorbidity patterns. Subgroup and sensitivity analyses were carried out to evaluate the consistency of the findings. Among 713119 eligible participants for primary analysis, 65490 prevalent cases of multimorbidity were identified at baseline over a median follow-up of 12.2 years. Compared to individuals without multimorbidity, associations of PM2.5 exposure with all-cause and cardiovascular mortality were more prominent among multimorbidity individuals (P <0.05 for heterogeneity). Our analysis unveiled a significant additive interaction between PM2.5 level and preexisting multimorbidity status, yielding estimated attributable proportions of 11.7%-17.8% and excess risks of 31.1%-72.6% for different mortality outcomes. Sex subgroup and sensitivity analyses consistently produced similar results. This large-scale multicohort analysis demonstrated markedly stronger associations between PM2.5 levels and risks of all-cause and cardiovascular mortality in multimorbidity populations compared to those without multimorbidity. PM2.5 exposure and preexisting multimorbidity showed synergistic effects in triggering mortality events, wherein the joint risks were intensified with elevated PM2.5 levels and an increased number of chronic conditions.

Spatiotemporal modeling of long-term PM2.5 concentrations and population exposure in Greece, using machine learning and statistical methods.

The lack of high-resolution, long-term PM2.5 observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial modeling approach to address this gap and assess PM2.5 spatial variability for the first time on a national level in Greece, by integrating in situ observations, meteorology, emissions and satellite AOD data among others. A high-resolution (1km2) gridded dataset of PM2.5 concentrations across Greece from 2015 to 2022 was developed, and seven statistical, machine learning, and hybrid models were evaluated under different prediction scenarios. Random Forest (RF) models demonstrated superior performance, (R2=0.73, MAE=2.2μgm-3), validated against ground-based measurements. Winter months consistently showed the highest PM2.5 levels, averaging 16.8μgm-3, over the domain, due to residential biomass burning (BB) and limited atmospheric dispersion. Summer months had the lowest concentrations, averaging 10.3μgm-3, while substantial decreases nationwide were observed during the 2020 COVID-19 lockdown. Population exposure analysis indicated that the entire Greek population was exposed to long-term PM2.5 concentrations exceeding the WHO air quality guideline (AQG) of 5μgm-3. Moreover, the dataset revealed elevated PM2.5 levels across several regions of mainland Greece. Notably, 70% to 90% of the population experience levels exceeding 10μgm-3 in Central and Northern regions of continental Greece like Thessaly, Central Macedonia, and Ioannina. The Ioannina region, which is severely impacted by residential BB, recorded pollution levels up to five times the WHO AQG highlighting the urgent need for targeted interventions. The high-resolution RF model's superior performance for monthly average concentrations, compared to the Copernicus Atmosphere Monitoring Service (CAMS) dataset, renders it a reliable tool for long-term PM2.5 assessment in Greece that can support air quality management and health studies.

Elevated PM2.5 Levels Associated with Incense Burning in Bangalamukhi Temple, Lalitpur, Nepal

Elevated PM2.5 Levels Associated with Incense Burning in Bangalamukhi Temple, Lalitpur, Nepal

Influence of an abnormally cold stratospheric polar vortex on the sub-regional PM2.5 anomaly in East Asia in March of 2021

This study investigates the critical role of meteorological variability, particularly polar vortex dynamics, in shaping PM2.5 anomaly patterns across sub-regions in East Asia. Following sudden stratospheric warming (SSW) in winter, East Asia experienced significant tropospheric transitions, including cooling-to-warming shifts. The strengthening of the Mongolian high, combined with tropospheric warming, altered lower tropospheric dynamics. Contrasting warming in Mongolia and cooling to the north intensified baroclinic instability, creating anomalous Mongolian cyclones for increased dust storm activity and higher PM2.5 levels. Elevated PM2.5 concentrations in eastern China, driven by substantial anthropogenic emissions, were dispersed by the divergent flow under the Mongolian high. Cyclones with increased baroclinic instability over southern and northeastern China contributed to precipitation, which led to negative anomaly variations of PM2.5 aerosols. Downwind regions like Korea saw elevated PM2.5 anomalies due to stable lower tropospheric conditions driven by the movement of the warmed Mongolian high.

