Indirect Effects of PM2.5 Exposure on COVID-19 Mortality in Greater Jakarta, Indonesia: An Ecological Study.

Background: Air pollution, including PM2.5, was suggested as one of the primary contributors to COVID-19 fatalities worldwide. Jakarta, the capital city of Indonesia, was recognized as one of the ten most polluted cities globally. Additionally, the incidence of COVID-19 in Jakarta surpasses that of all other provinces in Indonesia. However, no study has investigated the correlation between PM2.5 concentration and COVID-19 fatality in Jakarta. Objective: To investigate the correlation between short-term and long-term exposure to PM2.5 and COVID-19 mortality in Greater Jakarta area. Methods: An ecological time-trend study was implemented. The data of PM2.5 ambient concentration obtained from Nafas Indonesia and the National Institute for Aeronautics and Space (LAPAN)/National Research and Innovation Agency (BRIN). The daily COVID-19 death data obtained from the City's Health Office. Findings: Our study unveiled an intriguing pattern: while short-term exposure to PM2.5 showed a negative correlation with COVID-19 mortality, suggesting it might not be the sole factor in causing fatalities, long-term exposure demonstrated a positive correlation. This suggests that COVID-19 mortality is more strongly influenced by prolonged PM2.5 exposure rather than short-term exposure alone. Specifically, our regression analysis estimate that a 50 µg/m3 increase in long-term average PM2.5 could lead to an 11.9% rise in the COVID-19 mortality rate. Conclusion: Our research, conducted in one of the most polluted areas worldwide, offers compelling evidence regarding the influence of PM2.5 exposure on COVID-19 mortality rates. It emphasizes the importance of recognizing air pollution as a critical risk factor for the severity of viral respiratory infections.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Cold waves and fine particulate matter in high-altitude Chinese cities: assessing their interactive impact on outpatient visits for respiratory disease.

BACKGROUND: Extreme weather events like heatwaves and fine particulate matter (PM2.5) have a synergistic effect on mortality, but research on the synergistic effect of cold waves and PM2.5 on outpatient visits for respiratory disease, especially at high altitudes in climate change-sensitive areas, is lacking. METHODS: we collected time-series data on meteorological, air pollution, and outpatient visits for respiratory disease in Xining. We examined the associations between cold waves, PM2.5, and outpatient visits for respiratory disease using a time-stratified case-crossover approach and distributional lag nonlinear modeling. Our analysis also calculated the relative excess odds due to interaction (REOI), proportion attributable to interaction (AP), and synergy index (S). We additionally analyzed cold waves over time to verify climate change. RESULTS: Under different definitions of cold waves, the odds ratio for the correlation between cold waves and outpatient visits for respiratory disease ranged from 0.95 (95% CI: 0.86, 1.05) to 1.58 (1.47, 1.70). Exposure to PM2.5 was significantly associated with an increase in outpatient visits for respiratory disease. We found that cold waves can synergize with PM2.5 to increase outpatient visits for respiratory disease (REOI > 0, AP > 0, S > 1), decreasing with stricter definitions of cold waves and longer durations. Cold waves' independent effect decreased over time, but their interaction effect persisted. From 8.1 to 21.8% of outpatient visits were due to cold waves and high-level PM2.5. People aged 0-14 and ≥ 65 were more susceptible to cold waves and PM2.5, with a significant interaction for those aged 15-64 and ≥ 65. CONCLUSION: Our study fills the gap on how extreme weather and PM2.5 synergistically affect respiratory disease outpatient visits in high-altitude regions. The synergy of cold waves and PM2.5 increases outpatient visits for respiratory disease, especially in the elderly. Cold wave warnings and PM2.5 reduction have major public health benefits.

Combining analytical techniques to assess the translocation of diesel particles across an alveolar tissue barrier in vitro.

