[Association between exposure to PM_(2.5) and chronic kidney disease in a population with metabolic syndrome].

OBJECTIVE: To investigate the association between long-term fine particulate matter(PM_(2.5)) exposure and the risk of chronic kidney disease(CKD) in people with abnormal metabolism syndrome(MS) components. METHODS: Based on health checkup data from a hospital in Beijing, a retrospective cohort study was used to collect annual checkup data from 2013-2019. A questionnaire was used to obtain information on demographic characteristics and lifestyle habits. We measured blood pressure, height, weight, waist circumference, concentrations of triglycerides(TG), fasting glucose, and high-density lipoprotein cholesterol(HDL-C). Longitude and latitude were also extracted from the addresses of the study subjects for pollutant exposure data estimation. Logistic regression models were used to explore the estimated effect of long-term PM_(2.5) exposure on the risk of CKD prevalence in people with abnormal MS components. Two-pollutant and multi-pollutant models were developed to test the stability of these result. Subgroup analysis was conducted based on age, the presence of MS, individual MS component abnormalities, and dual-component MS abnormalities. RESULTS: The study included 1540 study subjects with abnormal MS components at baseline, 206 with CKD during the study period. The association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions was statistically significant, with a 2.26-fold increase in risk of CKD for every 10 µg/m~3 increase in PM_(2.5) exposure(OR=3.26, 95% CI 2.72-3.90). The result in the dual-pollutant models and multi-pollutant models suggested that the association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions remained stable after controlling for contemporaneous confounding by other air pollutants. The result of subgroup analysis revealed that individuals aged 45 or older, without MS, with TG<1.7 mmol/L, HDL-C≥1.04 mmol/L, without hypertension, and with central obesity and high blood sugar had a stronger association between PM_(2.5) exposure and CKD-related health effects. CONCLUSION: Long-term exposure to PM_(2.5) may increase the risk of CKD in people with abnormal MS components. More attention should be paid to middle-aged and elderly people aged ≥45 years, people with central obesity and hyperglycemia.

Long-term exposure to ambient fine particulate matter (PM2.5) and attributable pulmonary tuberculosis notifications in Ningxia Hui Autonomous Region, China: a health impact assessment.

INTRODUCTION: Long-term exposure to fine particulate matter (≤2.5 µm (PM2.5)) has been associated with pulmonary tuberculosis (TB) notifications or incidence in recent publications. Studies quantifying the relative contribution of long-term PM2.5 on TB notifications have not been documented. We sought to perform a health impact assessment to estimate the PM2.5- attributable TB notifications during 2007-2017 in Ningxia Hui Autonomous Region (NHAR), China. METHODS: PM2.5 attributable TB notifications were estimated at township level (n=358), stratified by age group and summed across NHAR. PM2.5-associated TB-notifications were estimated for total and anthropogenic PM2.5 mass and expressed as population attributable fractions (PAFs). The main analysis used effect and uncertainty estimates from our previous study in NHAR, defining a counterfactual of the lowest annual PM2.5 (30 µg/m3) level, above which we assumed excess TB notifications. Sensitivity analyses included counterfactuals based on the 5th (31 µg/m3) and 25th percentiles (38 µg/m3), and substituting effect estimates from a recent meta-analysis. We estimated the influence of PM2.5 concentrations, population growth and baseline TB-notification rates on PM2.5 attributable TB notifications. RESULTS: Over 2007-2017, annual PM2.5 had an estimated average PAF of 31.2% (95% CI 22.4% to 38.7%) of TB notifications while the anthropogenic PAF was 12.2% (95% CI 9.2% to 14.5%). With 31 and 38 µg/m3 as counterfactuals, the PAFs were 29.2% (95% CI 20.9% to 36.3%) and 15.4% (95% CI 10.9% to 19.6%), respectively. PAF estimates under other assumptions ranged between 6.5% (95% CI 2.9% to 9.6%) and 13.7% (95% CI 6.2% to 19.9%) for total PM2.5, and 2.6% (95% CI 1.2% to 3.8%) to 5.8% (95% CI 2.7% to 8.2%) for anthropogenic PM2.5. Relative to 2007, overall changes in PM2.5 attributable TB notifications were due to reduced TB-notification rates (-23.8%), followed by decreasing PM2.5 (-6.2%), and population growth (+4.9%). CONCLUSION: We have demonstrated how the potential impact of historical or hypothetical air pollution reduction scenarios on TB notifications can be estimated, using public domain, PM2.5 and population data. The method may be transferrable to other settings where comparable TB-notification data are available.

