State Key Joint Laboratory of Environment Simulation and Pollution Control

BACKGROUND: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. RESULTS: Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). CONCLUSIONS: The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Abstract. To tackle the problem of severe air pollution, China has implemented active clean air policies in recent years. As a consequence, the emissions of major air pollutants have decreased and the air quality has substantially improved. Here, we quantified China's anthropogenic emission trends from 2010 to 2017 and identified the major driving forces of these trends by using a combination of bottom-up emission inventory and index decomposition analysis (IDA) approaches. The relative change rates of China's anthropogenic emissions during 2010–2017 are estimated as follows: −62 % for SO2, −17 % for NOx, +11 % for nonmethane volatile organic compounds (NMVOCs), +1 % for NH3, −27 % for CO, −38 % for PM10, −35 % for PM2.5, −27 % for BC, −35 % for OC, and +16 % for CO2. The IDA results suggest that emission control measures are the main drivers of this reduction, in which the pollution controls on power plants and industries are the most effective mitigation measures. The emission reduction rates markedly accelerated after the year 2013, confirming the effectiveness of China's Clean Air Action that was implemented since 2013. We estimated that during 2013–2017, China's anthropogenic emissions decreased by 59 % for SO2, 21 % for NOx, 23 % for CO, 36 % for PM10, 33 % for PM2.5, 28 % for BC, and 32 % for OC. NMVOC emissions increased and NH3 emissions remained stable during 2010–2017, representing the absence of effective mitigation measures for NMVOCs and NH3 in current policies. The relative contributions of different sectors to emissions have significantly changed after several years' implementation of clean air policies, indicating that it is paramount to introduce new policies to enable further emission reductions in the future.

Understanding the mechanisms controlling community diversity, functions, succession, and biogeography is a central, but poorly understood, topic in ecology, particularly in microbial ecology. Although stochastic processes are believed to play nonnegligible roles in shaping community structure, their importance relative to deterministic processes is hotly debated. The importance of ecological stochasticity in shaping microbial community structure is far less appreciated. Some of the main reasons for such heavy debates are the difficulty in defining stochasticity and the diverse methods used for delineating stochasticity. Here, we provide a critical review and synthesis of data from the most recent studies on stochastic community assembly in microbial ecology. We then describe both stochastic and deterministic components embedded in various ecological processes, including selection, dispersal, diversification, and drift. We also describe different approaches for inferring stochasticity from observational diversity patterns and highlight experimental approaches for delineating ecological stochasticity in microbial communities. In addition, we highlight research challenges, gaps, and future directions for microbial community assembly research.

From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM 2.5 ) concentrations occurred nationwide. Here we estimate the drivers of the improved PM 2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population–weighted annual mean PM 2.5 concentrations decreased from 61.8 (95%CI: 53.3–70.0) to 42.0 µg/m 3 (95% CI: 35.7–48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM 2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM 2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9–7.1), 4.4- (95% CI: 3.8–4.9), 2.8- (95% CI: 2.5–3.0), and 2.2- (95% CI: 2.0–2.5) µg/m 3 declines in the national PM 2.5 concentration in 2017, respectively, and further reduced PM 2.5 -attributable excess deaths by 0.37 million (95% CI: 0.35–0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China’s recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

As the world's second largest economy, China has experienced severe haze pollution, with fine particulate matter (PM) recently reaching unprecedentedly high levels across many cities, and an understanding of the PM formation mechanism is critical in the development of efficient mediation policies to minimize its regional to global impacts. We demonstrate a periodic cycle of PM episodes in Beijing that is governed by meteorological conditions and characterized by two distinct aerosol formation processes of nucleation and growth, but with a small contribution from primary emissions and regional transport of particles. Nucleation consistently precedes a polluted period, producing a high number concentration of nano-sized particles under clean conditions. Accumulation of the particle mass concentration exceeding several hundred micrograms per cubic meter is accompanied by a continuous size growth from the nucleation-mode particles over multiple days to yield numerous larger particles, distinctive from the aerosol formation typically observed in other regions worldwide. The particle compositions in Beijing, on the other hand, exhibit a similarity to those commonly measured in many global areas, consistent with the chemical constituents dominated by secondary aerosol formation. Our results highlight that regulatory controls of gaseous emissions for volatile organic compounds and nitrogen oxides from local transportation and sulfur dioxide from regional industrial sources represent the key steps to reduce the urban PM level in China.

