Risk of concentrations of major air pollutants on the prevalence of cardiovascular and respiratory diseases in urbanized area of Kuala Lumpur, Malaysia

Determinants of Knowledge, Attitudes, Perceptions and Behaviors Regarding Air Pollution in Schoolchildren in Pristina, Kosovo

Air pollution poses a significant public health challenge, and Kosovo, a low-middle-income country in the Balkan peninsula, suffers from particularly poor air quality, especially around the area of the capital Pristina. The availability of accurate and timely information is crucial in mitigating the adverse effects of air pollution. This study aimed at evaluating the knowledge, attitudes, behaviors, and perceptions (KAPB) related to poor air quality in Pristina's low-middle schools. Furthermore, the study explored the connections between these factors and socio-demographic and health attributes and provided valuable inputs for the development of future strategies and policies in air pollution mitigation. Regression analysis provided insights into how these various factors interacted with KAPB scores. The results revealed limited knowledge about air pollution sources and risks among pupils, with insufficient awareness of reliable information sources. While attitudes were generally positive, they declined with higher grade levels. Parental education significantly influenced knowledge and attitudes, and better health correlated with more positive attitudes. Perceptions of air pollution risks were influenced by grade, gender, and parental education, with better-educated parents associated with improved perceptions. Overall behavior scores increased with higher levels of parental education. Understanding the factors that shape pupils' responses to air pollution is critical for strategy and policy development. These findings can guide strategies to enhance environmental awareness and promote healthy behavior, helping address the pressing issue of air pollution in the country.

Association between air pollutants and neural tube defects during pregnancy in Lanzhou, China: a time series analysis

Few studies have evaluated the association between air pollutants and neural tube defects (NTDs). Moreover, the existing research ignores the lag effect of air pollution on health and provides inconsistent epidemiological evidence. We aim to estimate the association between air pollution and NTDs during the first trimester of pregnancy and identify specific susceptible windows. Birth data was collected from the Birth Defects Surveillance Network in Lanzhou from September 1, 2014, to December 31, 2019. Air quality and meteorological data were collected from ambient air monitoring stations and China Meteorological Data Network. The log connection function of the Poisson distribution function is used to establish a DLNM model to estimate the exposure-effect relationship and exposure-lag relationship association between air pollutants levels and NTDs. There were 320,787 perinatal infants in Lanzhou from September 1, 2014, to December 31, 2019, and 486 cases of NTDs (1.5‰). The result indicates that exposure to inhalable particles (PM₁₀) at lag 2-4 weeks was significantly associated with the risk of NTDs, with the most significant impact at the lag 2 week (RR=1.048, 95%CI, 1.015-1.084). Exposure to fine particulate matter (PM_2.5) at the lag 2 week was significantly associated with the risk of NTDs, with the most significant impact at the lag 2 week (RR=1.077, 95%CI, 1.004-1.155). Exposure to sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) at lag 3-6weeks was significantly associated with the risk of NTDs, with the most significant impact at the lag 4 week (RR=1.220, 95%CI, 1.105-1.348; RR=1.143, 95%CI, 1.048-1.245). This study provides further evidence that exposure to air pollutants in the first trimester of pregnancy significantly increases the risk of neural tube defects.

Impacts of climate change and environmental degradation on children in Malaysia

The impacts of climate change and degradation are increasingly felt in Malaysia. While everyone is vulnerable to these impacts, the health and wellbeing of children are disproportionately affected. We carried out a study composed of two major components. The first component is an environmental epidemiology study comprised of three sub-studies: (i) a global climate model (GCM) simulating specific health-sector climate indices; (ii) a time-series study to estimate the risk of childhood respiratory disease attributable to ambient air pollution; and (iii) a case-crossover study to identify the association between haze and under-five mortality in Malaysia. The GCM found that Malaysia has been experiencing increasing rainfall intensity over the years, leading to increased incidences of other weather-related events. The time-series study revealed that air quality has worsened, while air pollution and haze have been linked to an increased risk of hospitalization for respiratory diseases among children. Although no clear association between haze and under-five mortality was found in the case-crossover study, the lag patterns suggested that health effects could be more acute if haze occurred over a longer duration and at a higher intensity. The second component consists of three community surveys on marginalized children conducted (i) among the island community of Pulau Gaya, Sabah; (ii) among the indigenous Temiar tribe in Pos Kuala Mu, Perak; and (iii) among an urban poor community (B40) in PPR Sg. Bonus, Kuala Lumpur. The community surveys are cross-sectional studies employing a socio-ecological approach using a standardized questionnaire. The community surveys revealed how children adapt to climate change and environmental degradation. An integrated model was established that consolidates our overall research processes and demonstrates the crucial interconnections between environmental challenges exacerbated by climate change. It is recommended that Malaysian schools adopt a climate-smart approach to education to instill awareness of the impending climate change and its cascading impact on children's health from early school age.

