Effects Of Air Pollution Research Articles

Breathless Returns: Assessing Air Pollution's Influence on Dividend Yields in Pakistan and the USA

Enterprises and companies are also impacted by air pollution in addition to people and the environment. Air pollution can result in decreased output and sharp rises in healthcare costs. Publicly traded firms' profitability and stock prices are significantly impacted by air pollution. Dividend yield, which has significant importance to investors, is a critical component of stock market growth. The relationship between air pollution and dividend yield can give investors relevant information on the financial success of businesses. Pakistan has been chosen as a case study for research on how air pollution affects dividend yield. In order to better understand the possible effects of air pollution on financial performance in emerging nations, it is fascinating to look at Pakistan, which has a booming economy and a rising stock market. Pakistan stock exchange (PSX) and S&P 500 stock exchanges' dividend yields are compared to see how air pollution affects them. In their respective regions, they are two of the most well-known and recognized stock market indices. This study employed regression analysis to look at the relationship between dividend yield and air pollution. Finding relationships between the variables and examining how air pollution impacts dividend yield were the two goals of the investigation. The results of the investigation depict that dividend yield is significantly impacted by air pollution.

Mitigating child exposure to traffic-related air pollution on the school commute: Views of parents and teachers in England

Children are particularly vulnerable to the adverse health effects of traffic-related air pollution (TRAP) due to their developing physiologies. A survey was distributed to schools in England for completion by parents and teachers. The survey examined perceptions of TRAP on the school commute and assessed views on potential interventions for reducing exposure. 76.7% of parents and 75.8% of teachers were concerned about TRAP at their school. The most common cause for concern was school proximity to a busy road (44.2% parents and 42.5% teachers).Active travel was the most common measure already undertaken. The biggest obstacles to reducing school TRAP were driving convenience, school proximity to busy roads, and lack of parental support. Parents and teachers considered local authorities most responsible for improving school TRAP. Active travel was a popular and desirable intervention for reducing potential child exposure, and parental education on this and related topics were also desirable.

Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study

BackgroundEpidemiological evidence regarding the association between air pollution and resting heart rate (RHR), a predictor of cardiovascular disease and mortality, is limited and inconsistent. ObjectivesWe used wearable devices and time-series analysis to assess the exposure-response relationship over an extended lag period. MethodsNinety-seven elderly individuals (>65 years) from the Taipei Basin participated from May to November 2020 and wore Garmin® smartwatches continuously until the end of 2021 for heart rate monitoring. RHR was defined as the daily average of the lowest 30-min heart rate. Air pollution exposure data, covering lag periods from 0 to 60 days, were obtained from nearby monitoring stations. We used distributed lag non-linear models and linear mixed-effect models to assess cumulative effects of air pollution. Principal component analysis was utilized to explore underlying patterns in air pollution exposure, and subgroup analyses with interaction terms were conducted to explore the modification effects of individual factors. ResultsAfter adjusting for co-pollutants in the models, an interquartile range increase of 0.18 ppm in carbon monoxide (CO) was consistently associated with increased RHR across lag periods of 0–1 day (0.31, 95 % confidence interval [CI]: 0.24–0.38), 0–7 days (0.68, 95 % CI: 0.57–0.79), and 0–50 days (1.02, 95 % CI: 0.82–1.21). Principal component analysis identified two factors, one primarily influenced by CO and nitrogen dioxide (NO2), indicative of traffic sources. Increases in the varimax-rotated traffic-related score were correlated with higher RHR over 0–1 day (0.36, 95 % CI: 0.25–0.47), 0–7 days (0.62, 95 % CI: 0.46–0.77), and 0–50 days (1.27, 95 % CI: 0.87–1.67) lag periods. Over a 0–7 day lag, RHR responses to traffic pollution were intensified by higher temperatures (β = 0.80 vs. 0.29; interaction p-value [P_int] = 0.011). Males (β = 0.66 vs. 0.60; P_int < 0.0001), hypertensive individuals (β = 0.85 vs. 0.45; P_int = 0.028), diabetics (β = 0.96 vs. 0.52; P_int = 0.042), and those with lower physical activity (β = 0.70 vs. 0.54; P_int < 0.0001) also exhibited stronger responses. Over a 0–50 day lag, males (β = 0.99 vs. 0.96; P_int < 0.0001), diabetics (β = 1.66 vs. 0.69; P_int < 0.0001), individuals with lower physical activity (β = 1.49 vs. 0.47; P_int = 0.0006), and those with fewer steps on lag day 1 (β = 1.17 vs. 0.71; P_int = 0.029) showed amplified responses. ConclusionsProlonged exposure to traffic-related air pollution results in cumulative cardiovascular risks, persisting for up to 50 days. These effects are more pronounced on warmer days and in individuals with chronic conditions or inactive lifestyles.