Abstract. The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: −8.1 % for SO2, +19.2 % for NOx, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH3, −3.4 % for PM10, −1.6 % for PM2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resolution of 0.25° × 0.25° are developed and can be accessed from http://www.meicmodel.org/dataset-mix.

Significance Exceedingly high levels of fine particulate matter (PM) occur frequently in China, but the mechanism of severe haze formation remains unclear. From atmospheric measurements in two Chinese megacities and laboratory experiments, we show that the oxidation of SO 2 by NO 2 occurs efficiently in aqueous media under two polluted conditions: first, during the formation of the 1952 London Fog via in-cloud oxidation; and second, on fine PM with NH 3 neutralization during severe haze in China. We suggest that effective haze mitigation is achievable by intervening in the sulfate formation process with NH 3 and NO 2 emission control measures. Hence, our results explain the outstanding sulfur problem during the historic London Fog formation and elucidate the chemical mechanism of severe haze in China.

strongly over the past decades in terms of activity, with revenue passenger kilometers increasing from 109 to 8269 billion km yr -1 , and in terms of climate change impacts, with CO 2 emissions increasing by a factor of 6.8 to 1034 Tg CO 2 yr -1 . Over the period 2013-2018, the growth rates in both terms show a marked increase. Here, we present a new comprehensive and quantitative approach for evaluating aviation climate forcing terms. Both radiative forcing (RF) and effective radiative forcing (ERF) terms and their sums are calculated for the years 2000-2018. Contrail cirrus, consisting of linear contrails and the cirrus cloudiness arising from them, yields the largest positive net (warming) ERF term followed by CO 2 and NO x emissions. The formation and emission of sulfate aerosol yields a negative (cooling) term. The mean contrail cirrus ERF/RF ratio of 0.42 indicates that contrail cirrus is less effective in surface warming than other terms. For 2018 the net aviation ERF is +100.9 milliwatts (mW) m -2 (5-95% likelihood range of (55, 145)) with major contributions from contrail cirrus (57.4 mW m -2 ), CO 2 (34.3 mW m -2 ), and NO x (17.5 mW m -2 ). Non-CO 2 terms sum to yield a net positive (warming) ERF that accounts for more than half (66%) of the aviation net ERF in 2018. Using normalization to aviation fuel use, the contribution of global aviation in 2011 was calculated to be 3.5 (4.0, 3.4) % of the net anthropogenic ERF of 2290 (1130, 3330) mW m -2 . Uncertainty distributions (5%, 95%) show that non-CO 2 forcing terms contribute about 8 times more than CO 2 to the uncertainty in the aviation net ERF in 2018. The best estimates of the ERFs from aviation aerosol-cloud interactions for soot and sulfate remain undetermined. C 2 -warming-equivalent emissions based on global warming potentials (GWP* method) indicate that aviation emissions are currently warming the climate at approximately three times the rate of that associated with aviation CO 2 emissions alone. CO 2 and NO x aviation emissions and cloud effects remain a continued focus of anthropogenic climate change research and policy discussions.

