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Scavenging of PM2.5 by precipitation and the effects of precipitation pattern changes on health risks related to PM2.5 in Tokyo, Japan

机译:降水对PM2.5的清除以及降水模式变化对与PM2.5相关的健康风险的影响

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Fine particulate matter (aerodynamic diameter <2.5 mu m; PM2.5) poses risks to human health. While precipitation is the main process for decreasing ambient pollutant concentrations, scavenging of PM2.5 by precipitation remains to be investigated. Here we formulated the processes of PM2.5 scavenging by precipitation from observed PM2.5 concentrations ([PM2.5]) and precipitation intensities. Then we analyzed how changes in precipitation patterns would affect health risks related to PM2.5 on the basis of a Monte Carlo simulation. Tokyo, the capital of Japan, was selected as the target for this study because of its social significance. We found that [PM2.5] decreased significantly through scavenging of PM2.5 from the atmosphere by precipitation. In contrast, we found no significant correlation between reduction of [PM2.5] and precipitation intensity. Our model for estimating the reduction of PM2.5 and the Monte Carlo simulation showed good agreement with observations. Among various changes in potential precipitation patterns, changes in the arithmetic mean of the number of events and/or in precipitation duration were more influential on reduction of [PM2.5] than changes in their standard deviations. Health risks due to PM2.5 will increase with decreases in precipitation duration and occurrence.
机译:细颗粒物(空气动力学直径<2.5微米; PM2.5)对人体健康构成威胁。虽然降水是降低环境污染物浓度的主要过程,但通过降水清除PM2.5仍有待研究。在这里,我们根据观测到的PM2.5浓度([PM2.5])和降水强度制定了通过沉淀清除PM2.5的过程。然后,我们在蒙特卡洛模拟的基础上分析了降水模式的变化将如何影响与PM2.5相关的健康风险。由于其社会意义,日本首都东京被选为这项研究的目标。我们发现,通过沉淀从大气中清除PM2.5,[PM2.5]显着降低。相反,我们发现[PM2.5]的降低与降水强度之间没有显着相关性。我们用于估计PM2.5减少量的模型和蒙特卡洛模拟结果与观察结果吻合良好。在潜在的降水模式的各种变化中,事件数量的算术平均值和/或降水持续时间的变化比标准差的变化对[PM2.5]降低的影响更大。 PM2.5引起的健康风险将随着降水持续时间和发生时间的减少而增加。

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