摘要
采用统计调查分析方法,对京津冀地区2014年霾气象服务情况进行调查。在此基础上,统计分析了公众对霾的认知程度、防护措施及预报服务评价等情况,并利用直接损失评估法和疾病成本法等方法,对北京地区2014年霾健康气象服务减少的健康人口损失和经济损失进行了估算。研究发现,京津冀地区的公众对霾的认知度和关注度极高,但公众对霾预报气象服务满意程度仍有较大的提升空间;七成以上的公众会根据霾预报服务信息采取适当的防护措施。经估算,北京市2014年由于PM2.5造成的健康人口损失数为3085人,占总死亡人数的2.3%,造成的健康经济损失值为24.52亿元;未来在与2014年同等的PM2.5暴露浓度之下,公众采用相应气象服务后,在不同的情景下所接受的PM2.5年平均暴露浓度减少5%和10%的情况下,北京市每年可能减少的健康人口损失数为113~226人,可能减少健康经济损失值为0.9~1.8亿元。
This study investigates the meteorological service on haze in the Beijing-Tianjin-Hebei region in 2014 by using the statistical investigation analysis method.On the basis of this,the awareness,protective measures and forecast service evaluations of the public about haze are analyzed.In addition,the reduced population loss and economic loss due to the health meteorological service on haze in Beijing region in 2014 is estimated with the direct loss assessment approach and disease cost approach.The results show that the public awareness and attention to haze in the Beijing-Tianjin-Hebei region is very high,but there is still much room for the public to improve their satisfaction to the haze forecasting service.More than 70%of the public would like to take appropriate protective measures according to haze forecast information.It is estimated that the population loss caused by PM2.5 in Beijing in 2014 was 3085 people(2.3%of all deaths)and the economic loss was 2.452 billion Yuan(RMB).With the same PM2.5 exposure concentration in the future as in 2014,the average received PM2.5 concentration would decline by 5%and 10%after the public use the relevant meteorological service.In such cases,the population loss in Beijing would reduce by 113226 people and the economic loss would decrease by 90180 million Yuan.
作者
吕明辉
张晓美
李筱竹
杨丹丹
Lv Minghui;Zhang Xiaomei;Li Xiaozhu;Yang Dandan(Public Weather Service Center of China Meteorological Administration,Beijing 100081,China;Shanghai Key Laboratory of Meteorology and Health,Shanghai 200030,China)
出处
《气象与环境科学》
2020年第3期18-23,共6页
Meteorological and Environmental Sciences
基金
上海市气象与健康重点实验室开放基金项目(QXJK201407)
国家重点研发计划重点专项项目课题4(2018YFC0807004)。
