Journal of Environmental Health Science and Engineering最新文献

Purpose

The Naseri Artificial Wetland was created by the discharge of agricultural drainage water, including effluent from the sugarcane development project. The continuous inflow of drainage water from the sugarcane development units has altered the natural regime of the wetland. Considering the high probability of herbicides entering agricultural runoff, this study was conducted to identify atrazine and to assess the health risks of it in this wetland.

Methods

Sixty water samples from the wetland and 15 samples from the drainage channel were collected in three seasons: June (summer), October (Autumn), and February(winter). Variables such as electrical conductivity, dissolved oxygen, temperature, and pH were also monitored at the sampling points.

Results

The average measured ATZ concentration in NAW was lowest in June (0.015 mg/L) and was 0.021 mg/L in October and 0.024 mg/L in February. The average ATZ concentration in the drainage channel in June, October, and February was 0.03, 0.04, and 0.05 mg/L, respectively. The average electrical conductivity and pH in June and October were 29,900 µS/cm, 28,544 µS/cm and 7.29 and 7.28, respectively. The maximum and minimum values ​​of temperature were 30.7 °C in June and 8.6 in February.

Conclusion

The health risk for children and adults, based on the HQ index, was calculated to be 0.12 and 0.014, respectively. Additionally, the carcinogenic risk, based on the ILCR index, was calculated to be 2.7E-2 and 3.2E-3, respectively, which indicates the risk of carcinogenic.

Predicting indoor air quality during infectious disease conditions relies on models simulating particle materials (PM)/bioaerosols distribution. Understanding the thermo-fluid properties of exhaled air is crucial for comprehending disease transmission dynamics. This study employs a computational fluid dynamics (CFD) model to simulate cough-induced particle dispersion in a closed space. Furthermore, the number of released particles and the presence of SARS-CoV-2 viral genomes by a cough were assessed (in eight COVID-19 patients). According to the CFD model, in the first 30 s of cough, the vertical height and lateral breadth of the particles’ dispersion were up to 138cm and 92cm, respectively. As the distance from the patient's respiratory zone increased, the lateral distribution width of particles expanded, reaching 1.3 m at 2.4 m away. Larger droplets (> 62.5µ) were deposited at shorter distances, while smaller particles remained airborne longer. The comparison of experimental and simulated results focused on particle dispersion at specific distances from the patient, particularly in the 2.5µ range. The distribution pattern of PM_2.5 and PM₁₀ at a distance of 1 and 2 m for women, not men, is similar to the distribution pattern of PM in CFD modeling. Viral genome detection was more prevalent in particles near the left side of the body, especially within the first 20 min post-cough, exhibiting a correlation with CFD predictions.

Purpose

The aim of this study was to investigate the impact of improved deep learning model on the predictive performance of PM_2.5 concentration.

Methods

We developed a new model combining one-dimensional convolutional neural network and bidirectional long short-term memory neural network to predict PM_2.5 concentrations at hourly intervals. The air pollution observation data from 2020 to 2022 collected at several national air quality monitoring stations in Shenyang (Liaoning province, China) were employed to train our model. The performance of the proposed model was boosted by connecting the layer of network calculated results with the PM_2.5 sequence data. Furthermore, data of most relevant air quality monitoring stations and PM_2.5 feature factors of the target station were screened. The spatial correlation of major air pollutant and the interaction between PM_2.5 and other pollutant factors were therefore considered to improve the accuracy of the model.

Results

The root mean square error, mean absolute error, mean absolute percentage error of the new method were reduced by 49%, 51%, 44% and the R² was improved by 4.6% respectively compared with the control group for the next hour prediction. The proposed improvement method can reduce the prediction error of the model in the next 6 h.

Conclusions

In this study, the proposed model improvement method can significantly reduce the error of the model in predicting PM_2.5 concentration. The proposed method can improve the model in the next 6 h prediction accuracy. This study provides a new perspective for establishing high-precision models for PM_2.5 prediction.

Bioenergy plays a crucial role in addressing the global energy crisis. The utilization of agricultural byproducts for biofuel production through fermentation is well-established. Among various pretreatment methods, breaking lignin and cellulose bonds under heat and pressure to release sugar moieties is the most predominant approach. This study focuses on enhancing sugar yield through the most economical, energy-efficient, and time-saving pretreatment of the highly underrated agricultural residue, cocoa pod shell (CPS), using green-synthesized FeO nanoparticles derived from CPS extract. The synthesized nanoparticles, ranging from 25 nm to 31 nm in size, exhibited an EDS spectrum confirming the atomic composition of C (30.01%), Fe (6.09%), O (59.76%), N (2.36%), P (0.79%), Cl (0.53%), and K (0.46%). FTIR analysis revealed the presence of O-H, C-H, C-Cl, and O = C = O stretching, indicating effective nanoparticle capping. The novel ex-situ hydrolysis process, coupled with induction heating, yielded 356.04 g/L of total sugars and 60.28 g/L of reducing sugars using 10% w/v biomass and 4% acid within just 30 min. RSM and ANN modeling were employed for process validation, yielding R² values of 0.91 and 0.92 for total and reducing sugars, respectively, while ANN modeling achieved R² values of 0.96 and 0.97. This energy-efficient hydrolysis process achieved a significant sugar yield in less time while requiring minimal raw material. It presents a scalable and reliable approach to the industries, providing a promising direction for biofuel production.

