# Air Pollution, Greenhouse Gases & Climate

In Press
Archana Dayalu, J. William Munger, Yuxuan Wang, Steven C. Wofsy, Yu Zhao, Thomas Nehrkorn, Chris P. Nielsen, Michael B. McElroy, and Rachel Chang. In Press. “Carbon dioxide emissions in Northern China based on atmospheric observations from 2005 to 2009.” Atmospheric Chemistry and Physics. Publisher's Version
2020
Jialin Liu, Fangyan Cheng, J. William Munger, Timothy G. Whitby, Peng Jiang, Siyue Chen, Weiwen Ji, and Xiuling Man. 2020. “Precipitation extremes influence patterns and partitioning of evapotranspiration and transpiration in a deciduous boreal larch forest.” Agricultural and Forest Meteorology, 287, 107936. Publisher's VersionAbstract
Ecosystems at the margins of their zone could be amongst the first to experience significant shifts in structure and function. At this site there have already been signs of permafrost degradation and more frequent temperature and precipitation anomalies. The canopy-dominant larch accounted for half the total T fluxes. The remaining 50% was distributed evenly among intermediate and suppressed trees. T is the dominant subcomponent in ET, where overall T/ET varies of 66%–84% depending on precipitation patterns. In dormant and early growing seasons, T still constitutes a majority of ET even though the canopy foliage is not fully developed because cold soil creates a negative soil to air vapor pressure gradient that impedes evaporation. However, in the peak growing season, excess precipitation reduces T while providing sufficient wetness for surface evaporation. ET from standard data product based on MODIS satellite reflectance underestimates tower ET by 17%–29%. Solar-induced chlorophyll fluorescence measured by satellite is well correlated with tower ET (r2 = 0.69–0.73) and could provide a better basis for regional ET extrapolations. Sites along boreal ecotones are critical to observe for signs of shifts in their structure, function, and response to climate anomalies.
2019
Mengyao Han, Bo Zhang, Yuqing Zhang, and Chenghe Guan. 2019. “Agricultural CH4 and N2O emissions of major economies: Consumption- vs. production-based perspectives.” Journal of Cleaner Production, 210, Pp. 276-286. Publisher's VersionAbstract

Agriculture is one of the most important sectors for global anthropogenic methane (CH4) and nitrous oxide (N2O) emissions. While much attention has been paid to production-side agricultural non-CO2 greenhouse gas (ANGHG) emissions, less is known about the emissions from the consumption-based perspective. This paper aims to explore the characteristics of agricultural CH4 and N2O emissions of global major economies by using the latest emission data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and the recently available global multi-regional input-output model from the World Input-Output Database (WIOD). The results show that in 2014, the 42 major economies together accounted for 60.7% and 65.0% of global total direct and embodied ANGHG emissions, respectively. The consumption-based ANGHG emissions in the US, Japan, and the EU were much higher than their production-based emissions, while the converse was true for Brazil, Australia, and India. The global-average embodied ANGHG emissions per capita was 0.7 t CO2-eq, but major developing countries such as China, India, Indonesia and Mexico were all below this average value. We find that the total transfer of embodied ANGHG emissions via international trade was 622.4 Mt CO2-eq, 11.9% of the global total. China was the largest exporter of embodied ANGHG emissions, while the US was the largest importer. Most developed economies were net importers of embodied emissions. Mexico-US, China-US, China-EU, China-Japan, China-Russia, Brazil-EU, India-EU and India-US formed the main bilateral trading pairs of embodied emission flows. Examining consumption-based inventories can be useful for understanding the impacts of final demand and international trade on agricultural GHG emissions and identifying appropriate mitigation potentials along global supply chains.

