Climate Change & Air Pollution

Reduction of methane emissions (CH4) plays an important role in addressing global climate change. Most previous studies have focused on the direct CH4 emissions of economies, but overlooked the upstream CH4 emissions along global supply chains induced by the final consumption of economies. Using a global multi-regional input-output analysis, this study aims to explore the evolution of CH4 emissions embodied in international trade and final consumption in major economies during 2000–2012. The results show that China, the EU, USA, India and Brazil were the top five economies with high volumes of consumption-based CH4 emissions from 2000 to 2012. In particular, China’s consumption-based CH4 emissions showed an observable growth trend, while the EU, the USA and Japan showed a downward trend. It’s estimated that growing amounts of CH4 emissions (i.e., the volume increase from 77.1 Mt in 2000 to 95.9 Mt in 2012) were transferred globally via international trade, primarily as exports from China, Russia and other large developing economies to consumers in major developed economies. Russia–EU, China–USA and China–EU formed the main bilateral trading pairs of embodied emission flows. Further analysis found that per capita consumption-based CH4 emissions was closely related to their per capita GDP. Quantifying the CH4 emissions embodied in trade and final demand of major economies can provide important basis for understanding economy-wide emission drivers to design global and regional CH4 reduction scheme from a consumer perspective.

Haze days induced by aerosol pollution in North and East China have posed a persistent and growing problem over the past few decades. These events are particularly threatening to densely-populated cities such as Beijing. While the sources of this pollution are predominantly anthropogenic, natural climate variations may also play a role in allowing for atmospheric conditions conducive to formation of severe haze episodes over populated areas. Here, an investigation is conducted into the effects of changes in global dynamics and emissions on air quality in China’s polluted regions using 35 simulations developed from the Community Earth Systems Model Large Ensemble (CESM LENS) run over the period 1920-2100. It is shown that internal variability significantly modulates aerosol optical depth (AOD) over China; it takes roughly a decade for the forced response to balance the effects from internal variability even in China’s most polluted regions. Random forest regressions are used to accurately model (R² > 0.9) wintertime AOD using just climate oscillations, the month of the year and emissions. How different phases of each oscillation affect aerosol loading are projected using these regressions. AOD responses are identified for each oscillation, with particularly strong responses from El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). As ENSO can be projected a few months in advance and improvements in linear inverse modelling (LIM) may yield a similar predictability for the PDO, results of this study offer opportunities to improve the predictability of China’s severe wintertime haze events, and to inform policy options that could mitigate subsequent health impacts.

The chemical mechanisms responsible for rapid sulfate production, an important driver of winter haze formation in northern China, remain unclear. Here, we propose a potentially important heterogeneous hydroxymethanesulfonate (HMS) chemical mechanism. Through analyzing field measurements with aerosol mass spectrometry, we show evidence for a possible significant existence in haze aerosols of organosulfur primarily as HMS, misidentified as sulfate in previous observations. We estimate that HMS can account for up to about one-third of the sulfate concentrations unexplained by current air quality models. Heterogeneous production of HMS by SO₂ and formaldehyde is favored under northern China winter haze conditions due to high aerosol water content, moderately acidic pH values, high gaseous precursor levels, and low temperature. These analyses identify an unappreciated importance of formaldehyde in secondary aerosol formation and call for more research on sources and on the chemistry of formaldehyde in northern China winter.

This study aims to quantify global CH4 and N2O footprints in 2012 in terms of 181 economies and 20 world regions based on the official national emission accounts from the UNFCCC database and the global multi-region input-output accounts from the EORA database. Global total CH4 and N2O emissions increased by 15.0% in 2012 compared to 1990, mainly driven by increasing demands of agricultural and energy products. Mainland China, Western Europe, the USA, Southeast Asia and Sub-Saharan Africa were identified as the largest five CH4 footprint contributors (55.6% of the global total), while Mainland China, the USA, Western Europe, Brazil and Sub-Saharan Africa as the largest N2O footprint contributors (59.2% of the global total). In most developed economies, the CH4 and N2O footprints were much higher than their emissions on the production side, but opposite picture is observed in emerging economies. 36.4% and 24.8% of the global CH4 and N2O emissions in 2012 were associated with international trade, respectively. Substantial energy-related CH4 and agricultural CH4 and N2O emissions were transferred from developed countries to developing countries. Several nations within Kyoto targets have reduced their CH4 and N2O emissions significantly between 1990 and 2012, but the generally-believed success is challenged when viewing from the consumption side. Mainland China, Southeast Asia, Sub-Saharan Africa, Brazil, Middle East and India witnessed prominent increase of CH4 and N2O footprints in the same period. The structure and spatial patterns of global CH4 and N2O footprints shed light on the role of consumption-side actions and international cooperation for future non-CO2 GHG emission reduction.

