Distribution and Formation Causes of PM_(2.5) and O_(3) Double High Pollution Events in China during 2013–20

Fine particulate matter(PM_(2.5))and ozone(O_(3))double high pollution(DHP)events have occurred frequently over China in recent years,but their causes are not completely clear.In this study,the spatiotemporal distribution of DHP events in China during 2013–20 is analyzed.The synoptic types affecting DHP events are identified with the Lamb–Jenkinson circulation classification method.The meteorological and chemical causes of DHP events controlled by the main synoptic types are further investigated.Results show that DHP events(1655 in total for China during 2013–20)mainly occur over the North China Plain,Yangtze River Delta,Pearl River Delta,Sichuan Basin,and Central China.The occurrence frequency increases by 5.1%during 2013–15,and then decreases by 56.1%during 2015–20.The main circulation types of DHP events are“cyclone”and“anticyclone”,accounting for over 40%of all DHP events over five main polluted regions in China,followed by southerly or easterly flat airflow types,like“southeast”,“southwest”,and“east”.Compared with non-DHP events,DHP events are characterized by static or weak wind,high temperature(20.9℃ versus 23.1℃)and low humidity(70.0%versus 64.9%).The diurnal cycles of meteorological conditions cause PM_(2.5)(0300–1200 LST,Local Standard Time=UTC+8 hours)and O_(3)(1500–2100 LST)to exceed the national standards at different periods of the DHP day.Three pollutant conversion indices further indicate the rapid secondary conversions during DHP events,and thus the concentrations of NO_(2),SO_(2) and volatile organic compounds decrease by 13.1%,4.7%and 4.4%,respectively.The results of this study can be informative for future decisions on the management of DHP events.

Stratospheric Ozone-induced Cloud Radiative Effects on Antarctic Sea Ice

Recent studies demonstrate that the Antarctic Ozone Hole has important influences on Antarctic sea ice.While most of these works have focused on effects associated with atmospheric and oceanic dynamic processes caused by stratospheric ozone changes,here we show that stratospheric ozone-induced cloud radiative effects also play important roles in causing changes in Antarctic sea ice.Our simulations demonstrate that the recovery of the Antarctic Ozone Hole causes decreases in clouds over Southern Hemisphere(SH)high latitudes and increases in clouds over the SH extratropics.The decrease in clouds leads to a reduction in downward infrared radiation,especially in austral autumn.This results in cooling of the Southern Ocean surface and increasing Antarctic sea ice.Surface cooling also involves ice-albedo feedback.Increasing sea ice reflects solar radiation and causes further cooling and more increases in Antarctic sea ice.

Erratum to:Stratospheric Ozone-induced Cloud Radiative Effects on Antarctic Sea Ice

The article“Stratospheric Ozone-induced Cloud Radiative Effects on Antarctic Sea Ice”,written by Yan XIA,Yongyun HU,Jiping LIU,Yi HUANG,Fei XIE,and Jintai LIN was originally published electronically on the publisher’s internet portal on 7 of March 2020 without open access.With the author(s)’decision to opt for Open Choice,the copyright of the article changed on 11 of April 2020 to©The Author(s),2020 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use,you will need to obtain permission directly from the copyright holder.To view a copy of this licence,visit http://creativecommons.org/licenses/by/4.0.

直接和间接消费所引起的中国城乡空气污染相关过早死亡不平等性

Household consumption in China is associated with substantial PM_(2.5)pollution,through activities directly(i.e.,fuel use)and/or indirectly(i.e.,consumption of goods and services)causing pollutant emissions.Urban and rural households exhibit different consumption preferences and living areas,thus their contributions to and suffering from air pollution could differ.Assessing this contrast is crucial for comprehending the environmental impacts of the nation’s ongoing urbanization process.Here we quantify Chinese urban and rural households’contributions to ambient PM_(2.5)pollution and the health risks they suffer from,by integrating economic,atmospheric,and health models and/or datasets.The national premature deaths related to long-term exposure to PM_(2.5)pollution contributed by total household consumption are estimated to be 1.1 million cases in 2015,among which 56%are urban households and 44%are rural households.For pollution contributed indirectly,urban households,especially in developed provinces,tend to bear lower mortality risks compared with the portions of deaths or pollution they contribute.The opposite results are true for direct pollution.With China’s rapid urbanization process,without adequate reduction in emission intensity,the increased indirect pollution-associated premature deaths could largely offset that avoided by reduced direct pollution,and the indirect pollution-associated urban–rural inequalities might become severer.Developing pollution mitigation strategies from both production and consumption sides could help with reducing pollution-related mortality and associated urban-rural inequality.