Phthalates are one of the most common environmental contaminants and endocrine disruptors. Environmental exposure to phthalates may increase the risk for allergic diseases. However, the existing literature presents conflicting findings regarding the long-term impact of early-life exposure to these substances.

We searched the Web of Science, PubMed and Google Scholar, Medline and Embase databases for all related publications from 1974 to September 1st, 2024. Ultimately, 22 studies with a total of 16,161 participants were selected. The relative risks (RRs) with 95% confidence intervals (95% CIs) were used to evaluate whether prenatal phthalate exposure is associated with allergic endpoints. To assess statistical heterogeneity across studies, both the Q-test and I² statistic were adopted. Publication bias of the included studies was evaluated using Begg’s and Egger’s tests. Stratified analysis was conducted based on the gender of children, molecular weight of phthalates, disease type, phthalate species, parental exposure period and region. The systematic literature search protocol was formally registered in PROSPERO.

Childhood wheeze (RR 1.10, 95% CI: 1.00-1.21), eczema (RR 1.09, 95% CI: 1.01–1.17), and rhinitis (RR 1.05, 95% CI: 1.02–1.09) are potentially associated with prenatal exposure to phthalates, particularly butyl-benzyl phthalate (RR 1.15, 95% CI: 1.06–1.24), di-ethyl-hexyl phthalate (RR 1.08, 95% CI: 1.02–1.15) and di-iso-nonyl phthalate (RR 1.12, 95% CI: 1.02–1.23).

Maternal phthalate exposure during pregnancy exhibits a significant association with elevated risks of childhood respiratory and allergic manifestations, including wheezing episodes, eczematous dermatitis, and rhinitis symptoms.

Climate change is a global issue that presents significant challenges for countries worldwide, including Iran. Researchers need up-to-date information on climate change within their own country, including statistics on its severity, efforts to address it, and the impacts on the environment, temperatures, extreme weather events, water resources, agriculture, biodiversity, migration, air quality, and human health. This review provides an overview of these topics in the context of Iran, discussing challenges, sustainable practices, renewable energy, government responses, and international collaborations to mitigate climate change effects. It aims to offer a comprehensive perspective on the current and potential future implications of climate change in Iran. Climate change in Iran has resulted in higher temperatures, droughts, and wildfires, impacting agriculture and exacerbating water scarcity. Extreme weather events such as floods and storms are causing damage to infrastructure. Climate change poses a significant threat to global health, with direct consequences including severe storms, heat stress, and deteriorating air quality. Despite this uncertainty, it is imperative to adapt to the adverse effects of climate change. Rising global temperatures are contributing to more frequent and severe extreme weather events, resulting in widespread damage and loss of life. Iran's efforts to address climate change include investing in renewable energy, and implementing sustainable practices. Collaboration between the government and local communities is crucial for mitigating the effects of climate change through effective policies and initiatives. Iran aims to reduce greenhouse gas emissions and promote sustainability through investments in renewable energy and energy efficiency initiatives.

Human pesticide exposure results in the development of several chronic diseases, including cardiometabolic, carcinogenic, neurological, and autoimmune processes. The induction of oxidative stress, subsequent tissue injury, and inflammatory response are widely accepted mechanisms related to environmental pollutants-induced diseases. In this line, several studies have been reported on the induction of systematic inflammatory state related to pesticide exposure. Nevertheless, there needs to be a consensus on the best inflammatory biomarker for measuring in response to pesticide exposure, and sources of risk of bias need to be assessed for future studies. This meta-analysis assessed whether pesticide exposure can start an inflammatory response in humans. A systematic review was performed focused on original reports that analyzed the relationship between human pesticide exposure and pro-inflammatory biomarkers. Fifteen studies were analyzed. The present meta-analysis included 3172 participants. The pooled analysis suggested that pesticide exposure can induce an inflammatory response and indicates that standardized clinical inflammatory biomarkers such as C-reactive protein are more recommended than hematological index or pro-inflammatory cytokines. Moreover, the need to consider multivariate statistical analysis is noted. The results suggested that pesticide-induced inflammatory response could be considered a mechanism through pesticide-induced diseases. These findings contribute to our understanding of the health effects of pesticide exposure and show the need for performing future studies to explore this area further, potentially leading to improved public health strategies.