BACKGROUND: During inhalation, airborne particles such as particulate matter ≤ 2.5 µm (PM2.5), can deposit and accumulate on the alveolar epithelial tissue. In vivo studies have shown that fractions of PM2.5 can cross the alveolar epithelium to blood circulation, reaching secondary organs beyond the lungs. However, approaches to quantify the translocation of particles across the alveolar epithelium in vivo and in vitro are still not well established. In this study, methods to assess the translocation of standard diesel exhaust particles (DEPs) across permeable polyethylene terephthalate (PET) inserts at 0.4, 1, and 3 µm pore sizes were first optimized with transmission electron microscopy (TEM), ultraviolet-visible spectroscopy (UV-VIS), and lock-in thermography (LIT), which were then applied to study the translocation of DEPs across human alveolar epithelial type II (A549) cells. A549 cells that grew on the membrane (pore size: 3 µm) in inserts were exposed to DEPs at different concentrations from 0 to 80 µg.mL- 1 ( 0 to 44 µg.cm- 2) for 24 h. After exposure, the basal fraction was collected and then analyzed by combining qualitative (TEM) and quantitative (UV-VIS and LIT) techniques to assess the translocated fraction of the DEPs across the alveolar epithelium in vitro. RESULTS: We could detect the translocated fraction of DEPs across the PET membranes with 3 µm pore sizes and without cells by TEM analysis, and determine the percentage of translocation at approximatively 37% by UV-VIS (LOD: 1.92 µg.mL- 1) and 75% by LIT (LOD: 0.20 µg.cm- 2). In the presence of cells, the percentage of DEPs translocation across the alveolar tissue was determined around 1% at 20 and 40 µg.mL- 1 (11 and 22 µg.cm- 2), and no particles were detected at higher and lower concentrations. Interestingly, simultaneous exposure of A549 cells to DEPs and EDTA can increase the translocation of DEPs in the basal fraction. CONCLUSION: We propose a combination of analytical techniques to assess the translocation of DEPs across lung tissues. Our results reveal a low percentage of translocation of DEPs across alveolar epithelial tissue in vitro and they correspond to in vivo findings. The combination approach can be applied to any traffic-generated particles, thus enabling us to understand their involvement in public health.

Model misspecification, measurement error, and apparent supralinearity in the concentration-response relationship between PM2.5 and mortality.

A growing number of studies have produced results that suggest the shape of the concentration-response (C-R) relationship between PM2.5 exposure and mortality is "supralinear" such that incremental risk is higher at the lowest exposure levels than at the highest exposure levels. If the C-R function is in fact supralinear, then there may be significant health benefits associated with reductions in PM2.5 below the current US National Ambient Air Quality Standards (NAAQS), as each incremental tightening of the PM2.5 NAAQS would be expected to produce ever-greater reductions in mortality risk. In this paper we undertake a series of tests with simulated cohort data to examine whether there are alternative explanations for apparent supralinearity in PM2.5 C-R functions. Our results show that a linear C-R function for PM2.5 can falsely appear to be supralinear in a statistical estimation process for a variety of reasons, such as spatial variation in the composition of total PM2.5 mass, the presence of confounders that are correlated with PM2.5 exposure, and some types of measurement error in estimates of PM2.5 exposure. To the best of our knowledge, this is the first simulation-based study to examine alternative explanations for apparent supralinearity in C-R functions.

Cross-shift changes in pulmonary function and occupational exposure to particulate matter among e-waste workers in Ghana.

Introduction: Little is known on the association between cross-shift changes in pulmonary function and personal inhalation exposure to particulate matter (PM) among informal electronic-waste (e-waste) recovery workers who have substantial occupational exposure to airborne pollutants from burning e-waste. Methods: Using a cross-shift design, pre- and post-shift pulmonary function assessments and accompanying personal inhalation exposure to PM (sizes <1, <2.5 µm, and the coarse fraction, 2.5-10 µm in aerodynamic diameter) were measured among e-waste workers (n = 142) at the Agbogbloshie e-waste site and a comparison population (n = 65) in Accra, Ghana during 2017 and 2018. Linear mixed models estimated associations between percent changes in pulmonary function and personal PM. Results: Declines in forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) per hour were not significantly associated with increases in PM (all sizes) among either study population, despite breathing zone concentrations of PM (all sizes) that exceeded health-based guidelines in both populations. E-waste workers who worked "yesterday" did, however, have larger cross-shift declines in FVC [-2.4% (95%CI: -4.04%, -0.81%)] in comparison to those who did not work "yesterday," suggesting a possible role of cumulative exposure. Discussion: Overall, short-term respiratory-related health effects related to PM exposure among e-waste workers were not seen in this sample. Selection bias due to the "healthy worker" effect, short shift duration, and inability to capture a true "pre-shift" pulmonary function test among workers who live at the worksite may explain results and suggest the need to adapt cross-shift studies for informal settings.

