The rising frequency of infectious diseases under climate change poses an emerging threat to environmental and agricultural sustainability by consuming large quantities of materials. The demand for crops to produce personal protective equipment (PPE) competes for land and fertilizers, leads to cropland expansion, and accelerates climate change, but the ecological impacts remain unclear. Here we explore the impacts of pandemics on global warming, agricultural production, and biodiversity loss in an Earth system model by developing relationships between consumption of PPE and the rate of infection during COVID-19. Meeting the demand for PPE would increase production of cotton lint, corn, and natural rubber, which accelerates global warming by 0.2 degrees C with 1.8% additional species losses by 2100. Our results suggest that the risks of public health, food security, climate change, and ecological integrity have been connected to each other, which should be considered when predicting the impacts of future pandemics.

Bioenergy with Carbon Capture and Storage (BECCS) is a key negative emission technology considered by many integrated assessment models (IAMs) to achieve the 2℃ or 1.5℃ goals in the Paris Agreement. However, the technical feasibility and economic costs of BECCS in these IAMs have been widely debated, which increases the uncertainty in the projection of climate change in the 21st century. Therefore, this paper reviews the latest understanding of BECCS. The key findings reveal the limitations of current models in projecting the capacity and costs of bioenergy with carbon capture and storage (BECCS), mainly due to insufficient consideration of ecological consequences, including availabilities of biomass and difficulties in the transportation of biomass and CO2. To reduce uncertainties in the capacity and costs of BECCS, it is urgently needed to apply spatially explicit method for estimating the life-cycle emissions and the complete cost items when deploying BECCS, optimize the network of biomass acquisition, power plants retrofitting and transportation of biomass and CO2, and represent the changes in the availability of biomass (for different types of bioenergy plants) under the impacts of climate change. This paper emphasizes the gap between the potential capacity of BECCS and the demand for BECCS that is needed to achieve the climate goals. Suggestion on policy interventions is provided to accelerate the application of BECCS from the aspects of economic tools, regulatory tools, and information tools. Deployment of BECCS could be accelerated to halt the rapid rise of global annual average temperature and reduce the risk of carbon lock-in from fossil-fuel supply infrastructure. As BECCS could play a key role in achieving ambitious climate targets, it is important to maintain a balance between environmental, social, and economic considerations in the Earth system under a high sustainability of development.

PM2.5 is the major pollutant in most cities of China, especially contributed significantly to the poor air quality in Beijing. This study aimed to investigate the long-term trend (2000–2015) of PM2.5, based on intensive observation and comprehensive literature investigation of PM2.5 and its chemical components. Results showed that the annual average concentration of PM2.5 generally decreased by 1.5 µg m−3 year−1 from 2000 to 2015 under the implementation of 16 phases’ air pollution control measures. In the most polluted season (winter), four change stages were found related with emission control effect and meteorological conditions: PM2.5 decreased in 2000–2008 and 2010–2013, increased in 2008–2010, and was at a high level in 2013–2015. As for detailed chemical components, OC, soil dust and typical elements (Si, Ca, Fe, Mn, Cu, Pb and K) presented a downward trend generally. EC had almost no change before 2003, increased from 2003 to 2007, but decreased after 2007; this may be caused by the replacement of fossil fuel and control of biomass emission. The continuous rising of OC/EC and SOC/OC (secondary organic carbon, SOC) in recent years illustrated the secondary carbonaceous species pollution is becoming serious. SO42− showed a slight increase from 2000–2013, but decreased obviously from 2013–2015. NO3− and NH4+ had an upward trend during the past decade. The proportion of secondary inorganic aerosol (SIA) increased at a rate of 0.7