Air pollution and the mystery of high household savings in China

Despite consumption being a key measure of residential welfare, the relationship of the residential savings rates, an inverse proxy for household consumption, with air pollution is unclear. This study empirically examines the impact of air pollution on household savings rates, and the underlying mechanism. The results reveal that worsening air pollution significantly increases household savings rates; this finding holds under alternative variable specifications and employing instrumental variables to address endogeneity issues. The mechanism analysis demonstrates that air pollution enhances household savings rates through health risks. Heterogeneity analysis indicates that in developed areas with more comprehensive public medical service systems, the positive effect of air pollution on household savings rates weakens in second-tier cities and above as well as in high-income areas. Furthermore, improvements in commercial insurance coverage mitigates the positive effect of air pollution on household savings rates. Overall, this study explains the high savings phenomenon among Chinese households from an environmental perspective. The Chinese government should encourage the use of renewable energy, improve air pollution control, and reduce the inhibitory effects of air pollution on residential consumption.

Exposure to ambient particulate matter and ovarian reserve impairment among reproductive age women in China

Ovarian aging, characterized by a decline in ovarian reserve, is a critical concern in female reproductive health. However, the evidence linking ambient air pollution exposure with ovarian reserve impairment remains limited. We aimed to estimate the association between exposure to fine particulate matter (PM2.5) and respirable particulate matter (PM10) and key indicators of ovarian reserve, including antral follicle count (AFC), ovarian volume (OV), anti-Müllerian hormone (AMH), follicle-stimulating hormone (FSH), estradiol (E2), luteinizing hormone (LH), FSH/LH ratio, and inhibin B (INHB). The cohort consisted of women attending an infertility clinic at the Tongji Reproductive and Environmental (TREE) study between 2018 and 2020. We used multivariate linear and Poisson regression models to estimate the association between PM2.5 and PM10 exposure and these ovarian reserve indicators. Our results showed that PM2.5 and PM10 exposure were associated with a reduction in AFC and an increase in E2 levels, highlighting the adverse effects of ambient air pollution on ovarian reserve. Our findings have important public health implications, emphasizing the urgent need for interventions to safeguard female reproductive health and reduce exposure to ambient air pollution.

Effects of outdoor air pollution on hospital admissions from cardiovascular diseases in the capital city of Mongolia

Cardiovascular diseases (CVD) are one of the primary causes of morbidity worldwide, while ambient air pollution (AAP) is a major contributor to CVD. This study is the first study that aimed to compare the differences in AAP concentrations and acute effects on the risk of admissions from CVD, ischaemic heart diseases (IHD), and stroke before and after introducing the new type of coal briquette in Ulaanbaatar, Mongolia. We collected daily hospital admission records, air quality and meteorology data between January 1, 2016 and December 31, 2022. The relative risks (RRs) of cause-specific hospitalisations associated with daily concentrations of AAP were estimated by a time-stratified case-crossover analysis with conditional Poisson distributed lag models. Overall, acute exposure to the pollutants, except for O3, was positively associated with an increased risk of all admissions. For each interquartile range (IQR) increase in PM2.5, PM10, SO2, NO2 and CO, the risk of CVD admissions increased by 0.5% (RR=1.005; 95% CI: 0.998, 1.012) at lag0, 2.9% (RR=1.029; 95% CI: 1.014, 1.044), 2.5% (RR=1.025; 95% CI: 1.010, 1.040), 4.1% (RR=1.041; 95% CI: 1.016, 1.068) and 0.3% (RR=1.003; 95% CI: 0.995, 1.012) at lag01, respectively. Subgroup analyses showed greater risks of CVD admissions for women and people aged <65 years, in the cold season, and after the raw coal ban period. During the cold winter season, Ulaanbaatar’s AAP levels remained higher than the World Health Organization’s guidelines, regardless of consuming the new coal briquettes. Adopting alternative efficient methods to improve air quality and associated health outcomes is necessary.