Gene Expression and Metabolome Analysis Reveals Anti-Inflammatory Impacts of 11,17diHDoPE on PM10-Induced Mouse Lung Inflammation.

Oxylipins, the metabolites of polyunsaturated fatty acids, are vital in regulating cell proliferation and inflammation. Among these oxylipins, specialized pro-resolving mediators notably contribute to inflammation resolution. Previously, we showed that the specialized pro-resolving mediators isomer 11,17dihydroxy docosapentaenoic acid (11,17diHDoPE) can be synthesized in bacterial cells and exhibits anti-inflammatory effects in mammalian cells. This study investigates the in vivo impact of 11,17diHDoPE in mice exposed to particulate matter 10 (PM10). Our results indicate that 11,17diHDoPE significantly mitigates PM10-induced lung inflammation in mice, as evidenced by reduced pro-inflammatory cytokines and pulmonary inflammation-related gene expression. Metabolomic analysis reveals that 11,17diHDoPE modulates inflammation-related metabolites such as threonine, 2-keto gluconic acid, butanoic acid, and methyl oleate in lung tissues. In addition, 11,17diHDoPE upregulates the LA-derived oxylipin pathway and downregulates arachidonic acid- and docosahexaenoic acid-derived oxylipin pathways in serum. Correlation analyses between gene expression and metabolite changes suggest that 11,17diHDoPE alleviates inflammation by interfering with macrophage differentiation. These findings underscore the in vivo role of 11,17diHDoPE in reducing pulmonary inflammation, highlighting its potential as a therapeutic agent for respiratory diseases.

Accumulation of Atmospheric PAHs in White Mustard - Can the Seeds Be Affected?

Traffic-related particulate matter emissions have been considerably reduced due to stringent regulations in Europe. However, emission of diesel-powered vehicles still poses a significant environmental threat, affecting rural ecosystems and agriculture. Several studies have reported that polycyclic aromatic hydrocarbons (PAHs), a group of potentially toxic organic compounds, can accumulate in crops and vegetables. In our study, white mustard (Sinapis alba L.) plants were experimentally treated with an extract of diesel exhaust. PAH concentrations were measured in the different plant compartments (stems, leaves and seeds), bioconcentration factors (BCFs) were also calculated. Significant accumulation was measured in the leaves and seeds, stems showed lower accumulation potential. All plant matrices showed high tendency to accumulate higher molecular weight PAHs, BCF was the highest in the 6-ring group. The fact that considerable accumulation was experienced in the seeds might show the risk of cultivating crops nearby roads highly impacted by traffic-related emissions.

PM2.5 exposure contributes to anxiety and depression-like behaviors via phenyl-containing compounds interfering with dopamine receptor.

As a global problem, fine particulate matter (PM2.5) really needs local fixes. Considering the increasing epidemiological relevance to anxiety and depression but inconsistent toxicological results, the most important question is to clarify whether and how PM2.5 causally contributes to these mental disorders and which components are the most dangerous for crucial mitigation in a particular place. In the present study, we chronically subjected male mice to a real-world PM2.5 exposure system throughout the winter heating period in a coal combustion area and revealed that PM2.5 caused anxiety and depression-like behaviors in adults such as restricted activity, diminished exploratory interest, enhanced repetitive stereotypy, and elevated acquired immobility, through behavioral tests including open field, elevated plus maze, marble-burying, and forced swimming tests. Importantly, we found that dopamine signaling was perturbed using mRNA transcriptional profile and bioinformatics analysis, with Drd1 as a potential target. Subsequently, we developed the Drd1 expression-directed multifraction isolating and nontarget identifying framework and identified a total of 209 compounds in PM2.5 organic extracts capable of reducing Drd1 expression. Furthermore, by applying hierarchical characteristic fragment analysis and molecular docking and dynamics simulation, we clarified that phenyl-containing compounds competitively bound to DRD1 and interfered with dopamine signaling, thereby contributing to mental disorders. Taken together, this work provides experimental evidence for researchers and clinicians to identify hazardous factors in PM2.5 and prevent adverse health outcomes and for local governments and municipalities to control source emissions for diminishing specific disease burdens.