全球变暖和厄尔尼诺导致青藏高原中部湿地迅速扩张

阐明青藏高原湿地的时空变化规律和驱动因素,对理解青藏高原湿地对气候变化的响应及保护其生态系统和生物多样性至关重要.由于湿地边界的不确定性、遥感数据光谱和纹理特征的复杂性,湿地的智能识别监测面临许多技术挑战.本研究提出了一种面向遥感场景分类的深度学习框架,实现了基于Landsat遥感图像的青藏高原湿地智能提取.结果表明,近30年(1990~2019年)青藏高原湿地面积增加了31.2%,其中青藏高原中部,即羌塘高原、柴达木盆地和三江源地区的湿地面积增长最明显.气温升高引起的多年冻土融化是该地区湿地面积增长的主要驱动力.因厄尔尼诺而导致的气温和降水异常进一步加剧了湿地面积的增长.

Environmental taxation and regional inequality in China

In order to combat environmental pollution, China enacted the Environmental Protection Tax Law in early 2018. Yet the impacts of the environmental tax on individual regions with different socioeconomic statuses, which are crucial for social justice and public acceptance, remain unclear. Based on a MultiRegional Input-Output(MRIO) table and a nationally regulated tax payment calculation method, this study analyzes the distributional impacts of an environmental tax based upon province’s consumption from both inter-provincial and rural-urban aspects. The national tax revenue based on the current levy mechanism is estimated to be only one seventh of the economic loss from premature mortality caused by ambient particulate matter(PM2.5). The taxation may slightly alleviate urban-rural inequality but may not be helpful with reducing inter-provincial inequality. We further analyze two alternative levy mechanisms. If each province imposes taxes to products it consumes(rather than produces, as in the current mechanism), with the tax rate linearly dependent on its per capita consumption expenditure, this would moderately increase the national tax revenue and significantly reduce inter-provincial inequality.To better compensate for the economic costs of air pollution and reduce regional inequality, it would be beneficial to increase the tax rate nationwide and implement a levy mechanism based on provincially differentiated levels of consumption and economic status.

Inequality in historical transboundary anthropogenic PM_(2.5) health impacts

Atmospheric transport of fine particulate matter(PM_(2.5)),the leading environmental risk factor for public health,is estimated to exert substantial transboundary effects at present.During the past several decades,human-produced pollutant emissions have undergone drastic and regionally distinctive changes,yet it remains unclear about the resulting global transboundary health impacts.Here we show that between 1950 and 2014,global anthropogenic PM_(2.5)has led to 185.7 million premature deaths cumulatively,including about 14%from transboundary pollution.Among four country groups at different affluence levels,on a basis of per capita contribution to transboundary mortality,a richer region tends to exert severer cumulative health externality,with the poorest bearing the worst net externality after contrasting import and export of pollution mortality.The temporal changes in transboundary mortality and cross-regional inequality are substantial.Effort to reduce PM_(2.5)-related transboundary mortality should seek international collaborative strategies that account for historical responsibility and inequality.

Impact of“ultra low emission”technology of coal-fired power on PM_(2.5)pollution in the Jing-Jin-Ji Region

In response to severe haze pollution,the Chinese government has announced a series of policies focusing on controlling emissions from coal consumption.“Ultra-low emission”(ULE)technologies have the potential to dramatically reduce emissions from coalfired power plants,and have been deployed at some facilities in recent years.This paper estimated the potential environmental benefits of the widespread adoption of ULE in the Jing-Jin-Ji Region.Atmospheric modeling scenarios were analyzed for three cases:a“standard”scenario assuming no ULE deployment,a“best case”scenario assuming complete adoption of ULE across all power plants in the region,and a“natural gas”scenario,assuming emissions factors consistent with natural gas-fired power generation.The simulations show that the widespread adoption of ULE technologies can be an effective and economically competitive option for reducing the impacts of coal-fired power generation on air quality.

Evaluation of county-level poverty alleviation progress by deep learning and satellite observations

Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries.China,which had the largest rural poverty-stricken population,has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation(TPA)policy in 2014,and by 2020,all national poverty-stricken counties(NPCs)have been out of poverty.This study combines deep learning with multiple satellite datasets to estimate county-level economic develop-ment from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties(NPCs)at country,provincial and county levels.Per capita gross domestic product(GDP)is used to measure the affluence level.From 2014 through 2019,the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%,higher than the average growth rate of 310 adjacent non-NPC counties(7.3%±0.4%)and of the whole country(6.3%).We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages.The inexpensive,timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.