The inherent power fluctuations of wind, photovoltaic (PV) and bioenergy with carbon capture and storage (BECCS) create a temporal mismatch between energy supply and demand. This mismatch could lead to a potential resurgence of fossil fuels, offsetting the effects of decarbonization and affecting the realization of the Paris target by limiting global warming to below 2 °C in the 21st century. While application of energy storage is widely recommended to address this limitation, there is a research gap to quantify the impacts of energy storage limitation on global warming. Here, we analyzed the hourly variation of global wind and PV power during the period 1981–2020 and the monthly capacity of biomass production in 2019, and thus quantified the impact of decreasing the capacity of energy storage on global warming using a state-of-the-art Earth system model. We found that global warming by 2100 in the SSP1-2.6 scenario would increase by about 20% and exceed 2 °C without deploying energy storage facilities. Achieving the 2 °C target requires reducing power losses of wind and PV by at least 30% through energy storage. This requirement delivers to a cumulative storage capacity of 16.46 TWh using batteries during the period 2021–2100, leading to the international trade of cobalt and manganese across countries due to deficits of minerals at a country level. In the context of energy security, we highlight the importance of considering the limitations of energy storage and mineral shortage in the forthcoming policies of decarbonization.

根据全球陆地范围内的风速、风功率密度和温度等1981—2020年的历史数据,通过建立空间回归模型,预测2021—2100年不同减排情景下全球风能发电潜力和风功率密度的变化情况,结果表明,1981—2020年,全球风能发电潜力和风功率密度呈现下降的趋势,而全球温度呈现上升的趋势,两者存在一定程度的相关性.简单线性回归模型结果显示,各大洲的年均发电潜力与全球11个地区的平均温度有较好的相关性,且大部分区域都呈现负相关关系.未来不同共享社会经济路径(SSPs)情景下,全球总的年风能发电潜力会有较明显的差异.在温度上升最快的SSP5-8.5情景下,21世纪末的发电潜力比2020年代下降3.3％;在减排力度最高的情景SSP1-1.9下,全球年发电潜力会随着温度的下降而回升,有望在21世纪末回到21世纪初的发电潜力.与最优情景SSP1-1.9相比,其他SSPs情景下,大部分(61％)网格化的风功率密度表现出下降趋势,且温度上升越快,风功率密度值下降越多,未来可实现风能发电的潜力越小.

China’s goal to achieve carbon (C) neutrality by 2060 requires scaling up photovoltaic (PV) and wind power from 1 to 10–15 PWh year −1 (refs. 1 – 5 ). Following the historical rates of renewable installation 1 , a recent high-resolution energy-system model 6 and forecasts based on China’s 14th Five-year Energy Development (CFED) 7 , however, only indicate that the capacity will reach 5–9.5 PWh year −1 by 2060. Here we show that, by individually optimizing the deployment of 3,844 new utility-scale PV and wind power plants coordinated with ultra-high-voltage (UHV) transmission and energy storage and accounting for power-load flexibility and learning dynamics, the capacity of PV and wind power can be increased from 9 PWh year −1 (corresponding to the CFED path) to 15 PWh year −1 , accompanied by a reduction in the average abatement cost from US$97 to US$6 per tonne of carbon dioxide (tCO 2 ). To achieve this, annualized investment in PV and wind power should ramp up from US$77 billion in 2020 (current level) to US$127 billion in the 2020s and further to US$426 billion year −1 in the 2050s. The large-scale deployment of PV and wind power increases income for residents in the poorest regions as co-benefits. Our results highlight the importance of upgrading power systems by building energy storage, expanding transmission capacity and adjusting power load at the demand side to reduce the economic cost of deploying PV and wind power to achieve carbon neutrality in China.

秸秆露天焚烧是重要的大气污染来源之一.河北是农业大省,研究其秸秆露天焚烧排放特征及其对京津冀地区空气质量影响具有重要意义.研究建立了日尺度、1 km×1 km分辨率的2014年河北秸秆露天焚烧排放清单,分析了其时空分布特征;进一步综合利用WRF-CAMx-PSAT模式、排放清单与卫星火点数据,对2014年6月和10月河北秸秆露天焚烧排放的空气质量贡献进行了模拟分析研究.研究结果显示,2014年河北秸秆露天焚烧排放SO2、NOx、PM10、PM2.5、NMVOC、NH3、CO、EC、OC、CO2和CH4分别为0.3、1.9、5.1、5.0、4.6、0.3、27.3、0.2、2.1、685.3和2.0万t;玉米和小麦是各污染物排放的主要贡献者,两者对不同污染物排放的贡献占比分别为46.6％ ～71.4％和20.5％ ～47.8％;月尺度排放呈现双峰现象,分别为6月与10月,其中6月排放量最高,占全年总排放的26.9％;日排放波动较大,PM2.5峰值出现在6月末(0.3万t)和11月初(0.2万t).空气质量影响方面,2014年6月和10月河北秸秆露天焚烧排放对京津冀地区PM2.5、EC、SO2和NO2的月均浓度贡献占比分别为4.2％和5.5％、2.6％和2.8％、0.4％和0.5％、2.5％和1.9％,除NO2外,其他污染物贡献浓度10月占比均高于6月;受秸秆焚烧特点影响,日尺度的贡献呈现明显波动变化特征,河北的秸秆露天焚烧对北京、天津和河北的PM2.5日均浓度贡献占比最高分别可达25.7％、23.1％和21.0％.

