Impacts of traffic-related particulate matter pollution on semen quality: A retrospective cohort study relying on the random forest model in a megacity of South China

Critical windows for exposure to chemical composition of ambient particulate matter and human semen quality decline

BACKGROUND

Critical windows for exposure to chemical components of particulate matter (PM <2.5 μm in diameter [PM2.5]) associated with the human semen quality decline remain unclear.

OBJECTIVES

To address this gap, we developed a new analytical framework by integrating a Linear Mixed Model (LMM) with subject- and center-specific intercepts and a Distributed Lag Model (DLM) to fully account for correlations between finely vulnerable exposure windows based on complete profile of the spermatogenesis cycle.

METHODS

We constructed a multicenter cohort involving 33,234 sperm donors with 78,952 semen samples covering 6 representative regions across China from 2014 to 2020 to investigate the week-scale critical windows for the exposure. Daily exposure to PM2.5 chemical components of donors was derived from grid data based on 1-km spatial resolution surface measurements.

RESULTS

Decreased sperm count was significantly associated with NO3- and SO42- at 9-10 weeks (e.g., β: -0.05 %, 95%CI: [-0.10 %, -0.00 %] at the 9th week) and 0-2 weeks (e.g., β: -0.66 %, 95%CI: [-1.24 %, -0.07 %] at the 1st week), respectively. Critical windows of progressive motility decline were 0-10 weeks for BC (e.g., β: -0.07 %, 95%CI: [-0.11 %, -0.03 %] at the 5th week), Cl- at 1-4 weeks (e.g., β: -2.21 %, 95%CI: [-3.77 %, -0.66 %] at the 2nd week), 0-6 weeks and 9-10 weeks for NO3- (e.g., β: -0.05 %, 95%CI: [-0.09 %, -0.01 %] at the 4th week), 1-3 weeks and the 8th week for NH4+ (e.g., β: -0.06 %, 95%CI: [-0.11 %, -0.01 %] at the 2nd week). Total motility is significantly negatively associated with BC at entire windows, Cl- at 0-3 weeks, the 5th week and 9-10 weeks.

CONCLUSIONS

There are week-scale vulnerable windows of exposure to PM2.5 chemical components for human semen quality. This highlights the need for more targeted pollution control strategies addressing PM2.5 and its chemical components.

Negative association of atmospheric pollutants with semen quality: A cross-sectional study in Taiyuan, China

BACKGROUND

In recent decades, the quality of male semen has decreased worldwide. Air pollution has been linked to lower semen quality in several studies. However, the effects of atmospheric pollutants on different semen characteristics have not always been consistent. The aim of this study was to investigate the association between the Air Quality Index (AQI) and six atmospheric pollutants (PM2.5, PM10, SO2, NO2, CO, and O3), semen quality, and their key exposure window periods.

METHODS

This study included 1711 semen samples collected at the reproductive clinics of the First Affiliated Hospital of Shanxi Medical University in Taiyuan, Shanxi, China, from October 10, 2021, to September 30, 2022. We evaluated the association of AQI and six atmospheric pollutants with semen quality parameters throughout sperm development and three key exposure windows in men using single-pollutant models, double-pollutant models, and subgroup analyses of semen quality-eligible groups.

RESULTS

Both the single-pollutant model and subgroup analyses showed that PM, CO, and O3 levels were negatively correlated with total and progressive motility. At 70-90 d before semen collection, CO exposure and semen volume (β =-1.341, 95 % CI: -1.805, -0.877, P <0.001), total motility (β =-2.593, 95 % CI: -3.425, -1.761, P <0.001), and progressive motility (β =-4.658, 95 % CI: -5.556, -3.760, P <0.001) were negatively correlated. At 0-9 days before semen collection, CO was negatively correlated with normal morphology (β =-3.403, 95 % CI: -5.099, -1.708, P <0.001). Additionally, the AQI was adversely associated with total and progressive motility in subgroup analyses of the semen quality-eligible groups.

CONCLUSIONS

During the entire sperm development process, multiple air pollutants were determined to have an adverse correlation with semen quality parameters. AQI was significant marker for the combined effects of various atmospheric pollutants on male reproductive health.

Exposure to ambient ozone and sperm quality among adult men in China

BACKGROUND

Limited evidence exists regarding the association between ozone exposure and adverse sperm quality. We aimed to assess the association between ozone exposure and sperm quality, and identify susceptible exposure windows.

METHODS

We recruited 32,541 men aged between 22 and 65 years old attending an infertility clinic in Wuhan, Hubei Province, China from 2014 to 2020. Ozone data were obtained from a satellite-based spatiotemporal model. Generalized linear models were used to estimate the association between ozone exposure and sperm quality parameters, including sperm concentration, sperm count, sperm total motility, and sperm progressive motility during the entire stage of sperm development (0-90 days before ejaculation) and three crucial stages (0-9 days, 10-14 days and 70-90 days before ejaculation). Stratified analyses were performed to evaluate whether associations varied by age, body mass index, and education levels.