Exploring spatiotemporal distribution characteristics of air quality and driving factors: empirical evidence of 288 cities in China.

Excellent air quality is important for China to achieve high quality economic development. The paper analyses the spatial and temporal distribution characteristics of the air quality index (AQI) in 288 Chinese cities, and further investigates the driving factors affecting air quality using the spatial Durbin model (SDM) based on the panel data of 288 Chinese cities from 2014 to 2021. The results of the study show that: (1) China's air quality level has improved in general, but there are large differences in air quality between regions; (2) China's AQI has significant spatial positive autocorrelation, and the Moran's scatter plot shows a high-high and low-low agglomeration; (3) The driving factors of air quality have different effects, and regional heterogeneity is obvious. Some developed regions in China have already crossed the inflexion point of the environmental Kuznets curve (EKC); promoting industrial upgrading and reducing pollutant emissions can significantly improve urban PM2.5 concentrations; and the "Three-Year Strategy for Conquering the Blue Sky War" policy has lowered the AQI in North China and improved PM2.5 concentrations nationwide. Based on the above findings, the paper puts forward corresponding policy recommendations.

[Pollution attributable mortality]. / Mortalité attribuable à la pollution.

POLLUTION ATTRIBUTABLE MORTALITY. Pollution is estimated to be responsible for 9 million premature deaths per year in the world. For each cause of death with a risk increased by a pollutant, the number of deaths attributable to it is computed by comparison with the number of deaths expected under a reference pollution level, which is 10 µg/m3 for ambient particulate matter pollution. Only 8% of the deaths attributable to pollution occur in high income countries, because of the large effects of water and indoor air pollution (caused by traditional cooking methods) in low and middle-income countries. In France, by this method, one estimates that 13.200 deaths a year are attributable to ambient particulate matter pollution and 1.100 to ozone. Santé publique France, which has concluded that 48.000 deaths a year were attributable to air pollution in France, overvalues the risk by a factor of nearly 4 by overestimating the risks associated with air pollution and taking a utopian reference scenario.

MORTALITÉ ATTRIBUABLE À LA POLLUTION. On estime que la pollution est responsable de 9 millions de décès prématurés par an dans le monde. Pour chaque cause de décès dont le risque est augmenté par la pollution, un nombre de décès attribuable à la pollution est calculé par comparaison avec le nombre attendu pour un niveau de pollution de référence qui est de 10 µg/m3 pour la pollution particulaire de l'air extérieur. Seulement 8 % des décès attribuables à la pollution surviennent dans les pays à revenu élevé (effets importants des pollutions de l'eau et de l'air intérieur par des modes de cuisson traditionnels dans les pays à revenus bas ou moyens). En France, par cette méthode, on estime que 13 200 décès par an sont liés à la pollution particulaire de l'air extérieur et 1 100 à l'ozone. Santé publique France, qui conclut que 48 000 décès par an sont attribuables à la pollution de l'air en France, surévalue donc le risque d'un facteur proche de 4 en surestimant l'effet de la pollution et en prenant une pollution de référence utopique.

An exploration of meteorological effects on PM2.5 air quality in several provinces and cities in Vietnam.