Paeoniflorin attenuates particulate matter-induced acute lung injury by inhibiting oxidative stress and NLRP3 inflammasome-mediated pyroptosis through activation of the Nrf2 signaling pathway.

Particulate matter (PM), the main component of air pollutants, emerges as a research hotspot, especially in the area of respiratory diseases. Paeoniflorin (PAE), known as anti-inflammatory and immunomodulatory effects, has been reported to alleviate acute lung injury (ALI). However, the effect of PAE on PM-induced ALI and the underlying mechanisms are still unclear yet. In this study, we established the PM-induced ALI model using C57BL/6J mice and BEAS-2B cells to explore the function of PAE. In vivo, mice were intraperitoneally injected with PAE (100 mg/kg) or saline 1 h before instilled with 4 mg/kg PM intratracheally and were euthanized on the third day. For lung tissues, HE staining and TUNEL staining were used to evaluate the degree of lung injury, ELISA assay was used to assess inflammatory mediators and oxidative stress level, Immunofluorescence staining and western blotting were applied to explore the role of pyroptosis and Nrf2 signaling pathway. In vitro, BEAS-2B cells were pretreated with 100 µM PAE before exposure to 200 µg/ml PM and were collected after 24h for the subsequent experiments. TUNEL staining, ROS staining, and western blotting were conducted to explore the underlying mechanisms of PAE on PM-induced ALI. According to the results, PAE can attenuate the degree of PM-induced ALI in mice and reduce PM-induced cytotoxicity in BEAS-2B cells. PAE can relieve PM-induced excessive oxidative stress and NLRP3 inflammasome-mediated pyroptosis. Additionally, PAE can also activate Nrf2 signaling pathway and inhibition of Nrf2 signaling pathway can impair the protective effect of PAE by aggravating oxidative stress and pyroptosis. Our findings demonstrate that PAE can attenuate PM-induced ALI by inhibiting oxidative stress and NLRP3 inflammasome-mediated pyroptosis, which is mediated by Nrf2 signaling pathway.

Decoding temporal patterns and trends of PM10 pollution over Delhi: a multi-year analysis (2015-2022).

The current study delved into an extensive analysis of multi-year observations on PM10 to have trends at various time scales in Delhi, India. High-resolution ground observations from all 37 monitoring stations from 2015 to 2022 were used. This study used non-parametric generalized additive model (GAM) based smooth-trend and Theil-Sen slope estimator techniques to analyze temporal trends and variations. The long-term PM10 concentration, both in its ambient and de-seasonalized forms, exhibited a statistically significant decreasing trend. An average decrease of - 7.57 [95% confidence interval (CI) - 16.51, 0.18] µg m-3 year-1 for ambient PM10 and - 8.45 [95% CI - 11.96, - 5.58] µg m-3 year-1 for de-seasonalized PM10 mass concentration was observed. Breaking it down into seasons, we observed significant declines in PM10 concentrations during monsoon (- 10.71 µg m-3 year-1, p < 0.1) and post-monsoon (- 7.49 µg m-3 year-1, p < 0.001). On the other hand, summer and winter displayed statistically insignificant declining trends of - 5.32 µg m-3 year-1 and - 6.06 µg m-3 year-1, respectively. Remarkably, all months except March displayed declining PM10 concentrations, suggesting a gradual reduction in particle pollution across the city. Further analysis of PM10 across various wind sectors revealed a consistent decreasing trend in all wind directions. The most substantial decrease was observed from the northwest (- 10.24 µg m-3 year-1), while the minimum reduction occurred from the east (- 5.67 µg m-3 year-1). Throughout the 8-year study period, the daily average PM10 concentration remained at 228 ± 124 µg m-3, ranging from 33 to 819 µg m-3. Seasonal variations were apparent, with concentrations during winter, summer, monsoon, and post-monsoon seasons averaging 279 ± 133, 224 ± 117, 135 ± 95, and 323 ± 142 µg m-3, respectively. November had the highest and August had the lowest concentration. Weekend PM10 concentration is slightly lower than weekdays. These findings emphasize the need for more stringent government action plans.