Unbiased temperature-related mortality estimates using weekly and monthly health data: a new method for environmental epidemiology and climate impact studies

Exposure to environmental factors has a high burden on human health, with millions of premature annual deaths associated with the short-term health effects of ambient temperatures and air pollution. However, direct estimations of exposure-related mortality from real data are still not available in most parts of the world, especially in low-resource settings, due to the unavailability of daily health records to calibrate epidemiological models. In this study, we have filled the crucial gap in available direct estimations by developing a method to make valid inference for the relationship between exposure and response data that uses only exposure and temporally aggregated response data. We provided the mathematical derivation of the method, and compared the results by using simulations applied to daily temperature and daily, weekly, and monthly mortality data. The method was then applied to the newly created database of the EARLY-ADAPT project. The daily and weekly models produced similar and unbiased estimates of the temperature-related relative risks and attributable mortality, with only slightly more imprecision in the weekly model. Even the estimates of the monthly model were unbiased when using enough data, although at the expense of a substantial increase in variability. The real data analysis showed that the similarity between the regional values of two aggregation models increased with the number of years and regions of the dataset, and decreased with the difference in their degree of temporal aggregation. Our method opens the door to conducting epidemiological studies in low-resource settings, where access to daily health data is not possible. Moreover, it allows accurate estimation of the short-term health effects of environmental exposures in near-real time, when daily health data are still not available, such as in the estimation of the mortality burden of recent record-breaking heat episodes. Overall, our method represents an important new approach to how the public health community can use data to create new evidence for research, translation and policy making. European Research Council (ERC).

Associations of ambient particulate matter and household fuel use with chronic liver disease in China: A nationwide analysis

BackgroundLong-term effects of outdoor and indoor air pollution on chronic liver disease (CLD) remain unclear. Thus, the study was conducted to investigate the relationship between prolonged exposure to ambient particulate matter (PM1, PM2.5 and PM10) and household fuel usage with CLD. MethodsData from the China Health and Retirement Longitudinal Study (CHARLS) covering the years 2011 to 2020 were employed. In the cross-sectional analysis, 16,680 participants were included, while 12,969 participants were enrolled in the longitudinal study. The associations between various sizes of particulate matter and CLD were elucidated using logistic regression model and generalized linear-mixed models. Additionally, the additive effects of ambient particulate matter (PM) levels and the utilization of solid fuels for cooking were investigated, with a comparison of effect sizes between converted and non-converted fuel types. ResultsOver a 10-year follow-up period, 746 (5.75 %) individuals developed CLD. For a 1-year average concentrations, PM1, PM2.5 and PM10 were each linked to a 1.549 (95 %CI:1.522–1.576), 1.296 (95 %CI:1.276–1.317) and 1.134 (95 %CI:1.118–1.150) fold risk of incident CLD per 10 μg/m3 increase, respectively. A similar effect of PM concentrations over a 2-year period on CLD was observed. Moreover, simultaneous exposure to ambient PM and solid fuels is associated with an increased risk of CLD. Those who continue using solid fuels may face a higher risk of CLD compared to individuals who switch to cleaner cooking fuels. Female participants, smokers, and individuals with shorter sleep duration and multiple chronic diseases exhibited slightly stronger effects. ConclusionLong-term exposure to varying sizes of PM (PM1, PM2.5, PM10) has been linked to an elevated risk of CLD incidence. Co-exposure to ambient PM and solid fuels is associated with higher health risks.

Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model

Accurate prediction of PM2.5 concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of port staff. However, in port clusters formed by multiple neighboring ports, we encountered several challenges owing to the impact of unique meteorological conditions, potential correlation between PM2.5 levels in neighboring ports, and coupling influence of background pollutants in city zones. Therefore, considering the spatiotemporal correlation among the factors influencing PM2.5 concentration variations within the harbor cluster, we developed a novel blending ensemble deep learning model. The proposed model combined the strengths of four deep learning architectures: graph convolutional networks (GCN), long short-term memory networks (LSTM), residual neural networks (ResNet), and convolutional neural networks (CNN). GCN, LSTM, and ResNet served as the base models aimed at capturing the spatial correlation of PM2.5 concentrations in neighboring ports, the potential long-term dependence of meteorological factors and PM2.5 concentrations, and the effects of urban ambient air pollutants, respectively. Following the blending ensemble technique, the prediction outcomes of three base models were used as the input data for the meta-model CNN, which employs the blending ensemble technique to produce the final prediction results. Based on actual data obtained from 18 ports in Nanjing, the proposed model was compared and analyzed for its prediction performance against six state-of-the-art models. The findings revealed that the proposed model provided more accurate predictions. It reduced mean absolute error (MAE) by 10.59 %–20.00 %, reduced root mean square error (RMSE) by 13.22 %–17.11 %, improved coefficient of determination (R2) by 10 %–35.38 %, and improved accuracy (ACC) by 3.48 %–7.08 %. Additionally, the contribution of each component to the prediction performance of the proposed model was measured using a systematic ablation study. The results demonstrated that the GCN model exerted the most substantial influence on the prediction performance of the GCN-LSTM-ResNet model, followed by the LSTM model. The influence of urban background pollutants can significantly enhance the generalizability of the complete model. Moreover, a comparison with three blended ensemble models incorporating any two base models demonstrated that the GCN-LSTM-ResNet model exhibited superior prediction performance and was particularly excellent in predicting the occurrence of high-concentration events. Specifically, the GCN-LSTM-ResNet model improved MAE and RMSE by at least 12.3% and 9.2%, respectively, but reduced R2 and ACC by 26.1% and 6.8%, respectively. The proposed model provided reliable PM2.5 concentration prediction outcomes and decision support for air quality management strategies in dry bulk port clusters.

Consumer-driven selection of low-emission vehicles for sustainable urban centers: An AHP-based approach

In densely populated urban areas, air quality is one of the main concerns, affecting human health and the environment. In developing and emerging countries, an alternative method for reducing the effects of air pollution is to select vehicles with lower pollutant emissions, as a way of making these large centers more sustainable. Since the complete elimination of vehicle emissions is not possible in the short term. The aim of this work is conducting a selectivity study of low-emission vehicles to increase the proportion of less polluting vehicles by using a dynamic combination of technical information while simultaneously meeting consumer preferences. An adapted Analytic Hierarchy Process (AHP), a multi-criteria technique, was applied on secondary data on vehicle characteristics. The Brazilian Labeling Program was the main resource used in this research. The research innovatively promotes vehicle selection considering environmental aspects. In addition to being adaptable, dynamic, and interactive, it facilitates its use in different vehicle markets and contributes to policies for reducing pollutant emissions. Thus, the study presents a strategy to minimize vehicle emissions in urban centers until the energy transition is consolidated, especially in emerging countries like Brazil.