RESULTS

The final analysis included 27,854 adult men. A 10 μg/m3 increase in ozone concentrations during the entire stage of sperm development was associated with a -4.17 % (95 % CI: -4.78 %, -3.57 %) decrease in sperm concentration, -6.54 % (95 % CI: -8.03 %, -5.60 %) decrease in sperm count, -0.50 % (95 % CI: -0.66 %, -0.34 %) decrease in sperm total motility, and -0.07 % (95 % CI: -0.22 %, 0.09 %) decrease in sperm progressive motility. The associations were stronger during 70-90 days before ejaculation and among men with middle school and lower education for sperm concentration.

CONCLUSIONS

Ozone exposure was associated with decreased sperm quality among Chinese adult men attending an infertility clinic. These results suggest that ozone may be a risk factor contributing to decreased sperm quality in Chinese men.

The Association between Short-Term Exposure to PM1 and Daily Hospital Admission and Related Expenditures in Beijing

Ambient particulate matter (PM) pollution is a leading environmental health threat worldwide. PM with an aerodynamic diameter ≤ 1.0 μm, also known as PM1, has been implicated in the morbidity and mortality of several cardiorespiratory and cerebrovascular diseases. However, previous studies have mostly focused on analyzing fine PM (PM2.5) associated with disease metrics, such as emergency department visits and mortality, rather than ultrafine PM, including PM1. This study aimed to evaluate the association between short-term PM1 exposure and hospital admissions (HAs) for all-cause diseases, chronic obstructive pulmonary disease (COPD), and respiratory infections (RIs), as well as the associated expenditures, using Beijing as a case study. Here, based on air pollution and hospital admission data in Beijing from 2015 to 2017, we performed a time-series analysis and meta-analysis. It was found that a 10 μg/m3 increase in the PM1 concentration significantly increased all-cause disease HAs by 0.07% (95% Confidence Interval (CI): [0, 0.14%]) in Beijing between 2015 and 2017, while the COPD and RI-related HAs were not significantly associated with short-term PM1 exposure. Meanwhile, we estimated the attributable number of HAs and hospital expenditures related to all-cause diseases. This study revealed that an average of 6644 (95% CI: [351, 12,917]) cases of HAs were attributable to ambient PM1, which was estimated to be associated with a 106 million CNY increase in hospital expenditure annually (95% CI: [5.6, 207]), accounting for 0.32% (95% CI: [0.02, 0.62%]) of the annual total expenses. The findings reported here highlight the underlying impact of ambient PM pollution on health risks and economic burden to society and indicate the need for further policy actions on public health.

A novel framework for quantitative attribution of particulate matter pollution mitigation to natural and socioeconomic drivers

Quantifying drivers contributing to air quality improvements is crucial for pollution prevention and optimizing local policies. Despite advances in machine learning for air quality analysis, their limited interpretability hinders attribution on global and local scales, vital for informed city management. Our study introduces an innovative framework quantifying socioeconomic and natural impacts on mitigation of particulate matter pollution in 31 Chinese major cities from 2014 to 2021. Two indices, formulated based on the additivity of Shapley additive explanations, are proposed to measure driver contributions globally and locally. Our analysis explores the self-contained and interactive effects of these drivers on particulate levels, pinpointing critical threshold values where these drivers trigger shifts in particulate matter levels. It is revealed that SO2, NOx, and dust emission reductions collectively account for 51.58 % and 51.96 % of PM2.5 and PM10 decreases at the global level. Moreover, our findings unveil a significant heterogeneity in driver contributions to pollutant mitigation across distinct cities, which can be instrumental in crafting location-specific policy recommendations.

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver's age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

Associations of exposure to ambient fine particulate matter constituents from different pollution sources with semen quality: Evidence from a prospective cohort

The association between ambient fine particulate matter (PM2.5) exposure and semen quality remains inconclusive, possibly due to variations in pollution sources and PM2.5 compositions. Studies investigating the constituents of PM2.5 have been hindered by small sample sizes, and research exploring the relationships between PM2.5 pollution sources and semen quality is lacking. To address this gap, we conducted a comprehensive study based on the Anhui prospective assisted reproduction cohort to evaluate the associations between semen quality and the constituents and pollution sources of PM2.5. This study included 9013 semen samples from 4417 males in the urban districts of Hefei. The median concentrations of PM2.5 constituents, including eight metals and four water-soluble ions (WSIs), were measured for seven days per month at two monitoring stations during the 0-90-day exposure window. A linear mixed-effects model, weighted quantile sum regression, and positive matrix factorisation were used to evaluate the associations of the constituents and pollution sources of PM2.5 with semen quality. The results showed that exposure to PM2.5-bound metals (antimony, arsenic, cadmium, lead, and thallium) and WSIs (sulphate and chloride) were negatively associated with semen quality parameters. Moreover, mixtures of PM2.5-bound metals and WSIs were negatively associated with semen quality. Additionally, PM2.5 derived from traffic emissions was negatively associated with semen quality. In summary, our study revealed that ambient PM2.5 and its constituents, especially metals, were negatively associated with semen quality. Antimony, lead, and thallium emerged as the primary contributors to toxicity, and PM2.5 from traffic emissions was associated with decreased semen quality. These findings have important public health implications for the management of PM2.5 pollution in the context of male reproductive health.