Association between ambient PM2.5 and outpatient visits of children's respiratory diseases in a megacity in Central China

Objective

To explore the relationship between ambient PM_2.5 level and outpatient visits of children with respiratory diseases in a megacity, Zhengzhou, in central China.

Methods

We collected daily outpatient visit data, air pollutant data, and meteorological data at the monitoring points of Zhengzhou from the time period 2018 to 2020 and used Spearman's rank correlation to analyze the correlation between children's respiratory outpatient visits and air pollutants and meteorological factors. Generalized additive models were used to analyze the association between PM_2.5 exposures and children's respiratory outpatient visits. A stratified analysis was further carried out for the seasons.

Results

From 2018 to 2020, the total number of outpatients with children's respiratory diseases was 79,1107, and the annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h in Zhengzhou were respectively 59.48 μg/m³, 111.12 μg/m³, 11.10 μg/m³, 47.77 μg/m³, 0.90 mg/m³ and 108.81 μg/m³. The single-pollutant model showed that the risk of outpatient visits for children with respiratory disease increased by 0.341% (95%CI: 0.274-0.407%), 0.532% (95%CI: 0.455-0.609%) and 0.233% (95%CI: 0.177-0.289%) for every 10 μg/m³ increase in PM_2.5 with a 3-day lag, 1-day lag, and 1-day lag respectively for the whole year, heating period, and non-heating period. The multi-pollutant model showed that the risk of PM_2.5 on children's respiratory disease visits was robust. The excess risk of PM_2.5 on children's respiratory disease visits increased by 0.220% (95%CI: 0.147-0.294%) when SO₂ was adjusted. However, the PM_2.5 effects were stronger during the heating period than during the non-heating period.

Conclusion

The short-term exposure to PM_2.5 was significantly associated with outpatient visits for children's respiratory diseases. It is therefore necessary to strengthen the control of air pollution so as to protect children's health.

Children's exposure to air pollution in a natural gas industrial area and their risk of hospital admission for respiratory diseases

The rapid expansion of the natural gas industry to meet the global demand have raised environmental health concerns. Few studies have found that areas with natural gas industrial activity have poor air quality. However, the negative health impacts of ambient air pollution in a natural gas industrial area remain unclear. This study aimed to explore the relationship between short-term exposure to air pollution and hospital admissions for respiratory diseases among children in a natural gas industrial area in Bintulu, Malaysia. Daily hospital admissions for respiratory diseases among children were collected from a hospital in Bintulu from 2010 to 2019. Data on six air pollutants (PM₁₀, PM_2.5, SO₂, NO₂, O₃, and CO) in the study area were obtained from the Department of Environment Malaysia. Quasi-Poisson time series regressions with distributed lag non-linear models (DLNM) were applied to explore the associations between ambient air pollution and childhood hospitalisations for respiratory diseases. Stratification analyses were performed by gender and age group to identify the vulnerable populations. A 10 μg/m³ increased PM_2.5 and SO₂ was associated with hospital admissions for respiratory diseases among children with the greatest relative risk of RR 1.089 (95% CI 1.001-1.183) at cumulative lag 0-2 days and RR 1.229 (95% CI 1.073-1.409) at cumulative lag 0-6 days, respectively. There was no significant association between short-term exposure of PM₁₀, NO₂, CO, and O₃ with childhood respiratory hospitalisation. The association between PM_2.5 and SO₂ exposure and hospital admissions for childhood respiratory diseases in the two pollutants model remained statistically significant. There were stronger associations in younger children aged 0-4 years and girls. This study reveals that short-term exposure to SO₂ was associated with a higher risk of respiratory hospitalisations among children in Bintulu than PM_2.5. Better air quality control is necessary for children's health living in the natural gas industrial area.