Urban air pollution represents one of the greatest environmental challenges facing mankind in the 21st century. Noticeably, many developing countries, such as China and India, have experienced severe air pollution because of their fast-developing economy and urbanization. Globally, the urbanization trend is projected to continue: 70% of the world population will reside in urban centers by 2050, and there will exist 41 megacities (with more than 10 million inhabitants) by 2030. Air pollutants consist of a complex combination of gases and particulate matter (PM). In particular, fine PM (particles with the aerodynamic diameter smaller than 2.5 μm or PM_(2.5)) profoundly impacts
\nhuman health, visibility, the ecosystem, the weather, and the climate, and these PM effects are largely dependent on the aerosol properties, including the number concentration, size, and chemical composition. PM is emitted directly into the atmosphere (primary) or formed in the atmosphere through
\ngas-to-particle conversion (secondary) (Figure 1). Also,
\nprimary and secondary PM undergoes chemical and physical
\ntransformations and is subjected to transport, cloud processing, and removal from the atmosphere.

to form sulfate, whereby high reaction rates are sustained by the high neutralizing capacity of the atmosphere in northern China. This mechanism is self-amplifying because higher aerosol mass concentration corresponds to higher aerosol water content, leading to faster sulfate production and more severe haze pollution.

The development of reliable anthropogenic emission inventories is essential for both understanding the sources of air pollution and designing effective air-pollution-control measures in China. However, it is challenging to quantify emissions in China accurately, given the variety of contributing sources, the complexity of the technology mix and the lack of reliable measurements. Over the last two decades, tremendous efforts have been made to improve the accuracy of emission inventories, and significant improvements have been realized. More reliable statistics and survey-based data have been used to reduce the uncertainties in activity rates and technology distributions. Local emission factors and source profiles covering various sources have been measured and reported. Based on these local databases, improved emission inventory models have been developed for power plants, large industrial plants and the residential, transportation and agricultural sectors. In this paper, we review the progress that has been made in developing inventories of anthropogenic emissions in China. We first highlight the major updates that have been made to emission inventory models and the underlying data by source category. We then summarize the sector-based estimates of emissions of different species contained in current inventories. The progress that has been made in the development of model-ready emissions is also presented. Finally, we suggest future directions for further improving the accuracy of emission inventories in China.

Biomass burning (BB) is a significant air pollution source, with global, regional and local impacts on air quality, public health and climate. Worldwide an extensive range of studies has been conducted on almost all the aspects of BB, including its specific types, on quantification of emissions and on assessing its various impacts. China is one of the countries where the significance of BB has been recognized, and a lot of research efforts devoted to investigate it, however, so far no systematic reviews were conducted to synthesize the information which has been emerging. Therefore the aim of this work was to comprehensively review most of the studies published on this topic in China, including literature concerning field measurements, laboratory studies and the impacts of BB indoors and outdoors in China. In addition, this review provides insights into the role of wildfire and anthropogenic BB on air quality and health globally. Further, we attempted to provide a basis for formulation of policies and regulations by policy makers in China.

Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93-0.99), precision (0.80-0.94), sensitivity (0.82-0.94), and specificity (0.95-0.98) on simulated communities, which are 10-160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and 'drift' (59%). Interestingly, warming decreases 'drift' over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTNucleation and Growth of Nanoparticles in the AtmosphereRenyi Zhang*†‡§, Alexei Khalizov†, Lin Wang‡, Min Hu§, and Wen Xu†View Author Information† Department of Atmospheric Sciences and Department of Chemistry, Center for Atmospheric Chemistry and Environment, Texas A&M University, College Station, Texas 77843, United States ‡ Department of Environmental Science & Engineering and Institute of Global Environment Change Research, Fudan University, Shanghai 200433, China§ State Key Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China*E-mail: [email protected]Cite this: Chem. Rev. 2012, 112, 3, 1957–2011Publication Date (Web):November 1, 2011Publication History Received17 May 2011Published online1 November 2011Published inissue 14 March 2012https://pubs.acs.org/doi/10.1021/cr2001756https://doi.org/10.1021/cr2001756review-articleACS PublicationsCopyright © 2011 American Chemical SocietyRequest reuse permissionsArticle Views17895Altmetric-Citations895LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Cluster chemistry,Molecules,Nanoparticles,Nucleation,Sulfuric acid Get e-Alerts