Yan Zhang, Xin Bo, Yu Zhao, and Chris P. Nielsen. 2019. “Benefits of current and future policies on emissions of China's coal-fired power sector indicated by continuous emission monitoring.” Environmental Pollution, 251, Pp. 415-424. Publisher's VersionAbstract
Emission inventories are critical to understanding the sources of air pollutants, but have high uncertainties in China due in part to insufficient on-site measurements. In this study, we developed a method of examining, screening and applying online data from the country's improving continuous emission monitoring systems (CEMS) to reevaluate a “bottom-up” emission inventory of China's coal-fired power sector. The benefits of China's current national emission standards and ultra-low emission policy for the sector were quantified assuming their full implementation. The derived national average emission factors of SO2, NOx and particulate matter (PM) were 1.00, 1.00 and 0.25 kg/t-coal respectively for 2015 based on CEMS data, smaller than those of previous studies that may not fully recognize improved emission controls in recent years. The annual emissions of SO2, NOx and PM from the sector were recalculated at 1321, 1430 and 334 Gg respectively, 75%, 63% and 76% smaller than our estimates based on a previous approach without the benefit of CEMS data. The results imply that online measurement with proper data screening can better track the recent progress of emission controls. The emission intensity (the ratio of emissions to economic output) of Northwest China was larger than that of other regions, attributed mainly to its less intensive economy and industry. Transmission of electricity to more-developed eastern provinces raised the energy consumption and emissions of less-developed regions. Judged by 95 percentiles of flue-gas concentrations measured by CEMS, most power plants met the current national emission standards in 2015 except for those in Northwest and Northeast China, while plants that met the ultra-low emission policy were much scarcer. National SO2, NOx and PM emissions would further decline by 68%, 55% and 81% respectively if the ultra-low emission policy can be strictly implemented, implying the great potential of the policy for emission abatement.
Jianxiong Sheng, Shaojie Song, Yuzhong Zhang, Ronald G. Prinn, and Greet Janssens-Maenhout. 2019. “Bottom-up estimates of coal mine methane emissions in China: A gridded inventory, emission factors, and trends.” Environmental Science and Technology Letters, 6 (8), Pp. 473-478. Publisher's VersionAbstract
China has large but uncertain coal mine methane (CMM) emissions. Inverse modeling (top-down) analyses of atmospheric methane observations can help improve the emission estimates but require reliable emission patterns as prior information. To serve this urgent need, we developed a high-resolution (0.25° × 0.25°) methane emission inventory for China’s coal mining using a recent publicly available database of more than 10000 coal mines in China for 2011. This number of coal mines is 25 and 2.5 times, respectively, more than the number available in the EDGAR v4.2 and EDGAR v4.3.2 gridded global inventories, which have been extensively used in past inverse analyses. Our inventory shows large differences with the EDGAR v4.2 as well as its more recent version, EDGAR v4.3.2. Our results suggest that China’s CMM emissions have been decreasing since 2012 on the basis of coal mining activities and assuming time-invariant emission factors but that regional trends differ greatly. Use of our inventory as prior information in future inverse modeling analyses can help better quantify CMM emissions as well as more confidently guide the future mitigation of coal to gas in China.
Haikun Wang, Xi Lu, Yu Deng, Yaoguang Sun, Chris P. Nielsen, Yifan Liu, Ge Zhu, Maoliang Bu, Jun Bi, and Michael B. McElroy. 2019. “China’s CO2 peak before 2030 implied from diverse characteristics and growth of cities.” Nature Sustainability, 2, Pp. 748–754. Publisher's VersionAbstract

China pledges to peak CO2 emissions by 2030 or sooner under the Paris Agreement to limit global warming to 2 °C or less by the end of the century. By examining CO2 emissions from 50 Chinese cities over the period 2000–2016, we found a close relationship between per capita emissions and per capita gross domestic product (GDP) for individual cities, following the environmental Kuznets curve, despite diverse trajectories for CO2 emissions across the cities. Results show that carbon emissions peak for most cities at a per capita GDP (in 2011 purchasing power parity) of around US$21,000 (80% confidence interval: US$19,000 to 22,000). Applying a Monte Carlo approach to simulate the peak of per capita emissions using a Kuznets function based on China’s historical emissions, we project that emissions for China should peak at 13–16 GtCO2 yr−1 between 2021 and 2025, approximately 5–10 yr ahead of the current Paris target of 2030. We show that the challenges faced by individual types of Chinese cities in realizing low-carbon development differ significantly depending on economic structure, urban form and geographical location.

Peng Jiang, Hongyan Liu, Shilong Piao, Philippe Ciais, Xiuchen Wu, Yi Yin, and Hongya Wang. 2019. “Enhanced growth after extreme wetness compensates for post-drought carbon loss in dry forests.” Nature Communications, 10, 195. Publisher's VersionAbstract
While many studies have reported that drought events have substantial negative legacy effects on forest growth, it remains unclear whether wetness events conversely have positive growth legacy effects. Here, we report pervasive and substantial growth enhancement after extreme wetness by examining tree radial growth at 1929 forest sites, satellite-derived vegetation greenness, and land surface model simulations. Enhanced growth after extreme wetness lasts for 1 to 5 years and compensates for 93 ± 8% of the growth deficit after extreme drought across global water-limited regions. Remarkable wetness-enhanced growths are observed in dry forests and gymnosperms, whereas the enhanced growths after extreme wetness are much smaller in wet forests and angiosperms. Limited or no enhanced growths are simulated by the land surface models after extreme wetness. These findings provide new evidence for improving climate-vegetation models to include the legacy effects of both drought and wet climate extremes.
Ying Wang, Bin Chen, Chenghe Guan, and Bo Zhang. 2019. “Evolution of methane emissions in global supply chains during 2000-2012.” Resources, Conservation and Recycling, 150, 104414. Publisher's VersionAbstract
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.
Peter Sherman, Meng Gao, Shaojie Song, Patrick Ohiomoba, Alex Archibald, and Michael B. McElroy. 2019. “The influence of dynamics and emissions changes on China’s wintertime haze.” Journal of Applied Meteorology and Climatology, 58, Pp. 1603-1611. Publisher's VersionAbstract

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.