As China makes every effort to control air pollution, India emerges as the world’s most polluted country, receiving worldwide attention with frequent winter (boreal) haze extremes. In this study, we found that the interannual variability of wintertime aerosol pollution over northern India is regulated mainly by a combination of El Niño and the Antarctic Oscillation (AAO). Both El Niño sea surface temperature (SST) anomalies and AAO-induced Indian Ocean Meridional Dipole SST anomalies can persist from autumn to winter, offering prospects for a prewinter forecast of wintertime aerosol pollution over northern India. We constructed a multivariable regression model incorporating El Niño and AAO indices for autumn to predict wintertime AOD. The prediction exhibits a high degree of consistency with observation, with a correlation coefficient of 0.78 (P < 0.01). This statistical model could allow the Indian government to forecast aerosol pollution conditions in winter and accordingly improve plans for pollution control.

During recent years, aggressive air pollution mitigation measures in northern China have resulted in considerable changes in gas and aerosol chemical composition. But it is unclear whether aerosol water content and acidity respond to these changes. The two parameters have been shown to affect heterogeneous production of winter haze aerosols. Here, we performed thermodynamic equilibrium modeling using chemical and meteorological data observed in urban Beijing for four recent winter seasons and quantified the changes in the mass growth factor and pH of inorganic aerosols. We focused on high relative humidity (>60%) conditions when submicron particles have been shown to be in the liquid state. From 2014/2015 to 2018/2019, the modeled mass growth factor decreased by about 9%–17% due to changes in aerosol compositions (more nitrate and less sulfate and chloride), and the modeled pH increased by about 0.3–0.4 unit mainly due to rising ammonia. A buffer equation is derived from semivolatile ammonia partitioning, which helps understand the sensitivity of pH to meteorological and chemical variables. The findings provide implications for evaluating the potential chemical feedback in secondary aerosol production and the effectiveness of ammonia control as a measure to alleviate winter haze.

Reducing CH4 emissions is a major global challenge, owing to the world-wide rise in emissions and concentration of CH4 in the atmosphere, especially in the past decade. China has been the greatest contributor to global anthropogenic CH4 emissions for a long time, but current understanding towards its growing emissions is insufficient. This paper aims to link China's CH4 emissions during 2005–2012 to their socioeconomic determinants by combining input-output models with structural decomposition analysis from both the consumption and income perspectives. Results show that changes in household consumption and income were the leading drivers of the CH4 growth in China, while changes in efficiency remained the strongest factor offsetting CH4 emissions. After 2007, with the global financial crisis and economic stimulus plans, embodied emissions from exports plunged but those from capital formation increased rapidly. The enabled emissions in employee compensation increased steadily over time, whereas emissions induced from firms' net surplus decreased gradually, reflecting the reform on income distribution. In addition, at the sectoral level, consumption and capital formation respectively were the greatest drivers of embodied CH4 emission changes from agriculture and manufacturing, while employee compensation largely determined the enabled emission changes across all industrial sectors. The growth of CH4 emissions in China was profoundly affected by the macroeconomic situation and the changes of economic structure. Examining economic drivers of anthropogenic CH4emissions can help formulate comprehensive mitigation policies and actions associated with economic production, supply and consumption.