The potential of mitigation actions to limit global warming within 2 °C (ref. 1 ) might rely on the abundant supply of biomass for large-scale bioenergy with carbon capture and storage (BECCS) that is assumed to scale up markedly in the future 2 – 5 . However, the detrimental effects of climate change on crop yields may reduce the capacity of BECCS and threaten food security 6 – 8 , thus creating an unrecognized positive feedback loop on global warming. We quantified the strength of this feedback by implementing the responses of crop yields to increases in growing-season temperature, atmospheric CO 2 concentration and intensity of nitrogen (N) fertilization in a compact Earth system model 9 . Exceeding a threshold of climate change would cause transformative changes in social–ecological systems by jeopardizing climate stability and threatening food security. If global mitigation alongside large-scale BECCS is delayed to 2060 when global warming exceeds about 2.5 °C, then the yields of agricultural residues for BECCS would be too low to meet the Paris goal of 2 °C by 2200. This risk of failure is amplified by the sustained demand for food, leading to an expansion of cropland or intensification of N fertilization to compensate for climate-induced yield losses. Our findings thereby reinforce the urgency of early mitigation, preferably by 2040, to avoid irreversible climate change and serious food crises unless other negative-emission technologies become available in the near future to compensate for the reduced capacity of BECCS.

Quantifying the threat that climate change poses to fine particle (PM2.5) pollution is hampered by large uncertainties in the relationship between PM2.5 and meteorology. To constrain the impact of climate change on PM2.5, statistical models are often employed in a different manner than physical-chemical models to reduce the requirement of input data. A majority of statistical models predict PM2.5 concentration (often log-transformed) as a simple function of meteorology, which could be biased due to the conversion of precursor gases to PM2.5. We reduced this bias by developing a unique statistic model where the sum of PM2.5 and the weighted precursor gases, rather than the PM2.5 alone, was predicted as a function of meteorology and a proxy of primary emissions, where the input data of PM10, CO, O3, NOx, and SO2 were obtained from routine measurements. This modification, without losing the simplicity of statistical models, reduced the mean-square error from 27 to 17% and increased the coefficient of determination from 47 to 67% in the model cross-validation using daily PM2.5 observations during 2013-2018 for 74 cities over China. We found a previously unrecognized mechanism that synoptic climate change in the past half-century might have increased low quantiles of PM2.5 more strenuously than the upper quantiles in large cities over China. Climate change during 1971-2018 was projected to increase the annual mean concentration of PM2.5 at a degree that could be comparable with the toughest-ever clean air policy during 2013-2018 had counteracted it, as inferred from the decline in the daily concentration of carbon monoxide as an inert gas. Our estimate of the impact of climate change on PM2.5 is higher than previous statistical models, suggesting that aerosol chemistry might play a more important role than previously thought in the interaction between climate change and air pollution. Our result indicated that air quality might degrade if the future synoptic climate change could continue interacting with aerosol chemistry as it had occurred in the past half-century.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.

As China ramped-up coal power capacities rapidly while CO 2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China.