S.J. Song, M. Gao, W.Q. Xu, Y.L. Sun, D.R. Worsnop, J.T. Jayne, Y.Z. Zhang, L. Zhu, M. Li, Z. Zhou, C.L. Cheng, Y.B. Lv, Y. Wang, W. Peng, X.B. Xu, N. Lin, Y.X. Wang, S.X. Wang, J. W. Munger, D. Jacob, and M.B. McElroy. 2019. “Possible heterogeneous hydroxymethanesulfonate (HMS) chemistry in northern China winter haze and implications for rapid sulfate formation.” Atmospheric Chemistry and Physics, 19, Pp. 1357-1371. Publisher's VersionAbstract
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.
Wenjie Tian, Xudong Wu, Rong Ma, and Bo Zhang. 2019. “Quantifying global CH4 and N2O footprints.” Journal of Environmental Management, 251, 109566. Publisher's VersionAbstract
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.
Meng Gao, Peter Sherman, Shaojie Song, Yueyue Yu, Zhiwei Wu, and Michael B. McElroy. 2019. “Seasonal prediction of Indian wintertime aerosol pollution using the Ocean Memory Effect.” Science Advances, 5, 7. Publisher's VersionAbstract
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.
Shaojie Song, Athanasios Nenes, Meng Gao, Yuzhong Zhang, Pengfei Liu, Jingyuan Shao, Dechao Ye, Weiqi Xu, Lu Lei, Yele Sun, Baoxian Liu, Shuxiao Wang, and Michael B. McElroy. 2019. “Thermodynamic modeling suggests declines in water uptake and acidity of inorganic aerosols in Beijing winter haze events during 2014/2015–2018/2019.” Environmental Science & Technology Letters, 6, Pp. 752-760. Publisher's VersionAbstract
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.
2018
Archana Dayalu, William Munger, Steven Wofsy, Yuxuan Wang, Thomas Nehrkorn, Yu Zhao, Michael McElroy, Chris Nielsen, and Kristina Luus. 2018. “Assessing biotic contributions to CO2 fluxes in northern China using the Vegetation, Photosynthesis and Respiration Model (VPRM-CHINA) and observations from 2005 to 2009.” Biogeosciences, 15, Pp. 6713-6729. Publisher's VersionAbstract
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.
Michael.B. McElroy. 2018. “Can China address air pollution and climate change?” In The China Questions: Critical Insights into a Rising Power, edited by Jennifer Rudolph and Michael Szonyi. Cambridge: Harvard University Press. Publisher's Version
Bo Zhang, Xueli Zhao, Xiaofang Wu, Mengyao Han, Chenghe Guan, and Shaojie Song. 2018. “Consumption‐based accounting of global anthropogenic CH4 emissions.” Earth's Future, 6, 9, Pp. 1349-1363. Publisher's VersionAbstract

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.

Jonathan M. Moch, Eleni Dovrou, Loretta J. Mickley, Frank N. Keutsch, Yuan Cheng, Daniel J. Jacob, Jingkun Jiang, Meng Li, J. William Munger, Xiaohui Qiao, and Qiang Zhang. 2018. “Contribution of hydroxymethane sulfonate to ambient particulate matter: A potential explanation for high particulate sulfur during severe winter haze in Beijing.” Geophysical Research Letters, 45, Pp. 11969-11979. Publisher's VersionAbstract

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.

Shaojie Song, Meng Gao, Weiqi Xu, Jingyuan Shao, Guoliang Shi, Shuxiao Wang, Yuxuan Wang, Yele Sun, and Michael McElroy. 2018. “Fine particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models.” Atmospheric Chemistry and Physics, 18, Pp. 7423-7438. Publisher's VersionAbstract
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.
Qing Yang, Hewen Zhou, Xiaoyan Zhang, Chris P. Nielsen, Jiashuo Li, Xi Lu, Haiping Yang, and Hanping Chen. 2018. “Hybrid life-cycle assessment for energy consumption and greenhouse gas emissions of a typical biomass gasification power plant in China.” Journal of Cleaner Production, 205, Pp. 661-671. Publisher's VersionAbstract

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.

Meng Gao, Gufran Beig, Shaojie Song, Hongliang Zhang, Jianlin Hu, Qi Ying, Fengchao Liang, Yang Liu, Haikun Wang, Xiao Lu, Tong Zhu, Gregory Carmichael, Chris P. Nielsen, and Michael B. McElroy. 2018. “The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India.” Environment International, 121, Part 1, Pp. 250-259. Publisher's VersionAbstract

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.