Risk assessment of PM2.5 from fossil energy consumption on the respiratory health of the elderly

Air pollution mainly comes from fossil energy consumption (FEC), and it brings great threat to public health. The respiratory system of the elderly is highly susceptible to the effects of air pollution due to the decline in body functions. PM2.5 is a major component of air pollution, so the study of the impact of PM2.5 generated by FEC on the respiratory health of the elderly is of great significance. The existing studies have focused more on the effect of PM2.5 on mortality, and this paper is a useful addition to the existing studies by examining the effect of PM2.5 from FEC on the health of the elderly from the perspective of prevalence. In this paper, the binary Logistic regression model was used to calculate the exposure-response relationship coefficient for respiratory health in older adults using the data in 2018 from the Chinese Longitudinal Healthy Longevity Survey. And referring to the Dynamic Projection model for Emissions in China, the changes in the number of older persons suffering from respiratory diseases due to PM2.5 from FEC in the baseline scenario, the clean air scenario, and the on-time peak-clean air scenario were predicted. The results indicated that: (1) PM2.5 from FEC mainly came from coal; (2) PM2.5 from FEC was detrimental to the respiratory health of the elderly, and older seniors were more affected as they age; (3) In the on-time peak-clean air scenario, the number of elderly people suffering from respiratory diseases due to PM2.5 from FEC was growing at the slowest rate. Based on the above results, this paper raised recommendations for reducing the effect of PM2.5 from FEC on the health of the elderly.

A prospective study on the cardiorespiratory effects of air pollution among residents of the Tibetan Plateau

The Tibetan Plateau is characterized by high ozone concentration which poses a significant public health concern. However, the causal evidence linking ozone pollution to adverse cardiopulmonary health impacts, as well as the understanding of its underlying biological mechanisms, remains limited. Additionally, exposure levels to particulate and other gaseous air pollutants along with their associated health effects in this region are largely unknown. To address these gaps, we conducted a prospective follow-up study in Tibet from May 2021 to November 2021. In consideration of the potential synergistic effects of chronic hypobaric hypoxia, two Tibetan cities with different altitudes, Lhasa (3650 m) and Nyingchi (3000 m), were chosen to implement atmospheric monitoring and health measurement. We employed cutting-edge, high-precision instruments at stationary monitoring sites to measure ambient air pollution and collected particle samples. Portable devices were used to monitor personal exposure levels of ozone and black carbon. A total of 212 healthy Tibetan college students participated in up to four clinical visits, yielding 774 visits in total, during which functional endpoints were measured and biological samples were collected. The primary aim of this study is to evaluate the cardiorespiratory effects of ambient ozone under hypoxic conditions, where its impact may be amplified due to the region's unique environmental characteristics. The secondary aim is to provide a comprehensive assessment of other air pollutants, including their exposure levels, sources, and health effects. By addressing these aims, the study offers valuable insights into air quality and its health implications in this unique high-altitude setting. This paper outlines the research motivation, measurement framework, and preliminary findings.

Air pollution exposure during pregnancy and preterm birth in Brazil

Ambient air pollution is a significant environmental risk factor for adverse pregnancy outcomes, including preterm birth. However, the impact of different pollutants across various regions and trimesters of pregnancy has not been fully investigated in Brazil. This study aimed to examine the associations between exposure to PM2.5, NO2, and O3 during different trimesters of pregnancy and the risk of preterm birth across five regions of Brazil. We used logistic regression models to estimate the odds ratios (OR) of preterm birth associated with PM2.5, NO2, and O3 adjusting for potential confounders such as maternal age, education, and socioeconomic status. Our study included over 9.9 million live births from 2001 to 2018, with data obtained from the Ministry of Health in Brazil. On average, for each 1-μg/m3 increase in PM2.5, we estimated a 0.26 % (95 % CI: 0.08–0.44 %) increase in the risk of preterm birth nationally in the first trimester. For NO2, each 1ppb increase was associated with a percentage increase in preterm birth risk of 7.26 % (95 % CI: 4.77–9.74 %) in the first trimester, 8.05 % (95 % CI: 5.73–10.38 %) in the second trimester, and 7.48 % (95 % CI: 5.25–9.72 %) in the third trimester. For O3, each 1ppb increase was associated with a percentage increase in preterm birth risk of 1.24 % (95 % CI: 0.29–2.18 %) in the first trimester, 1.51 % (95 % CI: 0.60–2.41 %) in the second trimester, and 0.72 % (95 % CI: −0.18–1.62 %) in the third trimester. This study highlights the significant impact of ambient air pollution on preterm birth risk in Brazil, with significant regional variations. Our findings underscore the need for targeted public health interventions to mitigate the effects of air pollution on pregnancy outcomes, particularly in the most affected regions.