Biofuels and bioenergy production are increasingly being viewed as viable alternatives to conventional energy sources because of their renewable nature and ability to reduce greenhouse gas emissions. Waste products, lignocellulosic materials, and agricultural residues are some of the feedstocks that can be used to create biofuels, including biodiesel, bioethanol, and biogas. The production of biofuels not only promotes sustainable energy but also addresses environmental problems. This review article explores the challenges posed by dependence on non-sustainable resources and the environmental benefits of renewable energy sources. It provides a detailed examination of the advancements, possibilities, and obstacles linked to biofuels and bioenergy. It outlines the harmful effects of prolonged fossil fuel use on the environment, including soil degradation, air and water contamination, and climate change, highlighting the critical necessity to shift towards renewable energy alternatives. The analysis evaluates the socioeconomic effects of bioenergy and its capacity to enhance food and energy security, generate employment, and boost rural economies. Nevertheless, it also recognizes important obstacles that need to be overcome for wider adoption, competition with food crops, issues related to water consumption, and regulatory constraints. It explores the potential of hydrogen fuel cell vehicles and battery electric vehicles as replacements for conventional vehicles that rely on fossil fuels. It emphasizes the need to explore alternative feedstock sources and implement next-generation conversion processes prioritising environmental sustainability by incorporating recent advancements in machine intelligence (MI), including machine learning and artificial intelligence techniques. The study dedicates considerable effort to exploring the global regulatory and policy landscape, including how various nations promote bioenergy initiatives through financial incentives, blending mandates, and sustainability criteria. To encourage the adoption of bioenergy solutions and facilitate a fair and effective energy transition, the research winds up by highlighting the importance of international collaboration, interdisciplinary investigation, and innovation. With the appropriate laws and technologies in place, biofuels and bioenergy could play an important role in achieving a sustainable, low-carbon future.

Purpose

This study aimed to determine the roles and responsibilities of stakeholders in decision-making, research, policy-making, and the implementation of an adaptation plan, with a comprehensive view of their positions, influence, and power.

Methods

This descriptive-analytical research was conducted using a social network analysis approach. The opinions of 25 university professors, experts, and executives were gathered through a questionnaire utilizing a Likert scale, selected via purposive and snowball sampling. Data analysis and graph design were performed using Microsoft Excel and Gephi software (version 0.9.2). Stakeholder interaction patterns were identified using the Force Atlas 2 algorithm and graph theory concepts.

Results

The network comprised 37 nodes, 3 clusters, and 63 edges. It closely resembled a complete graph, with a density of 0.971. Within the network of stakeholders, the Intergovernmental Panel on Climate Change emerged as the most active participant, exhibiting relatively strong external interactions with other stakeholders. In contrast, the Department of Environmental Protection and the Ministry of Health and Medical Education showed relatively weak and very weak external interactions, respectively.

Conclusion

Given the conflicting interests between industrial sectors and the health sector, it appears that the Ministry of Health and Medical Education should enhance its power and influence over other stakeholders. Additionally, involving representatives from the health sector in policy reviews and stakeholder consultations may help bridge the gap between health and other sectors regarding climate change issues.

Clinical trial number

Not applicable.

Over the last couple of decades, thyroid cancer cases have dramatically increased in frequency year by year. According to certain research, thyroid cancer may be linked to per-fluoroalkyl and poly-fluoroalkyl substances (PFAS) exposure. Our study aimed to investigate the possible link between thyroid cancer and PFAS exposure. Three researchers have looked up the following electronic databases: PubMed, Embase, Web of Science, Scopus, Cochrane, Science Direct, Chinese BioMedical Literature Database (CBM), Wanfang Database, China Science and Technology Journal Database, and China National Knowledge Infrastructure (CNKI) to get the whole articles before March 2024. In addition, Google Scholar was used as a complementary search engine. Finally, eight articles were included in our meta-analysis. It was shown by our results that the link between thyroid cancer and PFAS exposure was not statistically significant, for perfluorononanoic acid (PFNA), odds ratio (OR): 0.71, 95% confidence interval (95% CI): 0.30–1.12; for perfluorooctane sulfonate (PFOS), OR: 0.89, 95% CI: 0.5–1.28; for PFAS mixtures, OR: 1.10, 95% CI: 0.19–2.01. Furthermore, our study revealed that perfluorooctanoic acid (PFOA) exposure could decrease the risk of thyroid cancer (OR: 0.69, 95% CI: 0.44–0.95). These associations of PFOS and PFNA with thyroid cancer were robust in sensitivity analyses. In conclusion, these results increase our knowledge of the impact of PFAS on the thyroid cancer in a novel manner. At the same time, our study has certain limitations, we encourage the inclusion of more high-quality studies in the future.