Linking meteorology and air pollutants is a key challenge. The study investigated meteorological effects on PM2.5 concentration using the advanced convergent cross mapping method, utilizing hourly PM2.5 concentration and six meteorological factors across eight provinces and cities in Vietnam. Results demonstrated that temperature (ρ = 0.30) and radiation (ρ = 0.30) produced the highest effects, followed by humidity (ρ = 0.28) and wind speed (ρ = 0.24), while pressure (ρ = 0.22) and wind direction (ρ = 0.17) produced the weakest effects on PM2.5 concentration. Comparing the ρ values showed that temperature, wind speed, and wind direction had greater impacts on PM2.5 concentration during the dry season whereas radiation had a more influence during the wet season; Southern stations experienced larger meteorological effects. Temperature, humidity, pressure, and wind direction had both positive and negative influences on PM2.5 concentration, while radiation and wind speed mostly had negative influences. During PM2.5 pollution episodes, there was more contribution of meteorological effects on PM2.5 concentration indicated by ρ values. At contaminated levels, humidity (ρ = 0.45) was the most dominant factor affecting PM2.5 concentration, followed by temperature (ρ = 0.41) and radiation (ρ = 0.40). Pollution episodes were pointed out to be more prevalent under higher humidity, higher pressure, lower temperature, lower radiation, and lower wind speed. The ρ calculation also revealed that lower temperature, lower radiation, and higher humidity greatly accelerated each other under pollution episodes, further enhancing PM2.5 concentration. The findings contributed to the literature on meteorology and air pollution interaction.

Association between PM2.5 from a coal mine fire and FeNO concentration 7.5 years later.

BACKGROUND AND AIM: There are few long-term studies of respiratory health effects of landscape fires, despite increasing frequency and intensity due to climate change. We investigated the association between exposure to coal mine fire PM2.5 and fractional exhaled nitric oxide (FeNO) concentration 7.5 years later. METHODS: Adult residents of Morwell, who were exposed to the 2014 Hazelwood mine fire over 6 weeks, and unexposed residents of Sale, participated in the Hazelwood Health Study Respiratory Stream in 2021, including measurements of FeNO concentration, a marker of eosinophilic airway inflammation. Individual exposure to coal mine fire PM2.5 was modelled and mapped to time-location diaries. The effect of exposure to PM2.5 on log-transformed FeNO in exhaled breath was investigated using multivariate linear regression models in the entire sample and stratified by potentially vulnerable subgroups. RESULTS: A total of 326 adults (mean age: 57 years) had FeNO measured. The median FeNO level (interquartile range [IQR]) was 17.5 [15.0] ppb, and individual daily exposure to coal mine fire PM2.5 was 7.2 [13.8] µg/m3. We did not identify evidence of association between coal mine fire PM2.5 exposure and FeNO in the general adult sample, nor in various potentially vulnerable subgroups. The point estimates were consistently close to zero in the total sample and subgroups. CONCLUSION: Despite previous short-term impacts on FeNO and respiratory health outcomes in the medium term, we found no evidence that PM2.5 from the Hazelwood coal mine fire was associated with any long-term impact on eosinophilic airway inflammation measured by FeNO levels.

Fine particulate matter-sudden death association modified by ventricular hypertrophy and inflammation: a case-crossover study.

Background: Sudden death accounts for approximately 10% of deaths among working-age adults and is associated with poor air quality. Objectives: To identify high-risk groups and potential modifiers and mediators of risk, we explored previously established associations between fine particulate matter (PM2.5) and sudden death stratified by potential risk factors. Methods: Sudden death victims in Wake County, NC, from 1 March 2013 to 28 February 2015 were identified by screening Emergency Medical Systems reports and adjudicated (n = 399). Daily PM2.5 concentrations for Wake County from the Air Quality Data Mart were linked to event and control periods. Potential modifiers included greenspace metrics, clinical conditions, left ventricular hypertrophy (LVH), and neutrophil-to-lymphocyte ratio (NLR). Using a case-crossover design, conditional logistic regression estimated the OR (95%CI) for sudden death for a 5 µg/m3 increase in PM2.5 with a 1-day lag, adjusted for temperature and humidity, across risk factor strata. Results: Individuals having LVH or an NLR above 2.5 had PM2.5 associations of greater magnitude than those without [with LVH OR: 1.90 (1.04, 3.50); NLR > 2.5: 1.25 (0.89, 1.76)]. PM2.5 was generally less impactful for individuals living in areas with higher levels of greenspace. Conclusion: LVH and inflammation may be the final step in the causal pathway whereby poor air quality and traditional risk factors trigger arrhythmia or myocardial ischemia and sudden death. The combination of statistical evidence with clinical knowledge can inform medical providers of underlying risks for their patients generally, while our findings here may help guide interventions to mitigate the incidence of sudden death.