A genome-wide gene-environmental interaction study identified novel loci for the relationship between ambient air pollution exposure and depression, anxiety

BackgroundGenetic factors and environmental exposures, including air pollution, contribute to the risk of depression and anxiety. While the association between air pollution and depression and anxiety has been established in the UK Biobank, there has been limited research exploring this relationship from a genetic perspective. MethodsBased on individual genotypic and phenotypic data from a cohort of 104,385 participants in the UK Biobank, a polygenic risk score for depression and anxiety was constructed to explore the joint effects of nitric oxide (NO), nitrogen dioxide (NO2), particulate matter (PM) with a diameter of ⩽2.5 μm (PM2.5) and 2.5–10 μm (PMcoarse) with depression and anxiety by linear and logistic regression models. Subsequently, a genome-wide gene-environmental interaction study (GWEIS) was performed using PLINK 2.0 to identify the genes interacting with air pollution for depression and anxiety. ResultsA substantial risk of depression and anxiety development was detected in participants exposed to the high air pollution concomitantly with high genetic risk. GWEIS identified 166, 23, 18, and 164 significant candidate loci interacting with NO, NO2, PM2.5, and PMcoarse for Patient Health Questionnaire-9 (PHQ-9) score, and detected 44, 10, 10, and 114 candidate loci associated with NO, NO2, PM2.5, and PMcoarse for General Anxiety Disorder (GAD-7) score, respectively. And some significant genes overlapped among four air pollutants, like TSN (rs184699498, PNO2 = 3.47 × 10−9; rs139212326, PPM2.5 = 1.51 × 10−8) and HSP90AB7P(rs150987455, PNO2 = 1.63 × 10−11; rs150987455, PPM2.5 = 7.64 × 10−11), which were common genes affecting PHQ-9 score for both NO2 and PM2.5. ConclusionOur study identified the joint effects of air pollution with genetic susceptibility on the risk of depression and anxiety, and provided several novel candidate genes for the interaction, contributing to an understanding of the genetic architecture of depression and anxiety.

Effect of particulate matter on cognitive function among middle-aged and older adults in China: An instrumental variable approach

Emerging studies have suggested the association of ambient air pollution with cognitive decline; however, causal evidence remains scarce. We aimed to estimate the effect of particulate matter (PM) exposure on overall and domain-specific cognitive function. A total of 14,205 participants aged 45 and above were drawn from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). Cognitive function was assessed using a standardized questionnaire consisting of two domains: episodic memory and mental status. Participants’ exposure to city-level PM (PM1, PM2.5, and PM10) was evaluated using satellite-based spatiotemporal models. To avoid potential endogenous biases, an instrumental variable method with two-stage least squares estimation was employed to examine the effect of air pollution on overall and domain-specific cognitive function. Stratified analysis was further performed based on sociodemographic characteristics. We found PM exposure exerted a significant adverse effect on overall and domain-specific cognitive function, with mental status being more adversely affected as compared to episodic memory. The effect of PM exposure was more pronounced with smaller particle sizes, with PM1 exhibiting the largest effect size. Moreover, participants with lower education were more adversely affected by PM exposure. Our findings add additional evidence of the potential causal role of PM exposure in worsening overall and domain-specific cognitive function. Efforts to further reduce PM are needed to protect the cognitive health of the aging Chinese population.