Association Between Sulfur Dioxide and Daily Inpatient Visits With Respiratory Diseases in Ganzhou, China: A Time Series Study Based on Hospital Data

Background

Sulfur dioxide (SO₂) has been reported to be related to the mortality of respiratory diseases, but the relationship between SO₂ and hospital inpatient visits with respiratory diseases and the potential impact of different seasons on this relationship is still unclear.

Methods

The daily average concentrations of air pollutants, including SO₂ and meteorological data in Ganzhou, China, from 2017 to 2019 were collected. The data on daily hospitalization for respiratory diseases from the biggest hospital in the city were extracted. The generalized additive models (GAM) and the distributed lag non-linear model (DLNM) were employed to evaluate the association between ambient SO₂ and daily inpatient visits for respiratory diseases. Stratified analyses by gender, age, and season were performed to find their potential effects on this association.

Results

There is a positive exposure-response relationship between SO₂ concentration and relative risk of respiratory inpatient visits. Every 10 μg/m³ increase in SO₂ was related to a 3.2% (95% CI: 0.6–6.7%) exaltation in daily respiratory inpatient visits at lag3. In addition, SO₂ had a stronger association with respiratory inpatient visits in women, older adults (≥65 years), and warmer season (May-Oct) subgroups. The relationship between SO₂ and inpatient visits for respiratory diseases was robust after adjusting for other air pollutants, including PM₁₀, NO₂, O₃, and CO.

Conclusion

This time-series study showed that there is a positive association between short-term SO₂ exposure and daily respiratory inpatient visits. These results are important for local administrators to formulate environmental public health policies.

Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence techniques

Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran

This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM₁₀, SO₂, O₃, NO₂, and CO were 98.48 μg m^-3, 8.57 ppm, 19.66 ppm, 18.14 ppm, and 4.07 ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM₁₀ (R = 0.62). The presence of SO₂ and NO₂ can be dependent on meteorological parameters (R = 0.48). Despite there was a positive correlation between age and CD (p = 0.001), the highest correlation was detected between SO₂ and CD (R = 0.4). The annual variation trend of SO₂, NO₂, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O₃ variation rate at lag = 0. On the other hand, SO₂ has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO₂ and NO₂ were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO₂ were significant contaminants in the number of hospital admissions (R = 0.46, p = 0.001) that both pollutants were a function of air temperature (p = 0.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO₂, O₃, and SO₂, respectively. Thus, the R_all rate for these structures was 0.78-0.83. In these structures, according to the autocorrelation of error in lag = 0, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5 years' time series.

The Effects of Air Quality on Hospital Admissions for Chronic Respiratory Diseases in Petaling Jaya, Malaysia, 2013–2015

This study assesses the impact of a decrease in air quality and the risk of hospital admissions to a public hospital for chronic respiratory diseases for residents of Petaling Jaya, a city in the Greater Kuala Lumpur area in Malaysia. Data on hospital admissions for asthma, bronchitis, emphysema and other chronic obstructive pulmonary disease, weather conditions and the Malaysian Air Pollution Index, a composite indicator of air quality, were collated. An unconstrained distributed lag model to obtain risk of hospitalization for a 10 μg/m3 increase in the API. The lag cumulative effect for a 10 μg/m3 increase in the API was calculated to test for harvesting in the short term. Findings indicate that after an initial decrease in admissions (days 3 and 4), admissions increased again at day 7 and 8 and this relationship was significant. We therefore conclude that a 10 μg/m3 increase has a greater effect on admissions for respiratory health in the short term than a harvesting effect alone would suggest. These results suggest that while air quality is improving in the Greater Kuala Lumpur area, no level of air pollution can be deemed safe.

Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.