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.

The Joint Effects of Thunderstorms and Power Outages on Respiratory-Related Emergency Visits and Modifying and Mediating Factors of This Relationship.

BACKGROUND: While limited studies have evaluated the health impacts of thunderstorms and power outages (POs) separately, few have assessed their joint effects. We aimed to investigate the individual and joint effects of both thunderstorms and POs on respiratory diseases, to identify disparities by demographics, and to examine the modifications and mediations by meteorological factors and air pollution. METHODS: Distributed lag nonlinear models were used to examine exposures during three periods (i.e., days with both thunderstorms and POs, thunderstorms only, and POs only) in relation to emergency department visits for respiratory diseases (2005-2018) compared to controls (no thunderstorm/no PO) in New York State (NYS) while controlling for confounders. Interactions between thunderstorms and weather factors or air pollutants on health were assessed. The disparities by demographics and seasons and the mediative effects by particulate matter with aerodynamic diameter ≤2.5µm (PM2.5) and relative humidity (RH) were also evaluated. RESULTS: Thunderstorms and POs were independently associated with total and six subtypes of respiratory diseases in NYS [highest risk ratio (RR) = 1.12; 95% confidence interval (CI): 1.08, 1.17], but the impact was stronger when they co-occurred (highest RR = 1.44; 95% CI: 1.22, 1.70), especially during grass weed, ragweed, and tree pollen seasons. The stronger thunderstorm/PO joint effects were observed on chronic obstructive pulmonary diseases, bronchitis, and asthma (lasted 0-10 d) and were higher among residents who lived in rural areas, were uninsured, were of Hispanic ethnicity, were 6-17 or over 65 years old, and during spring and summer. The number of comorbidities was significantly higher by 0.299-0.782/case. Extreme cold/heat, high RH, PM2.5, and ozone concentrations significantly modified the thunderstorm-health effect on both multiplicative and additive scales. Over 35% of the thunderstorm effects were mediated by PM2.5 and RH. CONCLUSION: Thunderstorms accompanied by POs showed the strongest respiratory effects. There were large disparities in thunderstorm-health associations by demographics. Meteorological factors and air pollution levels modified and mediated the thunderstorm-health effects. https://doi.org/10.1289/EHP13237.

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.

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.

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.

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.

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.

Microbial dynamics and climatic interactions in pig sheds: Insights into airborne microbes and particulate matter concentrations.

Emissions of airborne pollutants from livestock buildings affect indoor air quality, the health and well-being of farmers, animals and the environment. This study aimed to evaluate the microbial count within pig sheds and its relationship with meteorological variables (temperature, relative humidity and air velocity) and particulate matter (PM10 and PM2.5) and microbial diversity. Sampling was conducted both inside and outside of two pig sheds over three seasons (summer, rainy and winter), with regular monitoring at fortnightly intervals. Results showed that the bacterial and fungal counts ranged from 0.07 to 3.98 x 103 cfu/m3 inside the sheds and 0.01 to 1.82 x 103 cfu/m3 outside. Seasonal variations were observed, with higher concentrations of particulate matter detected during the winter season, followed by summer. Climatic variables such as temperature, air velocity and relative humidity demonstrated significant impacts on the abundance of Enterobacteriaceae and fungi, while air velocity specifically influenced the presence of mesophilic bacteria and staphylococci. Importantly, no significant disparities were found between microbial counts and particulate matter levels. Staphylococcaceae emerged as the predominant bacterial family, while Aspergillus and Cladosporium spp. were the dominant fungal species within the pig sheds. The average levels of airborne bacteria and fungi in pig sheds were found to be within the recommended range, which can be attributed to the loose housing design and lower animal population on the farms.