Accurately quantifying the spatiotemporal distribution of the biological component of CO₂ surface–atmosphere exchange is necessary to improve top-down constraints on China's anthropogenic CO₂ emissions. We provide hourly fluxes of CO₂ as net ecosystem exchange (NEE; µmol CO₂ m⁻² s⁻¹) on a 0.25&#x2218;&#xD7;0.25&#x2218;" id="MathJax-Element-1-Frame" role="presentation" style="position: relative;" tabindex="0">0.25∘×0.25∘ grid by adapting the Vegetation, Photosynthesis, and Respiration Model (VPRM) to the eastern half of China for the time period from 2005 to 2009; the minimal empirical parameterization of the VPRM-CHINA makes it well suited for inverse modeling approaches. This study diverges from previous VPRM applications in that it is applied at a large scale to China's ecosystems for the first time, incorporating a novel processing framework not previously applied to existing VPRM versions. In addition, the VPRM-CHINA model prescribes methods for addressing dual-cropping regions that have two separate growing-season modes applied to the same model grid cell. We evaluate the VPRM-CHINA performance during the growing season and compare to other biospheric models. We calibrate the VPRM-CHINA with ChinaFlux and FluxNet data and scale up regionally using Weather Research and Forecasting (WRF) Model v3.6.1 meteorology and MODIS surface reflectances. When combined with an anthropogenic emissions model in a Lagrangian particle transport framework, we compare the ability of VPRM-CHINA relative to an ensemble mean of global hourly flux models (NASA CMS – Carbon Monitoring System) to reproduce observations made at a site in northern China. The measurements are heavily influenced by the northern China administrative region. Modeled hourly time series using vegetation fluxes prescribed by VPRM-CHINA exhibit low bias relative to measurements during the May–September growing season. Compared to NASA CMS subset over the study region, VPRM-CHINA agrees significantly better with measurements. NASA CMS consistently underestimates regional uptake in the growing season. We find that during the peak growing season, when the heavily cropped North China Plain significantly influences measurements, VPRM-CHINA models a CO₂ uptake signal comparable in magnitude to the modeled anthropogenic signal. In addition to demonstrating efficacy as a low-bias prior for top-down CO₂ inventory optimization studies using ground-based measurements, high spatiotemporal resolution models such as the VPRM are critical for interpreting retrievals from global CO₂ remote-sensing platforms such as OCO-2 and OCO-3 (planned). Depending on the satellite time of day and season of crossover, efforts to interpret the relative contribution of the vegetation and anthropogenic components to the measured signal are critical in key emitting regions such as northern China – where the magnitude of the vegetation CO₂ signal is shown to be equivalent to the anthropogenic signal.

Global anthropogenic CH₄ emissions have witnessed a rapid increase in the last decade. However, how this increase is connected with its socioeconomic drivers has not yet been explored. In this paper, we highlight the impacts of final demand and international trade on global anthropogenic CH₄ emissions based on the consumption‐based accounting principle. We find that household consumption was the largest final demand category, followed by fixed capital formation and government consumption. The position and function of nations and major economies to act on the structure and spatial patterns of global CH₄ emissions were systematically clarified. Substantial geographic shifts of CH₄emissions during 2000‐2012 revealed the prominent impact of international trade. In 2012, about half of global CH₄ emissions were embodied in international trade, of which 77.8% were from intermediate trade and 22.2% from final trade. Mainland China was the largest exporter of embodied CH₄ emissions, while the USA was the largest importer. Developed economies such as Western Europe, the USA and Japan were major net receivers of embodied emission transfer, mainly from developing countries. CH₄emission footprints of nations were closely related to their human development indexes (HDIs) and per capita gross domestic products (GDPs). Our findings could help to improve current understanding of global anthropogenic CH₄ emission increases, and to pinpoint regional and sectoral hotspots for possible emission mitigation in the entire supply chains from production to consumption.

 

PM 2.5 during severe winter haze in Beijing, China, has reached levels as high as 880μg/m³, with sulfur compounds contributing significantly to PM 2.5 composition. This sulfur has been traditionally assumed to be sulfate, although atmospheric chemistry models are unable to account for such large sulfate enhancements under dim winter conditions. Using a 1-D model, we show that well-characterized but previously overlooked chemistry of aqueous-phase HCHO and S(IV) in cloud droplets to form a S(IV)-HCHO adduct, hydroxymethane sulfonate, may explain high particulate sulfur in wintertime Beijing. We also demonstrate in the laboratory that methods of ion chromatography typically used to measure ambient particulates easily misinterpret hydroxymethane sulfonate as sulfate. Our findings suggest that HCHO and not SO2 has been the limiting factor in many haze events in Beijing and that to reduce severe winter pollution in this region, policymakers may need to address HCHO sources such as transportation.

