环境化学
污染
致癌物
环境科学
多环芳烃
风险评估
环境卫生
化学
有机化学
生物
计算机科学
生态学
医学
计算机安全
作者
Li Bai,Xiya Geng,Xinru Liu
标识
DOI:10.1016/j.envpol.2024.124816
摘要
In recent years, research on air pollution in cooking environments has gained increasing attention, particularly studies related to polycyclic aromatic hydrocarbons (PAHs) pollution. Hence, it is crucial and urgent to conduct a comprehensive review of research findings and further evaluate their carcinogenic risks. This study adopts a comprehensive literature review approach, systematically integrating and deeply analyzing the conclusions and data from 62 selected relevant studies. It focuses on the impact of different factors on PAHs concentrations, considers the indoor-outdoor PAHs concentration ratio, and conducts carcinogenic risk assessments for PAHs. The results show that Africa has the highest average PAHs pollution concentration globally at 14.74μg/m³, exceeding that of other continents by 1.5 to 160.9 times. Among various influencing factors, fuel type has the most significant impact on PAHs concentrations. Existing research data indicate that cooking with charcoal as fuel produces the highest PAHs concentration at 223.52μg/m³, with high molecular weight PAHs accounting for 58.16%, significantly higher than when using clean energy. Furthermore, efficient ventilation systems have been proven to substantially reduce PAHs concentrations, with a reduction rate of up to 88.1%. However, cooking methods and food types also have a small but non-negligible impact on PAHs production. Using mild cooking methods such as steaming and selecting low-fat foods can also reduce PAHs to some extent. Additionally, through the analysis of the Indoor/Outdoor ratio, it was found that cooking is the primary source of indoor pollution, and the average concentration of PAHs in cooking environments in Asia and Africa is much higher than in Europe and America. The Total Incremental Lifetime Cancer Risk (TILCR) exceeds 10⁻⁴, indicating a high level of carcinogenic risk.
科研通智能强力驱动
Strongly Powered by AbleSci AI