Temporal dynamics of urban air pollutants and their correlation with associated meteorological parameters: an investigation in northern Indian cities.

This study delves into the intricate dynamics of air pollution in the rapidly expanding northern regions of India, examining the intertwined influences of agricultural burning, industrialization, and meteorological conditions. Through comprehensive analysis of key pollutants (PM2.5, PM10, NO2, SO2, CO, O3) across ten monitoring stations in Uttar Pradesh, Haryana, Delhi, and Punjab, a consistent pattern of high pollution levels emerges, particularly notable in Delhi. Varanasi leads in SO2 and O3 concentrations, while Moradabad stands out for CO levels, and Jalandhar for SO2 concentrations. The study further elucidates the regional distribution of pollutants, with Punjab receiving significant contributions from SW, SE, and NE directions, while Haryana and Delhi predominantly face air masses from SE and NE directions. Uttar Pradesh's pollution sources are primarily local, with additional inputs from various directions. Moreover, significant negative correlations (p < 0.05) between PM10, NO2, SO2, O3, and relative humidity (RH) underscore the pivotal role of meteorological factors in shaping pollutant levels. Strong positive correlations between PM2.5 and NO2 (0.71 to 0.93) suggest shared emission sources or similar atmospheric conditions in several cities. This comprehensive understanding highlights the urgent need for targeted mitigation strategies to address the multifaceted drivers of air pollution, ensuring the protection of public health and environmental sustainability across the region.

The effect of fine particulate matter (PM2.5) pollution on health inequality: an intergenerational perspective.

Air pollution poses a serious challenge to public health and simultaneously exacerbating regional & intergenerational health inequality. This research introduces PM2.5 pollution into the intergenerational health transmission model, and estimates its impact on health inequality in China using Ordered Logit Regression (OLR) and Multi-scale Geographically Weighted Regression (MGWR) model. The results indicate that PM2.5 pollution exacerbate the intergenerational health inequality, and its impacts show inconsistency across family income levels, parental health insurance status, and area of residence. Specifically, it is more difficult for offspring in low-income families to escape from the influence of unhealthy family to become upwardly mobile. Additionally, this health inequality is more significant in households in which at least one parent does not have health insurance. Moreover, the intergenerational solidification caused by PM2.5 pollution is higher in the east and lower in the west. Both the PM2.5 level and solidification effect are high in Beijing-Tianjin-Hebei region, Yangtze River Delta region and central areas of China, which is the focus of air pollution management. These findings suggest that more emphasis should be placed on family-based health promotion. In areas with high PM2.5 pollution levels, resources, subsidies and air pollution protection should be provided for less healthy families with lower incomes and no health insurance.

Long-term exposure to PM2.5 and mortality in a national cohort in South Korea: effect modification by community deprivation, medical infrastructure, and greenness.

BACKGROUND: Long-term exposure to PM2.5 has been linked to increased mortality risk. However, limited studies have examined the potential modifying effect of community-level characteristics on this association, particularly in Asian contexts. This study aimed to estimate the effects of long-term exposure to PM2.5 on mortality in South Korea and to examine whether community-level deprivation, medical infrastructure, and greenness modify these associations. METHODS: We conducted a nationwide cohort study using the National Health Insurance Service-National Sample Cohort. A total of 394,701 participants aged 30 years or older in 2006 were followed until 2019. Based on modelled PM2.5 concentrations, 1 to 3-year and 5-year moving averages of PM2.5 concentrations were assigned to each participant at the district level. Time-varying Cox proportional-hazards models were used to estimate the association between PM2.5 and non-accidental, circulatory, and respiratory mortality. We further conducted stratified analysis by community-level deprivation index, medical index, and normalized difference vegetation index to represent greenness. RESULTS: PM2.5 exposure, based on 5-year moving averages, was positively associated with non-accidental (Hazard ratio, HR: 1.10, 95% Confidence Interval, CI: 1.01, 1.20, per 10 µg/m3 increase) and circulatory mortality (HR: 1.22, 95% CI: 1.01, 1.47). The 1-year moving average of PM2.5 was associated with respiratory mortality (HR: 1.33, 95% CI: 1.05, 1.67). We observed higher associations between PM2.5 and mortality in communities with higher deprivation and limited medical infrastructure. Communities with higher greenness showed lower risk for circulatory mortality but higher risk for respiratory mortality in association with PM2.5. CONCLUSIONS: Our study found mortality effects of long-term PM2.5 exposure and underlined the role of community-level factors in modifying these association. These findings highlight the importance of considering socio-environmental contexts in the design of air quality policies to reduce health disparities and enhance overall public health outcomes.