 

pH is an important property of aerosol particles but is difficult to measure directly. Several studies have estimated the pH values for fine particles in North China winter haze using thermodynamic models (i.e., E-AIM and ISORROPIA) and ambient measurements. The reported pH values differ widely, ranging from close to 0 (highly acidic) to as high as 7 (neutral). In order to understand the reason for this discrepancy, we calculated pH values using these models with different assumptions with regard to model inputs and particle phase states. We find that the large discrepancy is due primarily to differences in the model assumptions adopted in previous studies. Calculations using only aerosol phase composition as inputs (i.e., reverse mode) are sensitive to the measurement errors of ionic species and inferred pH values exhibit a bimodal distribution with peaks between −2 and 2 and between 7 and 10. Calculations using total (gas plus aerosol phase) measurements as inputs (i.e., forward mode) are affected much less by the measurement errors, and results are thus superior to those obtained from the reverse mode calculations. Forward mode calculations in this and previous studies collectively indicate a moderately acidic condition (pH from about 4 to about 5) for fine particles in North China winter haze, indicating further that ammonia plays an important role in determining this property. The differences in pH predicted by the forward mode E-AIM and ISORROPIA calculations may be attributed mainly to differences in estimates of activity coefficients for hydrogen ions. The phase state assumed, which can be either stable (solid plus liquid) or metastable (only liquid), does not significantly impact pH predictions of ISORROPIA.

Among biomass energy technologies which are treated as the promising way to mitigate critical energy crisis and global climate change, biomass gasification plays a key role given to its gaseous fuels especially syngas for distributed power plant. However, a system analysis for the energy saving and greenhouse gas emissions abatement potentials of gasification system has been directed few attentions. This study presents a system analysis that combines process and input-output analyses of GHG emissions and energy costs throughout the full chain of activities associated with biomass gasification. Incorporating agricultural production, industrial process and wastewater treatment which is always ignored, the energy inputs in life cycle are accounted for the first commercial biomass gasification power plant in China. Results show that the non-renewable energy cost and GHG emission intensity of the biomass gasification system are 0.163 MJ/MJ and 0.137 kg CO2-eq/MJ respectively, which reaffirm its advantages over coal-fired power plants in clean energy and environmental terms. Compared with other biomass energy processes, gasification performs well as its non-renewable energy cost and CO2 intensity are in the central ranges of those for all of these technologies. Construction of the plant is an important factor in the process’s non-renewable energy consumption, contributing about 44.48% of total energy use. Wastewater treatment is the main contributor to GHG emissions. The biomass gasification and associated wastewater treatment technologies have critical influence on the sustainability and renewability of biomass gasification. The results provide comprehensive analysis for biomass gasification performance and technology improvement potential in regulating biomass development policies for aiming to achieve sustainability globally.

Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM≤2.5 micrometers in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks.

 

Reliable inventory information is critical in informing emission mitigation efforts. Using the latest officially released emission data, which is production based, we take a consumption perspective to estimate the non-CO₂ greenhouse gas (GHG) emissions for China in 2012. The non-CO₂ GHG emissions, which cover CH₄, N₂O, HFCs, PFCs, and SF₆, amounted to 2003.0 Mt. CO₂-eq (including 1871.9 Mt. CO₂-eq from economic activities), much larger than the total CO₂ emissions in some developed countries. Urban consumption (30.1%), capital formation (28.2%), and exports (20.6%) derived approximately four fifths of the total embodied emissions in final demand. Furthermore, the results from structural path analysis help identify critical embodied emission paths and key economic sectors in supply chains for mitigating non-CO₂ GHG emissions in Chinese economic systems. The top 20 paths were responsible for half of the national total embodied emissions. Several industrial sectors such as Construction, Production and Supply of Electricity and Steam, Manufacture of Food and Tobacco and Manufacture of Chemicals, and Chemical Products played as the important transmission channels. Examining both production- and consumption-based non-CO₂ GHG emissions will enrich our understanding of the influences of industrial positions, final consumption demands, and trades on national non-CO₂ GHG emissions by considering the comprehensive abatement potentials in the supply chains.