Modelling of Glass Soiling Due to Air Pollution Exposure at Urban and National Scales: Coimbra (Portugal) Case Study

Impacts of air pollution are not limited to human health and ecosystems, but are also important for building materials. The main objective of this study is the quantification and mapping of air pollution effects on the materials, namely the soiling effect of modern glass. An integrated modelling approach was implemented to quantify and analyze the spatial distribution of glass soiling due to exposure to air pollution. The methodology is based on an integrated modelling approach (transportation-emissions-dispersion modelling) applied with high spatial resolution for Coimbra (Portugal) urban area and compared with national scale modelling, showing the important contribution of local pollution sources affecting spatial variability in the soiling effect. Air quality data from CAMS (Copernicus Atmosphere Monitoring Service) were used to quantify the soiling effect at national scale. The results are presented and analyzed in terms of haze. The results obtained at national scale suggest that the average time to reach a 1% haze is 320 days, and this time is reduced to 180 days in the most affected areas. However, urban scale modelling applied with a detailed characterization of local pollution sources in Coimbra provides significantly different results and reveals that in the most affected areas, near road traffic, 1% haze could be reached in approximately 80 days. The methodology proposed and implemented in this study provides relevant information for the maintenance and preservation of building materials and highlights the importance of integrated modelling with high spatial resolution for the assessment of the soiling effect in the built environment.

Individual and joint exposure to air pollutants and patterns of multiple chronic conditions.

Existing research on the detrimental effects of air pollution and its mixture on multiple chronic conditions (MCC) is not yet fully recognized. Our objective was to examine if individual and joint exposure to air pollution is associated with the incidence and patterns of MCC. Totally 10,231 CHARLS 2015 participants aged over 45 years and 1,938 without MCC were followed up in 2018 and 2020. Residential-levelcumulative personal exposure concentrations of PM1, PM10, PM2.5, CO, O3, NO2, SO2, NO3-, Cl-, NH4+, and SO42- at the residential level were determined utilizing a spatio-temporal random forest model with a spatial resolution of 0.1° × 0.1°. In the cross-sectional and longitudinal research, logistic regression, cox regression analysis, and quantile g-computation were utilized to estimate the single and joint effect with MCC and its patterns, respectively. Interaction analyses and stratified analyses were also performed. A correlation was observed between the prevalence of cardiovascular illnesses and the presence of all 11 major air pollutants. PM2.5, PM10, NH4+, NO3-, CO, and SO42- are associated with an increased frequency of respiratory disorders. An increase of PM2.5, PM1, PM10, NO2, and SO2 (a 10µg/m3 rise), CO (a 0.1mg/m3 rise), and PMCs (Cl-, NH4+, NO3-, and SO42-) (a 1µg/m3 rise) corresponded to the HRs (95% CI) for developing MCC of 1.194 (95% CI: 1.043, 1.367), 1.362 (95% CI: 1.073, 1.728), 1.115 (95% CI: 1.026, 1.212), 1.443 (95% CI: 1.151, 1.808), 3.175 (95% CI: 2.291, 4.401), 1.272 (95% CI: 1.149,1.410), 1.382 (95% CI: 1.011, 1.888), 1.107 (95% CI: 1.003, 1.222), 1.035 (95% CI: 0.984, 1.088), and 1.122 (95% CI: 1.086, 1.160), respectively. SO2 was the predominant contributor to the combined effect (HR: 2.083, 95% CI: 1.659-2.508). Gender, age, drinking, and health status could modify the effects of air pollutants on MCC patterns. Long-term exposure to air pollution is correlated to the incidence and patterns of MCC in middle-aged and elderly Chinese individuals. Preventive methods are essential to safeguarding those susceptible to MCC.

Association of residential air pollution and green space with all-cause and cause-specific mortality in individuals with diabetes: an 11-year prospective cohort study

