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.
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.
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.
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.
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.
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.
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.
The Harvard-China Project adopted an Open Access policy in September 2017. Journal articles that are already made open access by the publishers are available on our publications page as PDF attachments, while the final manuscripts of other articles published since our adoption of the policy are available in the Harvard University open-access repository, DASH, under the Harvard-China Project collection. There is usually a six-month delay after the article is published before its manuscript is uploaded to DASH.