While interactions between roots and microorganisms have been intensively studied, we know little about interactions among root-associated microbes. We used random matrix theory-based network analysis of 16S rRNA genes to identify bacterial networks associated with wild oat (Avena fatua) over two seasons in greenhouse microcosms. Rhizosphere networks were substantially more complex than those in surrounding soils, indicating the rhizosphere has a greater potential for interactions and niche-sharing. Network complexity increased as plants grew, even as diversity decreased, highlighting that community organisation is not captured by univariate diversity. Covariations were predominantly positive (> 80%), suggesting that extensive mutualistic interactions may occur among rhizosphere bacteria; we identified quorum-based signalling as one potential strategy. Putative keystone taxa often had low relative abundances, suggesting low-abundance taxa may significantly contribute to rhizosphere function. Network complexity, a previously undescribed property of the rhizosphere microbiome, appears to be a defining characteristic of this habitat.

Significance An ecological community is a dynamic complex system with a myriad of interacting species, which are controlled by various scale-dependent deterministic and stochastic forces. With rapid advances in genomics technologies, categorizing biological diversity, particularly microbial diversity, becomes relatively easy, but the great challenge is to disentangle the mechanisms controlling biological diversity. The general null model-based framework developed in this study provides an effective and robust tool to ecologists for quantitatively assessing ecological stochasticity. By highlighting the caveats such as model selection, similarity metrics, and spatial scales, this study provides guidance for appropriate use of null model-based approaches for examining community assembly processes. Although this framework was tested with microbial data, it should also be applicable to plant and animal ecology.

Magnetic nanoscaled Fe(3)O(4)/CeO(2) composite was prepared by the impregnation method and characterized as a heterogeneous Fenton-like catalyst for 4-chlorophenol (4-CP) degradation. The catalytic activity was evaluated in view of the effects of various processes, pH value, catalyst addition, hydrogen peroxide (H(2)O(2)) concentration, and temperature, and the pseudo-first-order kinetic constant of 0.11 min(-1) was obtained for 4-CP degradation at 30 °C and pH 3.0 with 30 mM H(2)O(2), 2.0 g L(-1) Fe(3)O(4)/CeO(2), and 0.78 mM 4-CP. The high utilization efficiency of H(2)O(2), calculated as 79.2%, showed a promising application of the catalyst in the oxidative degradation of organic pollutants. The reusability of Fe(3)O(4)/CeO(2) composite was also investigated after six successive runs. On the basis of the results of metal leaching, the effects of radical scavengers, intermediates determination, and X-ray photoelectron spectroscopic (XPS) analysis, the dissolution of Fe(3)O(4) facilitated by CeO(2) played a significant role, and 4-CP was decomposed mainly by the attack of hydroxyl radicals (•OH), including surface-bound •OH(ads) generated by the reaction of Fe(2+) and Ce(3+) species with H(2)O(2) on the catalyst surface, and •OH(free) in the bulk solution mainly attributed to the leaching of Fe.

The nationwide extent of surface ozone pollution in China and its comparison to the global ozone distribution have not been recognized because of the scarcity of Chinese monitoring sites before 2012. Here we address this issue by using the latest 5 year (2013–2017) surface ozone measurements from the Chinese monitoring network, combined with the recent Tropospheric Ozone Assessment Report (TOAR) database for other industrialized regions such as Japan, South Korea, Europe, and the United States (JKEU). We use various human health and vegetation exposure metrics. We find that although the median ozone values are comparable between Chinese and JKEU cities, the magnitude and frequency of high-ozone events are much larger in China. The national warm-season (April–September) fourth highest daily maximum 8 h average (4MDA8) ozone level (86.0 ppb) and the number of days with MDA8 values of >70 ppb (NDGT70, 29.7 days) in China are 6.3–30% (range of regional mean differences) and 93–575% higher, respectively, than the JKEU regional averages. Health exposure metrics such as warm-season mean MDA8 and annual SOMO35 (sum of ozone means over 35 ppb) are 6.3–16 and 25–95% higher in China, respectively. We also find an increase in the surface ozone level over China in 2016 and 2017 relative to 2013 and 2014. Our results show that on the regional scale the exposure of humans and vegetation to ozone is greater in China than in other developed regions of the world with comprehensive ozone monitoring.