Sperm quality decline associated with gaseous pollutant exposure: Evidence from a large cohort multicenter study

BACKGROUND

Poor sperm quality is a prevalent cause of male infertility, and the association between gaseous ambient air pollutants exposure and semen quality remains unclear.

OBJECTIVES

To examine the relationship between gaseous air pollution exposure with semen quality in a large-scale and multi-center study.

METHODS

We analyzed 78,952 samples corresponding to 33,234 study subjects from 2014 to 2020. The high-resolution grid pollution dataset was used to estimate personal exposures to CO, SO2, NO2 and O3 across entire stage of semen formation and three crucial stages. The linear mixed models were performed to evaluate the relationships.

RESULTS

The results showed that sperm count was inversely related to SO2 exposure (-0.0070, -0.0128 to -0.0011). Decreased sperm concentration was associated with SO2 (-0.0083, -0.0142 to -0.0024), NO2 (-0.0162, -0.0320 to -0.0005) and O3 (-0.0306, -0.0480 to -0.0133) during 0-90 lag days, respectively. Additionally, we observed significant decline of PR and total motility with SO2 exposure. Similar trends were observed for SO2 and CO exposure during 3 key periods.

CONCLUSIONS

Our findings suggest that exposure to gaseous air pollutants may have negative impacts on sperm quality. These findings highlight the importance that critical periods of sperm development should be considered when implementing protective measures.

Does air pollution exposure affect semen quality? Evidence from a systematic review and meta-analysis of 93,996 Chinese men

Background

Air pollution may impair male fertility, but it remains controversial whether air pollution affects semen quality until now.

Objectives

We undertake a meta-analysis to explore potential impacts of six pollutants exposure during the entire window (0-90 days prior to ejaculation) and critical windows (0-9, 10-14, and 70-90 days prior to ejaculation) on semen quality.

Methods

Seven databases were retrieved for original studies on the effects of six pollutants exposure for 90 days prior to ejaculation on semen quality. The search process does not limit the language and search date. We only included original studies that reported regression coefficients (β) with 95% confidence intervals (CIs). The β and 95% CIs were pooled using the DerSimonian-Laird random effect models.

Results

PM2.5 exposure was related with decreased total sperm number (10-14 lag days) and total motility (10-14, 70-90, and 0-90 lag days). PM10 exposure was related with reduced total sperm number (70-90 and 0-90 lag days) and total motility (0-90 lag days). NO2 exposure was related with reduced total sperm number (70-90 and 0-90 lag days). SO2 exposure was related with declined total motility (0-9, 10-14, 0-90 lag days) and total sperm number (0-90 lag days).

Conclusion

Air pollution affects semen quality making it necessary to limit exposure to air pollution for Chinese men. When implementing protective measures, it is necessary to consider the key period of sperm development.

Association between ambient PM1 and semen quality: A cross-sectional study of 27,854 men in China

BACKGROUND

Exposure to ambient fine and respirable particulate matter is associated with poor sperm quality, but evidence for particulate matter with an aerodynamic diameter ≤ 1 μm (PM₁) is scarce. We aimed to estimate the association between PM₁ exposure and sperm concentration, sperm count, sperm total motility, and sperm progressive motility in Chinese men.

METHODS

We conducted a cross-sectional study of 33,221 men attending an infertility clinic in Hubei, China, between 2014 and 2020. Daily concentrations of PM₁ data were estimated from a validated spatiotemporal artificial intelligence model. We used multivariate linear regression to estimate the association between PM₁ exposure and sperm parameters during the spermatogenesis period after adjusting for age, body mass index (BMI), education, ever having fathered a child, and season of semen collection. In addition, we performed stratified analysis to assess whether the association was varied by age, BMI, and educational attainment.

RESULTS

A total of 27,854 participants were included in the final analysis. An interquartile range (17.2 μg/m³) increase in PM₁ during the entire period of semen development was associated with declined semen concentration [-4.39% (95% CI: -7.67%, -1.12%)] and sperm count [-23.56% (95% CI: -28.95%, -18.18%)], reduced total motility [-0.86% (95% CI: -1.66%, -0.06%)] and progressive motility [-2.22% (95% CI: -3.00%, -1.43%)]. The associations were homogeneous across subgroups defined by age and education, but were more pronounced among men with underweight for sperm concentration and sperm count. We identified a critical exposure window of 0-9 lag days, 10-14 lag days, and 70-90 lag days before semen collection for sperm count and progressive motility.

CONCLUSIONS

Among men attending an infertility clinic in China, exposure to PM₁ was associated with poor semen quality, especially during the 70-90 days before ejaculation. These results suggest that exposure to PM₁ might be a novel risk factor for impaired semen quality.