Lockdown measures are essential to containing the spread of coronavirus disease 2019 (COVID-19), but they will slow down economic growth by reducing industrial and commercial activities. However, the benefits of activity control from containing the pandemic have not been examined and assessed. Here we use daily carbon dioxide (CO2) emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide (NO2) measured by the Ozone Monitoring Instrument (OMI) as an indicator for reduced activities consecutive to a lockdown. We perform a correlation analysis to show that a 1% day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO2 emissions of 0.22% ± 0.02% using statistical data for energy consumption relative to emissions without COVID-19, or 0.20% ± 0.02% using satellite data for atmospheric column NO2. We estimate that swift action in China is effective in limiting the number of COVID-19 cases <100,000 with a reduction in CO2 emissions of up to 23% by the end of February 2020, whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same goal. By analyzing the costs of health care and fatalities, we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic product. Our findings suggest an unprecedentedly high cost of maintaining activities and CO2 emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.

Domestic biomass burning (DBB) influences both indoor and outdoor air quality due to the multiple pollutants released during incomplete and inefficient combustion. The emissions are not well quantified because of insufficient information, which were the key parameters related to fuel consumption estimation, such as province- and year-specific percentage of domestic straw burning (Pstraw) and firewood consumption (Fc). In this study, we established the quantitative relationship between rural-related socioeconomic parameters (e.g., rural per-capita income and rural Engel's coefficient) and Pstraw/Fc. DBB emissions, including 12 crop straw types and firewood for 12 kinds of pollutants in China during the period 1995-2014, were estimated based on fuel-specific emission factors and detailed fuel consumption data. The results revealed that the national emissions generally increased initially and then decreased with the turning point around 2007-2008. Firewood burning was the major source of the NH3 and BC emissions; straw burning contributed more to SO2, NMVOC, CO, OC, and CH4 emissions; while the major contributor changed from firewood to domestic straw burning for NOx, PM10, PM2.5, CO2, and Hg emissions. The emission trends varied among the 31 provinces. The major agricultural regions of north-eastern, central, and south-western China were always characterized by high emissions. The spatial variation mainly occurred in the northeast and north China (increase), and central-south and coastal regions of China (decrease).

Agricultural machinery is an important non-road mobile source, which can exhaust multi-pollutants, making primary and secondary contributions to the air pollution. China is a significant agricultural country of the world; however, the agricultural machinery emissions research is at an early stage, and an emission inventory with a high temporal-spatial resolution is still needed. In this study, a comprehensive emission inventory with a high temporal-spatial resolution for agricultural machinery in China was first developed. The results showed that the total emissions in 2014 were 262.69 Gg, 249.25 Gg, 121139 Gg, 2192.05 Gg, 1448.16 Gg and 25.14 Gg for PM10, PM25, THC, NOx, CO and SO2, respectively. Tractors and farm transport vehicles were the top two greatest contributors, accounting for approximately 39.9%-53.6% and 17.4%-24.6%, respectively, of the total emissions of the five pollutants (except THC). The farm transport vehicles contributed the most (81.8%) to the THC emissions. The county-level emissions were further allocated into 1 km x 1 km grids according to source-specific allocation surrogates. The spatial characteristic analysis indicated that high emissions were distributed in northeast, north and central south China. To obtain a high temporal resolution emission inventory, a comprehensive investigation on the agricultural practice timing in different provinces was conducted. Then, the annual emissions in the different provinces were distributed to a spatial resolution of ten-day periods (i.e. the early, mid- and late ten-day periods in each month). It was found that higher emissions in China occurred in late April, mid-June and early October. In addition, the emission uncertainty was also analyzed based on the Monte Carlo simulation. The estimated high temporal-spatial resolution emission inventory could provide important basic information for environmental/climate implications research, emission control policy making, and air quality modeling. (C) 2018 Elsevier Ltd. All rights reserved.