To assess the long-term impact of residential air pollution and green space exposure on cause-specific mortality in individuals with type 2 diabetes mellitus (T2DM). This study includes 174,063 participants newly diagnosed with T2DM from a prospective cohort in Shanghai, China, enrolled between 2011 and 2013. Residential annual levels of air pollutants, including fine (PM2.5) and coarse (PM2.5-10) particulate matter, nitrogen dioxide (NO2), along with the normalized difference vegetation index (NDVI), were derived from satellite-based exposure models. During a median follow-up of 7.9 years (equivalent to 1,333,343 person-years), this study recorded 22,205 deaths. Higher exposure to PM2.5 was significantly associated with increased risks for all mortality outcomes, whilst PM2.5-10 showed no significant impacts. The strongest associations of PM2.5 were observed for diabetes with peripheral vascular diseases [hazard ratio (HR): 2.70; per 10μg/m3 increase] and gastrointestinal cancer (2.44). Effects of NO2 became significant at concentrations exceeding approximately 45μg/m³, with the highest associations for lung cancer (1.20) and gastrointestinal cancer (1.19). Conversely, each interquartile range increase in NDVI (0.10) was linked to reduced mortality risks across different causes, with HRs ranging from 0.76 to 1.00. The association between greenness and mortality was partly and significantly mediated by reduced PM2.5 (23.80%) and NO2 (26.60%). There was a significant and negative interaction between NO2 and greenness, but no interaction was found between PM2.5 and greenness. Our findings highlight the vulnerability of individuals with T2DM to the adverse health effects of air pollution and emphasise the potential protective effects of greenness infrastructure. The 6th Three-year Action Program of Shanghai Municipality for Strengthening the Construction of Public Health System (GWVI-11.1-22), the National Key Research and Development Program (2022YFC3702701), and the National Natural Science Foundation of China (82030103, 82373532).

Adverse health effects of climate change and air pollution in people with disabilities: a systematic review.

Global warming and air pollution adversely affect the health of the entire human population, particularly older adults, people with disabilities (PWDs), and children. In this systematic review, we investigated the adverse health effects of climate change and air pollution in PWDs. We conducted a comprehensive literature search of the PubMed database using the terms "disab*," "air pollution," and "climate change" on July 4, 2023, and August 8, 2023 and searched the Web of Science (WOS) database on December 28, 2023. We identified 425 and 1169 studies on climate change cited in PubMed and WOS, respectively, as well as 333 studies on air pollution in PubMed and 495 studies on air pollution in WOS. The studies were classified by type of exposure, and full-text screening was conducted to confirm that the population, intervention or exposure, comparator, outcome statement, and inclusion and exclusion criteria were met. The Newcastle-Ottawa Scale was used to assess the quality of the included cohort and case-control studies and for data analysis. In extreme temperatures, PWDs experienced higher rates of injury, heat-related illness, functional impairment, heart disease, mental disorders, and mortality than people who were nondisabled (ND). Exposure to air pollution resulted in higher rates of obesity, cardiovascular disease, poststroke neurological and functional disability, and mortality in PWDs than in people who were ND. Therefore, because PWDs were more affected by climate change and air pollution than people who were ND, sensitive policies and preparedness measures should be developed for PWDs.

Acute effects of ambient nitrogen dioxide pollution on outpatient visits for neurological diseases in Xinxiang, China

BackgroundAccumulating evidence suggests that exposure to air pollution acts as a potential trigger for neurological diseases (NDs), yet the current knowledge regarding the impact of ambient nitrogen dioxide (NO2) on the patients with NDs remains limited. In this study, we conducted a time-series study to evaluate the association between short-term exposure to NO2 and hospital visits for NDs in Xinxiang, China.MethodsAn over-dispersed Poisson generalized additive model was used to analyze the association between ambient NO2 concentrations and daily outpatient visits for NDs from January 1, 2015 to December 31, 2017. The model adjusted for meteorological factors, temporal trends, day of the week, and public holidays. The concentrations of air pollutants were collected from four air quality stations in Xinxiang.ResultsA total of 38, 865 outpatient visits for NDs were retrieved during the study period. 86.5% of the patients were below the age of 65 years. It was revealed that a 10 µg/m3 increase in NO2 at lag 0 was associated with a significant rise of 1.50% (95% CI: 0.45–2.56%) in outpatient visits for NDs, which was stronger during the cold season. However, the overall results from stratified analyses did not reach statistical significance.ConclusionsShort-term exposure to NO2 is associated with increased outpatient visits for NDs. These findings underscore the need for implementing mitigating measures to reduce the neurological health effects of air pollutants.