The longitudinal associations between ambient air pollution exposure and dementia in the UK: results from the cognitive function and ageing study II and Wales.

BACKGROUND: Air pollution has been recognised as a potential risk factor for dementia. Yet recent epidemiological research shows mixed evidence. The aim of this study is to investigate the longitudinal associations between ambient air pollution exposure and dementia in older people across five urban and rural areas in the UK. METHODS: This study was based on two population-based cohort studies of 11329 people aged ≥ 65 in the Cognitive Function and Ageing Study II (2008-2011) and Wales (2011-2013). An algorithmic diagnosis method was used to identify dementia cases. Annual concentrations of four air pollutants (NO2, O3, PM10, PM2.5) were modelled for the year 2012 and linked via the participants' postcodes. Multistate modelling was used to examine the effects of exposure to air pollutants on incident dementia incorporating death and adjusting for sociodemographic factors and area deprivation. A random-effect meta-analysis was carried out to summarise results from the current and nine existing cohort studies. RESULTS: Higher exposure levels of NO2 (HR: 1.04; 95% CI: 0.94, 1.14), O3 (HR: 0.90; 95% CI: 0.70, 1.15), PM10 (HR: 1.17; 95% CI: 0.86, 1.58), PM2.5 (HR: 1.41; 95% CI: 0.71, 2.79) were not strongly associated with dementia in the two UK-based cohorts. Inconsistent directions and strengths of the associations were observed across the two cohorts, five areas, and nine existing studies. CONCLUSIONS: In contrast to the literature, this study did not find clear associations between air pollution and dementia. Future research needs to investigate how methodological and contextual factors can affect evidence in this field and clarity the influence of air pollution exposure on cognitive health over the lifecourse.

Association between exposure to ambient air pollution, meteorological factors and atopic dermatitis consultations in Singapore-a stratified nationwide time-series analysis.

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 20% of children globally. While studies have been conducted elsewhere, air pollution and weather variability is not well studied in the tropics. This time-series study examines the association between air pollution and meteorological factors with the incidence of outpatient visits for AD obtained from the National Skin Centre (NSC) in Singapore. The total number of 1,440,844 consultation visits from the NSC from 2009 to 2019 was analysed. Using the distributed lag non-linear model and assuming a negative binomial distribution, the short-term temporal association between outpatient visits for AD and air quality and meteorological variability on a weekly time-scale were examined, while adjusting for long-term trends, seasonality and autocorrelation. The analysis was also stratified by gender and age to assess potential effect modification. The risk of AD consultation visits was 14% lower (RR10th percentile: 0.86, 95% CI 0.78-0.96) at the 10th percentile (11.9 µg/m3) of PM2.5 and 10% higher (RR90th percentile: 1.10, 95% CI 1.01-1.19) at the 90th percentile (24.4 µg/m3) compared to the median value (16.1 µg/m3). Similar results were observed for PM10 with lower risk at the 10th percentile and higher risk at the 90th percentile (RR10th percentile: 0.86, 95% CI 0.78-0.95, RR90th percentile: 1.10, 95% CI 1.01-1.19). For rainfall for values above the median, the risk of consultation visits was higher up to 7.4 mm in the PM2.5 model (RR74th percentile: 1.07, 95% CI 1.00-1.14) and up to 9 mm in the PM10 model (RR80th percentile: 1.12, 95% CI 1.00-1.25). This study found a close association between outpatient visits for AD with ambient particulate matter concentrations and rainfall. Seasonal variations in particulate matter and rainfall may be used to alert healthcare providers on the anticipated rise in AD cases and to time preventive measures to reduce the associated health burden.