Impact of wildfires and PM2.5 on ST-elevation acute coronary syndrome in Rosario City: a case-crossover study

ABSTRACT We conducted a modified case-crossover study to examine whether wildfires in the Paraná River Delta departments surrounding Rosario, Argentina, during 2021–2022 increased PM₂.₅ concentrations in this city and if this rise subsequently elevated the risk of ST-elevation myocardial infarction (STEMI). We first observed that daily PM₂.₅ levels in Rosario were significantly higher on wildfire days compared to non-wildfire days (34 µg/m3 vs. 25.75 µg/m3, p < 0.01). In the case-crossover analysis, we found that the odds of STEMI were significantly higher on days when PM₂.₅ concentrations exceeded 20 µg/m3 (OR 2.2 [95% CI 1.2–4.2, p = 0.02]). Additionally, the odds of STEMI increased with higher PM₂.₅ concentrations, with a significant difference when comparing the first and fourth quartiles concentration (OR 1.76 [95% CI 1.06–2.92, p = 0.03] and OR 2.02 [95% CI 1.25–3.27, p < 0.01], respectively). In conclusion, wildfires in the surrounding departments of Rosario City in the Paraná River Delta led to elevated PM₂.₅ levels, and this increase was associated with a higher risk of STEMI hospital admissions.

Seasonal Inter-comparison of Fine Particulate Matter (PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt;) Over Addis Ababa, Ethiopia

The seasonality of meteorology significantly influences the distribution of atmospheric pollution that have harmful effect on human, environment and economy. Similarly, Ethiopia has erratic seasons, this can impact air pollution. Thus, this study focused on intercomparison of PurpleAir PM&amp;lt;sub&amp;gt;2.5 &amp;lt;/sub&amp;gt;measurement at Addis Ababa city. The existing data processed by R software. Accordingly, the finding show that, during the rainy season, PM&amp;lt;sub&amp;gt;2.5 &amp;lt;/sub&amp;gt;levels exhibit a consistent pattern with concentrations peaking in the early night and reaching their lowest at midday. At Black Lion Hospital (BLH), peak concentrations extend to midday due to &amp;lt;i&amp;gt;due to heavy traffic and cross-sectional jams to travel commercial areas&amp;lt;/i&amp;gt;. In contrast, during the semi-rainy and dry seasons, PM&amp;lt;sub&amp;gt;2.5 &amp;lt;/sub&amp;gt;levels peak in the early morning and decrease by midday. Hourly variations in PM&amp;lt;sub&amp;gt;2.5 &amp;lt;/sub&amp;gt;concentrations could be influenced by factors such as temperature inversion, wind, relative humidity, and solar intensity, alongside transportation and industrial activities. Analysis reveals that a significant proportion of the seasonal hourly mean trend during the rainy season, vary in between 30 µg/m3 to 50 µg/m3 of the hourly data while 15 µg/m3 to 40 µg/m3 of data in both the semi-rainy and dry seasons also surpass these guidelines. Despite the general reduction in pollution levels due to rain, the rainy season still contributes to elevated PM&amp;lt;sub&amp;gt;2.5 &amp;lt;/sub&amp;gt;concentrations, posing substantial risks to human health, the environment, and development activities. The monthly mean pattern further highlights a peak in PM&amp;lt;sub&amp;gt;2.5 &amp;lt;/sub&amp;gt;concentrations during the rainy season, underscoring the complex dynamics of air quality. This finding emphasizes the need for targeted strategies to manage pollution throughout the year. The finding suggest that, expand air quality monitoring, and reduce traffic emissions, strengthen industrial regulations and increase public awareness. It may relevance for air quality management strategies for local and regional governments.

Influence of seasonal variation on spatial distribution of PM2.5 concentration using low-cost sensors.