The Indian government has set an ambitious target for future renewable power generation, including 60 GW of cumulative wind power capacity by 2022. However, the benefits of these substantial investments are vulnerable to the changing climate. On the basis of hourly wind data from an assimilated meteorology reanalysis dataset covering the 1980–2016 period, we show that wind power potential may have declined secularly over this interval, particularly in western India. Surface temperature data confirm that significant warming occurred in the Indian Ocean over the study period, leading to modulation of high pressure over the ocean. A multivariable linear regression model incorporating the pressure gradient between the Indian Ocean and the Indian subcontinent can account for the interannual variability of wind power. A series of numerical sensitivity experiments confirm that warming in the Indian Ocean contributes to subsidence and dampening of upward motion over the Indian continent, resulting potentially in weakening of the monsoonal circulation and wind speeds over India.

Recent studies show that international trade affects global distributions of air pollution andpublic health. Domestic interprovincial trade has similar effects within countries, but has notbeen comprehensively investigated previously. Here we link four models to evaluate theeffects of both international exports and interprovincial trade on PM2.5pollution and publichealth across China. We show that 50–60% of China’s air pollutant emissions in 2007 wereassociated with goods and services consumed outside of the provinces where they wereproduced. Of an estimated 1.10 million premature deaths caused by PM2.5pollutionthroughout China, nearly 19% (208,500 deaths) are attributable to international exports. Incontrast, interprovincial trade leads to improved air quality in developed coastal provinceswith a net effect of 78,500 avoided deaths nationwide. However, both international exportand interprovincial trade exacerbate the health burdens of air pollution in China’s lessdeveloped interior provinces. Our results reveal trade to be a critical but largely overlookedconsideration in effective regional air quality planning for China.

China hosts the world’s largest market for wind-generated electricity. The financial return and carbon reduction benefits from wind power are sensitive to changing wind resources. Wind data derived from an assimilated meteorological database are used here to estimate what the wind generated electricity in China would have been on an hourly basis over the period 1979 to 2015 at a geographical resolution of approximately 50 km × 50 km. The analysis indicates a secular decrease in generating potential over this interval, with the largest declines observed for western Inner Mongolia (15 ± 7%) and the northern part of Gansu (17 ± 8%), two leading wind investment areas. The decrease is associated with long-term warming in the vicinity of the Siberian High (SH), correlated also with the observed secular increase in global average surface temperatures. The long-term trend is modulated by variability relating to the Pacific Decadal Oscillation (PDO) and the Arctic Oscillation (AO). A linear regression model incorporating indices for the PDO and AO, as well as the declining trend, can account for the interannual variability of wind power, suggesting that advances in long-term forecasting could be exploited to markedly improve management of future energy systems.

To evaluate the effectiveness of national air pollution control policies, the emissions of SO₂, NO_X, CO and CO₂ in China are estimated using bottom-up methods for the most recent 15-year period (2000–2014). Vertical column densities (VCDs) from satellite observations are used to test the temporal and spatial patterns of emissions and to explore the ambient levels of gaseous pollutants across the country. The inter-annual trends in emissions and VCDs match well except for SO₂. Such comparison is improved with an optimistic assumption in emission estimation that the emission standards for given industrial sources issued after 2010 have been fully enforced. Underestimation of emission abatement and enhanced atmospheric oxidization likely contribute to the discrepancy between SO₂ emissions and VCDs. As suggested by VCDs and emissions estimated under the assumption of full implementation of emission standards, the control of SO₂ in the 12th Five-Year Plan period (12th FYP, 2011–2015) is estimated to be more effective than that in the 11th FYP period (2006–2010), attributed to improved use of flue gas desulfurization in the power sector and implementation of new emission standards in key industrial sources. The opposite was true for CO, as energy efficiency improved more significantly from 2005 to 2010 due to closures of small industrial plants. Iron & steel production is estimated to have had particularly strong influence on temporal and spatial patterns of CO. In contrast to fast growth before 2011 driven by increased coal consumption and limited controls, NO_X emissions decreased from 2011 to 2014 due to the penetration of selective catalytic/non-catalytic reduction systems in the power sector. This led to reduced NO₂ VCDs, particularly in relatively highly polluted areas such as the eastern China and Pearl River Delta regions. In developed areas, transportation is playing an increasingly important role in air pollution, as suggested by the increased ratio of NO₂ to SO₂ VCDs. For air quality in mega cities, the inter-annual trends in emissions and VCDs indicate that surrounding areas are more influential in NO₂ level for Beijing than those for Shanghai.