PM2.5 regulates the progression of lung adenocarcinoma through the axis of HCG18, miR-195 and ATG14.

Relevant studies have indicated the association of HCG18 with tumour occurrence and progression. In this study, we observed that PM2.5 can enhance the growth of lung adenocarcinoma cells by modulating the expression of HCG18. Further investigations, including overexpression and knockout experiments, elucidated that HCG18 suppresses miR-195, which in turn upregulates the expression of ATG14, resulting in the upregulation of autophagy. Consequently, exposure to PM2.5 leads to elevated HCG18 expression in lung tissues, which in turn increases Atg14 expression and activates autophagy pathways through inhibition of miR-195, thereby contributing to oncogenesis.

Salidroside Ameliorates Lung Injury Induced by PM 2.5by Regulating SIRT1-PGC-1α in Mice.

Objective: This study aimed to clarify the intervention effect of salidroside (SAL) on lung injury caused by PM 2.5 in mice and illuminate the function of SIRT1-PGC-1ɑ axis. Methods: Specific pathogen-free (SPF) grade male C57BL/6 mice were randomly assigned to the following groups: control group, SAL group, PM 2.5 group, SAL+PM 2.5 group. On the first day, SAL was given by gavage, and on the second day, PM 2.5 suspension was given by intratracheal instillation. The whole experiment consist of a total of 10 cycles, lasting 20 days. At the end of treatment, blood samples and lung tissues were collected and analyzed. Observation of pathological changes in lung tissue using inverted microscopy and transmission electron microscopy. The expression of inflammatory, antioxidants, apoptosis, and SIRT1-PGC-1ɑ proteins were detected by Western blotting. Results: Exposure to PM 2.5 leads to obvious morphological and pathologica changes in the lung of mice. PM 2.5 caused a decline in levels of antioxidant-related enzymes and protein expressions of HO-1, Nrf2, SOD2, SIRT1 and PGC-1ɑ, and an increase in the protein expressions of IL-6, IL-1ß, Bax, caspase-9 and cleaved caspase-3. However, SAL reversed the aforementioned changes caused by PM 2.5 by activating the SIRT1-PGC-1α pathway. Conclusion: SAL can activate SIRT1-PGC-1ɑ to ameliorate PM 2.5-induced lung injury.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain.

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33 years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

The stress-responsive cytotoxic effect of diesel exhaust particles on lymphatic endothelial cells.

Diesel exhaust particles (DEPs) are very small (typically < 0.2 µm) fragments that have become major air pollutants. DEPs are comprised of a carbonaceous core surrounded by organic compounds such as polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs. Inhaled DEPs reach the deepest sites in the respiratory system where they could induce respiratory/cardiovascular dysfunction. Additionally, a previous study has revealed that a portion of inhaled DEPs often activate immune cells and subsequently induce somatic inflammation. Moreover, DEPs are known to localize in lymph nodes. Therefore, in this study we explored the effect of DEPs on the lymphatic endothelial cells (LECs) that are a constituent of the walls of lymph nodes. DEP exposure induced cell death in a reactive oxygen species (ROS)-dependent manner. Following exposure to DEPs, next-generation sequence (NGS) analysis identified an upregulation of the integrated stress response (ISR) pathway and cell death cascades. Both the soluble and insoluble components of DEPs generated intracellular ROS. Three-dimensional Raman imaging revealed that DEPs are taken up by LECs, which suggests internalized DEP cores produce ROS, as well as soluble DEP components. However, significant cell death pathways such as apoptosis, necroptosis, ferroptosis, pyroptosis, and parthanatos seem unlikely to be involved in DEP-induced cell death in LECs. This study clarifies how DEPs invading the body might affect the lymphatic system through the induction of cell death in LECs.

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities.

BACKGROUND: Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-µg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition.

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5.