Fine particulate matter (PM2.5) is one of the major airborne pollutants in urban environments and is associated with severe health impacts. In this study, a dense network of low-cost sensor (LCS) is used to cover large spatial area and detect ambient PM2.5 concentration in Guwahati city. The measurements were conducted at multiple sites in different seasons between July 2022 and June 2023. Seasonal variability significantly influences regional meteorology, aerosol optical depth (AOD), and PM2.5 concentration. The seasonal average PM2.5 concentration was highest during winter (113.05µgm -3), followed by post-monsoon (56.11µgm-3), then pre-monsoon (46.60µgm-3), and least for monsoon (32.36µgm-3) season. The elevated PM2.5 concentrations may be attributed to environmental conditions (low ambient temperature, calm wind, and low planetary boundary layer height) that resulted in the least dispersion of PM2.5. The concentration-weighted trajectory (CWT) analysis identifies the effect of regional (Indo-Gangetic Plain and northeast region) and transboundary (Bay of Bengal, Bangladesh, and northwest Asian countries) transported air masses on urban air quality. Post-monsoon and winter season has a high influence on long-range transported aerosols, whereas the monsoon and pre-monsoon seasons are affected by ocean and land air masses. Changes in surrounding activities and meteorology influence spatial distribution of PM2.5 particles. Elevated PM2.5 concentrations were recorded at in-city and outskirt sites because of the nearby activities (industry and traffic) and build-up area. In meteorology, wind significantly affects spatial dispersion of PM2.5 concentration to the sites located in upwind and downwind directions.

Geospatial Evaluation of Ambient Air Quality in Akure South Local Government Area, Ondo State, Nigeria

The study evaluates the ambient air quality in Akure South Local Government Area, Ondo State, Nigeria, focusing on particulate matter (PM2.5 and PM10) and its health implications on residents. Primary data were collected using Purple Air Monitor devices at 28 ground stations and structured questionnaires, base maps. The study utilized QGIS for mapping, SPSS for statistical analysis and WHO’s AirQ software for health risk assessment. Findings indicated exceedances of WHO and USEPA recommended limits for PM2.5 and PM10, with areas near major highways showing elevated PM levels. The health risk assessment revealed potential increases in hospitalizations and mortality due to air pollution. This research contributes uniquely by applying geospatial evaluation and health risk assessment tools for comprehensive air quality analysis. It highlights the urgent need for effective air quality management strategies, including the deployment of air quality monitoring stations and public education campaigns. The study provides crucial insights for policy makers, emphasizing the importance of mitigating air pollution’s adverse effects on urban settings. Received: 15 September 2024 / Accepted: 8 November 2024 / Published: 20 November 2024

Towards Cleaner Cities: Estimating Vehicle-Induced PM2.5 with Hybrid EBM-CMA-ES Modeling.

In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM2.5 concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R2 of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM2.5 concentrations, followed by humidity and temperature. Elevated PM2.5 levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM2.5 levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM2.5 concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management.

Objective Air Pollution and the Subjective Well-being of Chinese Residents

In this study, we constructed a new database to investigate the complex relationship between hazy weather, heightened air pollution concerns due to elevated PM 2.5 levels, and residents’ well-being in China. Our study concludes that the results of the baseline regression showing that PM2.5 significantly increases the well-being of the population is at odds with common sense, which is attributed to potential endogeneity issues, including reverse causation and omitted variables. For this purpose, first, we used the instrumental variables method to exclude the endogeneity problem and obtained a causal relation between objective air pollution and well-being, that is, objective air pollution significantly and negatively affects residents’ well-being. Second, we also considered the effect of individual differences and verified the mechanistic pathways by which objective air pollution affects well-being through residents’ subjective air pollution. Specifically, we found that in the air quality evaluation process, “pessimistic” residents have a lower level of well-being, and “optimistic” residents have a higher level of well-being. Finally, we verified that the level of household income can weaken the negative impact of objective air pollution on residents’ well-being and has a positive moderating effect. By integrating macro and micro-level data through innovative technology, our research provides policy recommendations for effective pollution management and improved overall well-being in China.

Preoperative Exposure to Fine Particulate Matter and Risk of Postoperative Complications: A Single Center Observational Cohort Bayesian Analysis.