Abstract. The mandate of the Task Force Hemispheric Transport of Air Pollution (TF HTAP) under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) is to improve the scientific understanding of the intercontinental air pollution transport, to quantify impacts on human health, vegetation and climate, to identify emission mitigation options across the regions of the Northern Hemisphere, and to guide future policies on these aspects. The harmonization and improvement of regional emission inventories is imperative to obtain consolidated estimates on the formation of global-scale air pollution. An emissions data set has been constructed using regional emission grid maps (annual and monthly) for SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, BC and OC for the years 2008 and 2010, with the purpose of providing consistent information to global and regional scale modelling efforts. This compilation of different regional gridded inventories – including that of the Environmental Protection Agency (EPA) for USA, the EPA and Environment Canada (for Canada), the European Monitoring and Evaluation Programme (EMEP) and Netherlands Organisation for Applied Scientific Research (TNO) for Europe, and the Model Inter-comparison Study for Asia (MICS-Asia III) for China, India and other Asian countries – was gap-filled with the emission grid maps of the Emissions Database for Global Atmospheric Research (EDGARv4.3) for the rest of the world (mainly South America, Africa, Russia and Oceania). Emissions from seven main categories of human activities (power, industry, residential, agriculture, ground transport, aviation and shipping) were estimated and spatially distributed on a common grid of 0.1° × 0.1° longitude-latitude, to yield monthly, global, sector-specific grid maps for each substance and year. The HTAP_v2.2 air pollutant grid maps are considered to combine latest available regional information within a complete global data set. The disaggregation by sectors, high spatial and temporal resolution and detailed information on the data sources and references used will provide the user the required transparency. Because HTAP_v2.2 contains primarily official and/or widely used regional emission grid maps, it can be recommended as a global baseline emission inventory, which is regionally accepted as a reference and from which different scenarios assessing emission reduction policies at a global scale could start. An analysis of country-specific implied emission factors shows a large difference between industrialised countries and developing countries for acidifying gaseous air pollutant emissions (SO2 and NOx) from the energy and industry sectors. This is not observed for the particulate matter emissions (PM10, PM2.5), which show large differences between countries in the residential sector instead. The per capita emissions of all world countries, classified from low to high income, reveal an increase in level and in variation for gaseous acidifying pollutants, but not for aerosols. For aerosols, an opposite trend is apparent with higher per capita emissions of particulate matter for low income countries.

Abstract Revealing the linkages between community assembly and species coexistence, which is crucial for the understanding of ecosystem diversity and functioning, is a fundamental but rarely investigated subject in microbial ecology. Here we examined archaeal, bacterial, and fungal community assembly in adjacent pairs of maize (water-unsaturated) and rice (water-saturated) fields across different habitats and regions throughout Eastern China. The high-throughput sequencing dataset was analyzed by variation partitioning, null model, and neutral community model analyses. We demonstrated that microbial community assembly was governed more by species sorting than by dispersal limitation in maize fields, and to a lesser extent in rice fields. The relative importance of species sorting in maize soils was greater at low latitudes than at high latitudes, while rice soils exhibited an opposite trend. Microbial co-occurrence associations tended to be higher when communities were primarily driven by dispersal limitation relative to species sorting. There were greater community dissimilarities between maize and rice soils in low-latitude regions, which was consistent with the higher proportion of negative edges in the correlation networks. The results indicate that a balance between species sorting and dispersal limitation mediates species coexistence in soil microbiomes. This study enhances our understanding of contemporary coexistence theory in microbial ecosystems.