Climate change and declining fertility rate in Malaysia: the possible connexions

Climate change is an incessant global phenomenon and has turned contentious in the present century. Malaysia, a developing Asian country, has also undergone significant vicissitudes in climate, which has been projected with significant deviations in forthcoming decades. As per the available studies, climate changes may impact on the fertility, either via direct effects on the gonadal functions and neuroendocrine regulations or via several indirect effects on health, socioeconomic status, demeaning the quality of food and water. Malaysia is already observing a declining trend in the Total fertility rate (TFR) over the past few decades and is currently recorded below the replacement level of 2.1 which is insufficient to replace the present population. Moreover, climate changes reportedly play a role in the emergence and cessation of various infectious diseases. Besides its immediate effects, the long-term effects on health and fertility await to be unveiled. Despite the huge magnitude of the repercussion of climate changes in Malaysia, research that can explain the exact cause of the present reduction in fertility parameters in Malaysia or any measures to preserve the national population is surprisingly very scarce. Thus, the present review aims to elucidate the possible missing links by which climate changes are impairing fertility status in Malaysia.

Towards the Healthy Community: Residents’ Perceptions of Integrating Urban Agriculture into the Old Community Micro-Transformation in Guangzhou, China

In the renewal of old communities, one of the development directions is to improve health and enhance well-being. A healthy community includes four aspects of health, namely, healthy production, healthy lifestyle, healthy environment and ecosystem, and healthy physical and mental states of residents living in the community. Urban agriculture (UA), as a form of the community garden, is a supplementary form for the lack of production function in the urban community. It also has the potential to contribute to sustainable and resilient urban communities. This study focuses on analysing the health benefits of UA and attempts to identify old community residents’ attitudes and perceptions towards UA and understand their confusion and worry. The purpose of this study is to promote the healthy and sustainable development of old communities by integrating UA into the micro-transformation of old communities and provide planning and design strategies and community development ideas for the micro-transformation. Surveys were conducted on 10 old communities in Yuexiu district, located in Guangzhou, China. Statistical analysis was conducted using IBM Statistical SPSS version 26 to obtain information on the factor structure of residents’ perceptions towards the health benefits of UA. The analysis results showed significant differences between gender groups and the status of children on old community residents’ perceptions towards general UA benefits. The main factors accounting for old community residents’ perceptions towards the health benefits of UA were environmental health benefits, physical and psychological health benefits, and community health benefits. When developing UA in old communities, co-construction and co-sharing mode, public participation mode, and promotion mode are three important development strategies. Construction location, design style, and seasonal design are also critical for the construction of UA in old communities.

Quantification of mortality and morbidity attributed to the ambient air criteria pollutants in Shiraz city, Iran

According to the epidemiological surveys, ambient air pollution has directly related to mortality and different diseases such as cardiovascular and respiratory defects. Among the atmospheric contaminants, criteria air ones (NO₂, O₃, PM_2.5/10, SO₂) demonstrated that have particular importance in the community disease. The overall goal of this paper was to study the impact of criteria air contaminants on the health of the inhabitants of Shiraz city, Iran. To accomplish this, the AirQ_2.2.3 software was applied. The results of the study revealed that the annual average NO₂, SO₂, PM_2.5, PM_10, and O₃ concentrations are 39.98, 27.6, 14.35, 46.16, and 120.03 μg/m³ in 2016 and 30.27, 23.97, 16.45, 51.65, and 52.58 μg/m³ in 2017. The total International Classification of Diseases (ICD), cardiovascular, and respiratory mortalities caused by air contaminants in Shiraz was predicted as 911, 628, and 182 cases in 2016, and 346, 370, and 82 cases in 2017, respectively. Sulfur dioxide (SO₂) had the greatest rate of total mortality with the attributable equivalent of 4.3% in 2016, but this value has been decreased to 0.42% in 2017. The findings of this research revealed that air contamination has caused problems in Shiraz city according to the predicted results. The findings of this work provide useful data for regional and national health policymakers, who should take decisions to develop strategies for control air contaminants and estimate the cost-effectiveness of interventions.

Prenatal exposure to ambient air multi-pollutants significantly impairs intrauterine fetal development trajectory

BACKGROUND

Impaired in utero fetal growth trajectory may have long term health consequences of the newborns and increase risk of adulthood metabolic diseases. Prenatal exposure to air pollution has been linked to fetal development restriction; however, the impact of exposure to ambient air pollutants on the entire course of intrauterine fetal development has not been comprehensively investigated.