While exposure to fine particulate matter air pollution (PM 2.5 ) is known to cause adverse health effects, its impact on postoperative outcomes in US adults remains understudied. Perioperative exposure to PM 2.5 may induce inflammation that interacts insidiously with the surgical stress response, leading to higher postoperative complications. We conducted a single center, retrospective cohort study using data from 49,615 surgical patients living along Utah's Wasatch Front and who underwent elective surgical procedures at a single academic medical center from 2016-2018. Patients' addresses were geocoded and linked to daily Census-tract level PM 2.5 estimates. We hypothesized that elevated PM 2.5 concentrations in the week prior to surgery would be associated with an increase in a bundle of major postoperative complications. A hierarchical Bayesians regression model was fit adjusting for age, sex, season, neighborhood disadvantage, and the Elixhauser index of comorbidities. Postoperative complications increased in a dose-dependent manner with higher concentrations of PM 2.5 exposure, with a relative increase of 8% in the odds of complications (OR=1.082) for every 10ug/m 3 increase in the highest single-day 24-hr PM 2.5 exposure during the 7 days prior to surgery. For a 30 fold increase in PM 2.5 (1 ug/m 3 to 30ug/m 3 ) the odds of complication rose to over 27% (95%CI: 4%-55%). The association persisted after controlling for comorbidities and confounders; our inferences were robust to modeling choices and sensitivity analysis. In this large Utah cohort, exposure to elevated PM 2.5 concentrations in the week before surgery was associated with a dose-dependent increase in postoperative complications, suggesting a potential impact of air pollution on surgical outcomes. These findings merit replication in larger datasets to identify populations at risk and define the interaction and impact of different pollutants. PM 2.5 exposure is a potential perioperative risk factor and, given the unmitigated air pollution in urban areas, a global health concern.

Compound dry-hot-fire events connecting Central and Southeastern South America: an unapparent and deadly ripple effect

South America has experienced severe compound drought-heatwaves (CDHW), exacerbating fires. Recently, the unprecedented Pantanal 2020 fire season (P20F), burning a third of the biome, resulted in well-reported local impacts on the ecosystem, economy, and health. Nevertheless, the long-range ripple effects of this event remain unknown. We investigated the P20F-related cascading hazards, integrating models, observational and satellite-based data. P20F-related smoke elevated PM2.5 levels in the SA’s most populated area, exceeding WHO guidelines by up to 600%. Smoke-induced air pollution episodes coincided with widespread heatwaves, amplifying health risks. The mortality burden attributable to this multi-hazard short-term (14 days) exposure was estimated to be 2150 premature deaths (21% increase above expected levels). Our findings highlight that the impacts of CDHW-fires in SA are beyond the local level, implying growing challenges for risk management and public health and the need for governance based on telecoupled flows, linking different systems over multiple scales.

Impact of fine particulate matter (PM2.5) on ocular health among people living in Chiang Mai, Thailand

Considering the limited information on the impact of PM2.5 content on ocular health, a follow-up study was conducted on 50 healthy adults. Samples were collected twice, once before the PM2.5 exposure season and again after exposure. Daily PM2.5 concentration data was gathered from Thung Satok monitoring station. All subjects completed the self-structured ocular symptom questionnaire. The concentrations of 1-OHP were determined using HPLC-FLD. Logistic regression analysis investigated the relationship between PM2.5 toxicity and ocular symptoms. The findings revealed that daily PM2.5 concentrations surpassed the WHO-recommended range by around threefold. Exposure to PM2.5 significantly raised the likelihood of ocular redness (adjusted OR: 12.39, 95% CI), watering (adjusted OR: 2.56, 95% CI), and dryness (adjusted OR: 5.06, 95% CI). Additionally, these symptoms had an exposure-response relationship with increasing 1-OHP levels. Ocular symptoms worsened in frequency and severity during the high PM2.5 season, showing a strong link to elevated PM2.5 levels. Lymphocyte counts were also positively correlated with redness, watering, and dryness during high PM2.5 exposure. In conclusion, our study shows that subjects exposed to higher PM2.5 levels presented more significant ocular surface alterations.

Dust over water: Analyzing the impact of lake desiccation on dust storms on the Iranian Plateau

