国家环境经济政策进展评估报告2022

本年度报告采取实地调研法和政策分析法,针对2022年我国环境经济政策实践开展系统评估,总体认为2022年环境经济政策体系建设不断完善,环境经济政策改革与创新工作取得积极进展,有力推动了高质量发展动力转换、绿色发展结构转型,在生态文明与美丽中国建设中发挥了重要作用。未来需要进一步优化环境经济政策,建立健全一套更加科学合理、公平长效、激发活力的环境经济政策体系,充分支撑环境质量改善与高质量发展。     

北京冬季奥运会历史同期大气PM2.5污染特征分析

基于2015~2021年的1~3月北京市大气PM_2.5浓度与化学组成长期观测数据,分析了2022年北京冬季奥林匹克运动会（冬奥会）和北京冬季残疾人奥林匹克运动会（冬季残奥会）历史同期的PM_2.5污染态势、化学组成特征以及潜在源区.2015~2018年的1~3月重污染［日均ρ（PM_2.5）>75 μg·m^-3］天数以及重污染期间PM_2.5平均值下降十分显著,之后这两者未发生明显改变.2018~2021年的1~3月每年平均发生重污染23 d,重污染天ρ（PM_2.5）平均值约为120.0 μg·m^-3.2015~2021年的1~3月超长重污染过程（连续重污染超过5 d）平均每年发生2~3次,其中2021年发生3次,且持续时间最长达到8 d.历年冬奥会历史同期发生重污染的天数为2~9 d,春节期间烟花爆竹大量燃放可能是该时期重污染发生的重要原因之一;冬季残奥会历史同期重污染天数一般为1~5 d,但2021年受频繁出现的静稳天气影响,重污染天数高达9 d.在同时段重污染期间,PM_2.5化学组成均以二次组分为主,例如在PM_2.5可测组分中,2020年NO₃^-质量分数高达46%,较同年清洁天（11%）显著增加;SO₄^2-质量分数为12%~19%,说明当前硫酸盐污染仍不容忽视.北京市1~3月PM_2.5主要贡献区域包括内蒙古自治区中西部、河北省、天津市、山西省、陕西省、山东省中西部和河南省北部.研究结果将为北京市冬季空气质量持续改善以及2022年冬奥会与冬季残奥会期间北京市环境空气质量保障提供科学依据.     

条件非线性最优扰动在热带气旋调控减灾中的应用初探

为主动减小热带气旋灾害,给将来热带气旋调控的工程实践奠定理论基础。从RossHoffman对热带气旋调控减灾的研究出发,引出了作为非线性最优控制技术的四维变分天气控制版本。简要介绍了条件非线性最优扰动(CNOP)的概念,在此基础上,重点介绍了CNOP方法与四维变分方法相比改进方面的主要应用,它可以利用控制过程中的观测数据,从而在天气控制方面优于四维变分技术。进一步研究了CNOP在热带气旋控制中的另外两个应用,即对初始扰动发展为热带气旋的反演以及利用CNOP寻求产生最大垂直风切变的扰动。新的非线性最优控制技术可称为"非线性最优强迫变分(NOFV)"或"非线性最优强迫扰动(NOFP)"方法,它可以使控制尽可能接近观测。研究表明,CNOP方法在热带气旋调控减灾中大有用武之地。     

Pioneering observation of atmospheric volatile organic compounds in Hangzhou in eastern China and implications for upcoming 2022 Asian Games

Understanding the emission sources of volatile organic compounds (VOCs) is critical for air pollution mitigation. Continuous measurements of atmospheric VOCs were conducted from January to February in Hangzhou in 2021. The average measured concentration of total VOCs (TVOCs) was 38.2 ± 20.9 ppb, > 42% lower than that reported by previous studies at the urban center in Hangzhou. The VOC concentrations and proportions were similar between weekdays and weekends. During the long holidays of the Spring Festival in China, the concentrations of TVOCs were ∼50% lower than those during the regular days, but their profiles showed no significant difference (p > 0.05). Further, we deduced that aromatics and alkenes were the most crucial chemicals promoting the formation of O₃ and secondary organic aerosol (SOA) in Hangzhou. According to interspecies correlations, combustion processes and solvent use were inferred as major VOC emission sources. This study provides implications for air quality improvements before and during the upcoming Asian Games that will be hosted in Hangzhou in 2022.     

异常性检测算法在引信干扰信号识别中的应用

目的 解决传统分类引信抗干扰算法因干扰信号难获取、特征信号样本少、正负样本数不平衡而导致计算精度低的问题,克服引信抗干扰算法对样本的依赖性,并提高引信信号识别准确率。方法 通过WVD时频变换的方法,将拆分后的真实含扰引信信号切片进行重组,使其由一维时序信号向二维图片信息进行扩展,基于数据倍增策略,提升算法泛化性,并降低其对真实数据样本的依赖。融合GANomaly与EfficientNet网络,在扩充的引信数据集上进行线下干扰信号特征学习,并对含扰引信图像数据进行线上异常性判断与干扰信号识别。结果 GE-FS网络能够在真实引信小样本信号的基础上进行有效数据扩充,基于扩充数据训练后,引信含扰识别准确率达到98.4%。结论 GE-FS网络能有效针对引信异常信号进行精确检测与识别,可以增强引信系统的抗干扰能力与作战自适应性。     

Local and regional contributions to PM2.5 in the Beijing 2022 Winter Olympics infrastructure areas during haze episodes

• Regional transportation contributed more than local emissions during haze episodes. • Short-range regional transportation contributed the most to the PM_2.5 in the OIAs. • Low wind speeds and low PBLHs led to higher local contributions to Beijing. The 2022 Winter Olympics is scheduled to take place in Beijing and Zhangjiakou, which were defined as OIAs (Olympic infrastructure areas) in this study. This study presents the characteristics and source apportionment of PM_2.5 in the OIAs, China. The entire region of mainland China, except for the OIAs, was divided into 9 source regions, including four regions in the BTH(Beijing-Tianjin-Hebei) region, the four provinces surrounding the BTH and the remaining areas. Using CAMx/PSAT, the contributions of the nine regions to the PM_2.5 concentration in the OIAs were simulated spatially and temporally. The simulated source apportionment results showed that the contribution of regional transportation was 48.78%, and when PM_2.5 concentration was larger than 75 μg/m³ central Hebei was the largest contributor with a contribution of 19.18%, followed by Tianjin, northern Hebei, Shanxi, Inner Mongolia, Shandong, southern Hebei, Henan and Liaoning. Furthermore, the contribution from neighboring regions of the OIAs was 47.12%, which was nearly twice that of long-range transportation. Haze episodes were analyzed, and the results presented the importance of regional transportation during severe PM_2.5 pollution periods. It was also found that they were associated with differences in pollution sources between Zhangjiakou and Beijing. Regional transportation was the main factor affecting PM_2.5 pollution in Zhangjiakou due to its low local emissions. Stagnant weather with a low planetary boundary layer height and a low wind velocity prevented the local emitted pollutants in Beijing from being transported outside, and as a result, local emissions constituted a larger contribution in Beijing.