Many studies have been conducted focusing on the contribution of land emission sources to PM2.5 in China; however, little attention had been paid to other contributions, especially the secondary contributions from shipping emissions to atmospheric PM2.5. In this study, a combined source apportionment approach, including principle component analysis (PCA) and WRF-CMAQ simulation, was applied to identify both primary and secondary contributions from ships to atmospheric PM2.5. An intensive PM2.5 observation was conducted from April 2014 to January 2015 in Qinhuangdao, which was close to the largest energy output port of China. The chemical components analysis results showed that the primary component was the major contributor to PM2.5, with proportions of 48.3%, 48.9%, 55.1% and 55.4% in spring, summer, autumn and winter, respectively. The secondary component contributed higher fractions in summer (48.2%) and winter (36.8%), but had lower percentages in spring (30.1%) and autumn (32.7%). The hybrid source apportionment results indicated that the secondary contribution (SC) of shipping emissions to PM2.5 could not be ignored. The annual average SC was 2.7%, which was comparable to the primary contribution (2.9%). The SC was higher in summer (5.3%), but lower in winter (1.1%). The primary contributions to atmospheric PM2.5 were 3.0%, 2.5%, 3.4% and 2.7% in spring, summer, autumn and winter, respectively. As for the detailed chemical components, the contributions of shipping emissions were 2.3%, 0.5%, 0.1%, 1.0%, 1.7% and 0.1% to elements & sea salt, primary organic aerosol (POA), element carbon (EC), nitrate, sulfate and secondary organic carbon (SOA), respectively. The results of this study will further the understanding of the implications of shipping emissions in PM2.5 pollution.

Biomass burning injects many different gases and aerosols into the atmosphere that could have a harmful effect on air quality, climate, and human health. In this study, a comprehensive biomass burning emission inventory including domestic and in-field straw burning, firewood burning, livestock excrement burning, and forest and grassland fires is presented, which was developed for mainland China in 2012 based on county-level activity data, satellite data, and updated source-specific emission factors (EFs). The emission inventory within a 1 × 1 km2 grid was generated using geographical information system (GIS) technology according to source-based spatial surrogates. A range of key information related to emission estimation (e.g. province-specific proportion of domestic and in-field straw burning, detailed firewood burning quantities, uneven temporal distribution coefficient) was obtained from field investigation, systematic combing of the latest research, and regression analysis of statistical data. The established emission inventory includes the major precursors of complex pollution, greenhouse gases, and heavy metal released from biomass burning. The results show that the emissions of SO2, NOx, PM10, PM2.5, NMVOC, NH3, CO, EC, OC, CO2, CH4, and Hg in 2012 are 336.8 Gg, 990.7 Gg, 3728.3 Gg, 3526.7 Gg, 3474.2 Gg, 401.2 Gg, 34 380.4 Gg, 369.7 Gg, 1189.5 Gg, 675 299.0 Gg, 2092.4 Gg, and 4.12 Mg, respectively. Domestic straw burning, in-field straw burning, and firewood burning are identified as the dominant biomass burning sources. The largest contributing source is different for various pollutants. Domestic straw burning is the largest source of biomass burning emissions for all the pollutants considered, except for NH3, EC (firewood), and NOx (in-field straw). Corn, rice, and wheat represent the major crop straws. The combined emission of these three straw types accounts for 80 % of the total straw-burned emissions for each specific pollutant mentioned in this study. As for the straw burning emission of various crops, corn straw burning has the largest contribution to all of the pollutants considered, except for CH4; rice straw burning has highest contribution to CH4 and the second largest contribution to other pollutants, except for SO2, OC, and Hg; wheat straw burning is the second largest contributor to SO2, OC, and Hg and the third largest contributor to other pollutants. Heilongjiang, Shandong, and Henan provinces located in the north-eastern and central-southern regions of China have higher emissions compared to other provinces in China. Gridded emissions, which were obtained through spatial allocation based on the gridded rural population and fire point data from emission inventories at county resolution, could better represent the actual situation. High biomass burning emissions are concentrated in the areas with more agricultural and rural activity. The months of April, May, June, and October account for 65 % of emissions from in-field crop residue burning, while, regarding EC, the emissions in January, February, October, November, and December are relatively higher than other months due to biomass domestic burning in heating season. There are regional differences in the monthly variations of emissions due to the diversity of main planted crops and climatic conditions. Furthermore, PM2.5 component results showed that OC, Cl−, EC, K+, NH4+, elemental K, and SO42− are the main PM2.5 species, accounting for 80 % of the total emissions. The species with relatively high contribution to NMVOC emission include ethylene, propylene, toluene, mp-xylene, and ethyl benzene, which are key species for the formation of secondary air pollution. The detailed biomass burning emission inventory developed by this study could provide useful information for air-quality modelling and could support the development of appropriate pollution-control strategies.