The desiccation of lakes on the Iranian Plateau, driven by both natural processes and anthropogenic activities, has led to significant land cover changes, exposing lake beds and triggering adverse environmental consequences. These transformations pose serious threats to ecological conditions and the provision of ecosystem services. This study investigates the potential links between lake desiccation and the occurrence of dust storms across major lakes on the Iranian Plateau. By utilizing remote sensing data and time series statistical analysis, including the Mann-Kendall test, Ordinary Least Square (OLS) regression, and frequency analysis, we assessed changes in lake extent and their effects on Aerosol Optical Depth (AOD) and Particular Matter (PM2.5) from 1985 to 2020. An analysis of Landsat imagery reveals that lake desiccation commenced in the late 1990s, exhibiting a marked downward trend that has intensified in recent years. The results show a significant inverse correlation between aerosol parameters (AOD and PM2.5) and lake extent. Reduced lake area is associated with elevated AOD and PM2.5 concentrations, with the highest aerosol levels observed when lakes reach their smallest extent. Notably, despite an 81 % reduction in the lake area, dust storm occurrences have increased by 20%. These findings highlight the critical role of lake desiccation in exacerbating dust storms in the surrounding regions. Furthermore, the temporal alignment between fluctuations in lake extent and dust storm frequency is validated through regression analysis with a 95 % confidence level. The consistency between our results and existing literature underscores the reliability of the observed relationship, emphasizing the urgent need to address the environmental consequences of lake desiccation to mitigate its broader ecological impacts and potential human health impacts.Graphical Abstract

Ambient particulate matter and frequency of outpatient visits for chronic rhinosinusitis in the United States.

Emerging evidence has underscored the harmful effects of air pollution on the upper airway. We investigated the relationship between ambient particulate matter (PM) level and the frequency of outpatient visits for chronic rhinosinusitis (CRS). We conducted an ecological cohort study of US adults enrolled in The Merative MarketScan outpatient database from 2007 to 2020. For each geographical subunit (core-based statistical area [CBSA]), we calculated the annual rate of CRS-related outpatient visits per 1000 well-patient checkup visits (CRS-OV). Using data from the Environmental Protection Agency's Air Quality System, we mapped the rolling statistical average of daily PM2.5 and PM10 over the preceding year onto each CBSA×year combination. We employed multivariable negative binomial regression modeling to estimate the association between PM levels and subsequent CRS-OV. Across 3933 observations (CBSA×year combinations), encompassing ∼4 billion visits, the median CRS-OV was 164 (interquartile range 110-267). The mean PM2.5 level was 8.9µg/m3 (SD 2.6) and the mean PM10 level was 20.2µg/m3 (SD 7.2). Adjusting for patient demographics and respiratory comorbidities, a compounded rise in subsequent CRS-OV was observed with increasing PM levels. Each µg/m3 rise in PM2.5 independently predicted a 10% increase in CRS-OV (adjusted incidence rate ratio [aIRR]) 1.10, 95% confidence interval [CI] 1.08-1.13) and each µg/m3 rise in PM10 independently predicted a 3% increase in CRS-OV (aIRR 1.03, 95% CI 1.02-1.04). Elevated ambient PM2.5 and PM10 levels are associated with a subsequent compounded increase in the frequency of CRS-OV, with PM2.5 predicting a more pronounced rise compared to PM10.

Heart rate variability, electrodermal activity and cognition in adults: Association with short-term indoor PM2.5 exposure in a real-world intervention study

BackgroundLong-term effects of ambient fine particulate matter (PM2.5) exposure on mortality and morbidity are well established. The study aims to evaluate how short-term indoor PM2.5 exposure affects physiological responses and understand potential mechanisms mediating the cognitive outcomes in working-age adults. MethodsThis real-world randomized single-blind crossover intervention study was conducted in an urban office setting, with desk-based air purifiers used as the intervention. Participants (N = 40) were exposed to average PM2.5 levels of 18.0 μg/m3 in control and 3.7 μg/m3 in intervention conditions. Cognitive tests, heart rate variability (HRV), and electrodermal activity (EDA) measures were conducted after 5 h of exposure. Self-reported mental effort, exhaustion, and task difficulty were collected after the cognitive tests. ResultsParticipants in the intervention condition had significantly higher HRV during cognitive testing, particularly in the standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), and high-frequency power (HF) indices. Mediation analysis revealed that elevated PM2.5 exposure reduced HRV indices, which mediated the effect on two executive function-related cognitive skills out of 16 assessed skills. No significant differences were found in EDA, self-reported task difficulty, or exhaustion, but self-reported mental effort was higher in the control condition. ConclusionsLower indoor PM2.5 level was associated with reduced mental effort and higher HRV during cognitive testing. Furthermore, the association between indoor PM2.5 exposure and executive function might be mediated through cardiovagal responses. These findings provide insights on the mechanisms through which fine particle exposure adversely affects the autonomic nervous system and how this in turn affects cognition. The potential cardiovascular and cognitive health benefits of PM2.5 reduction warrants further research.