METHODS

During 2015-2018, two cohorts of mother-infant dyads (N = 678 and 227) were recruited in Shanghai China, from which three categories of data were systematically collected: (1) daily exposure to six air pollutants during pregnancy, (2) fetal biometry in the 2nd (gestational week 24, [GW24]) and 3rd trimester (GW36), and (3) neonatal outcomes at birth. We investigated the impact of prenatal exposure to air pollutant mixture on the trajectory of fetal development during the course of gestation, adjusting for a broad set of potential confounds.

RESULTS

Prenatal exposure to PM_2.5, PM₁₀, SO₂ and O₃ significantly reduced fetal biometry at GW24, where SO₂ had the most potent effect. For every 10 μg/m³ increment increase of daily SO₂ exposure during the 1st trimester shortened femur length by 2.20 mm (p = 6.7E-21) translating to 5.3% reduction from the average of the study cohort. Prenatal air pollution exposure also decreased fetal biometry at GW36 with attenuated effect size. Comparing to the lowest exposed quartile, fetus in the highest exposed quartile had 6.3% (p = 3.5E-5) and 2.1% (p = 2.4E-3) lower estimated intrauterine weight in GW24 and GW36, respectively; however, no difference in birth weight was observed, indicating a rapid catch-up growth in the 3rd trimester.

CONCLUSIONS

To our knowledge, for the first time, we demonstrated the impact of prenatal exposure to ambient air pollutants on the course of intrauterine fetal development. The altered growth trajectory and rapid catch-up growth in associated with high prenatal exposure may lead to long-term predisposition for adulthood metabolic disorders.

Air pollution and hospital admissions due to deep vein thrombosis (DVT) in Ahvaz, Iran

There are limited studies on the relation between short-term exposure to air pollutants and the risk of deep venous thrombosis (DVT). The aim of this study was to determine the relation between the concentration of these pollutants and the risk of hospital admissions due to DVT in Ahvaz, which is one of the world's highly polluted cities. Daily data on pollutants including O₃, NO, NO₂, SO₂, CO, PM₁₀, and PM_2.5and DVT hospital admissions were collected from2008until 2018. Quasi-Poisson regression combined with linear distributed lag models; adjusted for trend, seasonality, temperature, relative humidity, weekdays, and holidays were used to assess the relation between the daily average of air pollutants and hospital admission for DVT. The results showed that there was a significant increase in hospital admissions due to DVT in the total, men, women, and elderly populations in relation to NO and NO₂. There was also a significant increase in DVT hospital admissions in the male and ≤60 years populations related to PM₁₀; and among the female and ≤60 years old populations, related to PM_2.5. Finally, the results showed that there were significant positive associations between SO2 and CO exposure and the incidence of DVT hospital admissions among men and women, respectively. The results of this study show the possible effect of short-term exposure to air pollution on the risk of DVT. Further studies are required to investigate whether direct interventions through industry and government policy may alter the impact of specific pollutants in order to alter the incidence of DVT and other identified health complications.

Ambient air pollution and its influence on human health and welfare: an overview

Human health is closely related to his environment. The influence of exposure to air pollutants on human health and well-being has been an interesting subject and gained much volume of research over the last 50 years. In general, polluted air is considered one of the major factors leading to many diseases such as cardiovascular and respiratory disease and lung cancer for the people. Besides, air pollution adversely affects the animals and deteriorates the plant environment. The overarching objective of this review is to explore the previous researches regarding the causes and sources of air pollution, how to control it and its detrimental effects on human health. The definition of air pollution and its sources were introduced extensively. Major air pollutants and their noxious effects were detailed. Detrimental impacts of air pollution on human health and well-being were also presented.

Impacts of Outdoor Air Pollution on Human Semen Quality: A Meta-Analysis and Systematic Review

Introduction

Several studies have explored the association between outdoor air pollution and semen quality. However, the results were inconsistent. We performed the current meta-analysis to evaluate the role of outdoor air pollution in semen quality. Material and Methods. Databases including PubMed, Web of Science, and Embase were searched to identify relevant studies. Relative data in participants under higher exposure and lower exposure to air pollution were extracted. Pooled weighted mean differences (WMDs) with corresponding 95% confidence intervals (CIs) were utilized to assess the effects of outdoor air pollution on semen quality. In addition, trial sequential analyses (TSAs) were performed to obtain a more comprehensive assessment of analyses.

Results

A total of 11 studies with 4562 males were enrolled in the current meta-analysis. Higher air pollution levels were associated with significant decreases in semen volume (WMD: -0.16, 95% CI: -0.27 to -0.05), sperm concentration (WMD: -5.52, 95% CI: -9.88 to -1.16), progressive motility (WMD: -6.23, 95% CI: -11.64 to -0.81), total motility (WMD: -7.65, 95% CI: -14.09 to -1.20), and normal sperm morphology rate (WMD: -3.71, 95% CI: -5.59 to -1.82). In addition, the DNA fragmentation index significantly increased (WMD: 4.11, 95% CI: 1.94 to 6.29).

Conclusions

Air pollution is associated with decreased semen volume, sperm concentration, motility, and normal morphology rate.

Effect of air pollution on hospitalization for acute exacerbation of chronic obstructive pulmonary disease, stroke, and myocardial infarction

This study aims to analyze the acute effects of PM_2.5, PM₁₀, SO₂, NO₂, and O₃ on hospitalizations for acute exacerbation of chronic obstructive pulmonary disease (AECOPD), stroke, and myocardial infarction (MI) from 2014 to 2017 in Shenyang, China. Hospitalization records for AECOPD (17,655), stroke (276,736) and MI (26,235) and air pollutions concentration data (PM_2.5, PM₁₀, SO₂, NO₂, and O₃) were collected. A generalized additive model (GAM) was utilized to determine the impact of air pollutants on the relative risk (RR) of hospitalization for AECOPD, stroke, and MI. Stratified analysis for AECOPD was based on gender and age. It was based on gender, age, hypertension, and diabetes for stroke, and for MI it was based on gender, age, and coronary atherosclerosis. The lag effect for AECOPD in terms of gender analysis occurred at lag3-lag5. The hospitalization risk for stroke with hypertension due to SO₂ and NO₂ was greater than that of stroke without hypertension. The risk of hospitalization for stroke with hypertension as a comorbidity due to O₃ was lower than without hypertension. The risk of hospitalization for MI combined with coronary atherosclerosis due to PM_2.5, PM₁₀, or NO₂ was higher than that of hospitalizations for MI without coronary atherosclerosis. Air pollution increased the rate of hospitalization for AECOPD. SO₂ and O₃ appeared protective for stroke patients with coronary atherosclerosis. PM_2.5, PM₁₀, and NO₂ had no influence on total hospitalization for myocardial infarction.

Association between outdoor air pollution and semen quality: Protocol for an updated systematic review and meta-analysis

BACKGROUND

Various studies have explored the association between outdoor air pollution and semen quality. However, the results were still controversial. The object of the current meta-analysis is to evaluate the role of outdoor air pollution in semen quality.

METHODS

Databases including PubMed, Web of Science, and Embase will be searched to identify qualified studies. All qualified cross-sectional studies researching the association between outdoor air pollution and sperm parameters will be included. Relative data in participants under higher exposure and lower exposure to air pollution will be extracted by 2 investigators independently. Only participants under the highest and the lowest exposure will be enrolled if the original study contained more than 2 exposure levels. The semen volume, sperm concentration, progressive motility, total motility, and normal morphology rate will be the primary outcomes of the current study. Pooled estimates with corresponding 95% confidence intervals will be calculated to assess the specific effects of outdoor air pollution in semen quality. Moreover, trial sequential analyses will be performed to obtain a more comprehensive assessment of analyses.

RESULTS

A high-quality synthesis of the current evidence for the association between sperm parameters and outdoor air pollution will be provided.

CONCLUSIONS

This meta-analysis and systematic review will generate evidence for judging whether outdoor air pollution can impair semen quality.

PROSPERO REGISTRATION NUMBER

PROSPERO CRD 42019126060.