Amos P. K. Tai

Taxonomic resolution, ecotypes and the biogeography of Prochlorococcus

In order to expand our understanding of the diversity and biogeography of Prochlorococcus ribotypes, we PCR-amplified, cloned and sequenced the 16S/23S rRNA ITS region from sites in the Atlantic and Pacific oceans. Ninety-three per cent of the ITS sequences could be assigned to existing Prochlorococcus clades, although many novel subclades were detected. We assigned the sequences to operational taxonomic units using a graduated scale of sequence identity from 80% to 99.5% and correlated Prochlorococcus diversity with respect to environmental variables and dispersal time between the sites. Dispersal time was estimated using a global ocean circulation model. The significance of specific environmental variables was dependent on the degree of sequence identity used to define a taxon: light correlates with broad-scale diversity (90% cut-off), temperature with intermediate scale (95%) whereas no correlation with phosphate was observed. Community structure was correlated with dispersal time between sample sites only when taxa were defined using the finest sequence similarity cut-off. Surprisingly, the concentration of nitrate, which cannot be used as N source by the Prochlorococcus strains in culture, explains some variation in community structure for some definitions of taxa. This study suggests that the spatial distribution of Prochlorococcus ecotypes is shaped by a hierarchy of environmental factors as well dispersal limitation.

Editorial: Utilization of data from air quality monitoring networks

The severe air pollution in Asian countries has attracted great attention of the public, government, and scientists in recent decades. Measurement of criteria pollutants including particulate matter, ozone, sulfur dioxide, nitrogen dioxide and carbon monoxide and air quality indexes (AQI) are calculated based on these species to inform the public about levels of air pollution and associated health risks. This system has been widely used worldwide but only developed countries such as Japan and South Korean in Asian have national monitoring systems for long time. Recently, more countries started releasing regular pollutants concentrations and calculated AQI values. For example, the Chinese Ministry of Environmental Protection (MEP) started to publish hourly air quality data of the six criteria pollutants at individual monitoring site for 74 major cities through the website in January 2013. India also has a National Air Quality Monitoring Programme (NAMP) executed by Central Pollution Control Board to measure selected pollutants at about 100 cities. These extensive and publicly accessible air quality observation data are helpful to inform the public and allow them take actions to reduce adverse effects. These data can be used to determine the status and trends of air quality in each city, to identify non-attainment cities, and other policy purposes. They also help the researchers understand the characteristics, formation, source, and climate effects of air pollutants since the lack of detailed measurements of particulate matter size and components as well as other major components playing roles in the formation of criteria pollutants. Simulation studies benefit from the dataset as well by using them to validate and improve the models. Several studies have been published based on the dataset and more studies are expected in near future. A total of 17 papers were selected for publication after peer-review process, all utilizing data from air quality monitoring networks. Guo et al. characterizes criteria air pollutants in Beijing, and Shen et al. investigates air pollution characteristics in Henan, China. Ahmed et al. analyzes spatiotemporal interpolation of air pollutants in the Greater Cairo and the Delta, Egypt. Hu et al. compares the atmospheric visibility trends in China, India, and United States. The monitoring data are also used for health analysis. Jerrett et al. using air pollution sensors to improve expires estimates for epidemiological analysis, Chen K et al. quantifies the acute effects of ozone exposure on daily mortality in Jiangsu, and Chen C et al. analyzes short-term exposures to PM2.5 and cause-specific mortality of cardiovascular health in China. Asl et al. quantifies health impacts of air pollutants in Hamadan, Iran. Huang et al. compares individual exposure, perception, and acceptable levels of PM2.5 with air pollution policy objectives in China. The monitoring data can be used for various purposes. Yao shows the inhibition of evapotranspiration by air pollution in the North China Plain (NCP) and Li Z et al. discusses the factors affecting variability of PM2.5 exposure in a metro system. Li S et al. focuses on dynamics and ecological risk assessment of chromophoric dissolved organic matter. The data from monitoring networks were also used for improving land use regression models. For example, Huang et al. develops land use regression models for PM2.5, SO2, NO2 and O3 in Nanjing and Shi et al. incorporated wind availability into land use regression modelling of air quality. The special issues also includes studies that try to improve the monitoring of air pollutants. Yang and Wang proposes a new air quality monitoring and early warning system for air quality assessment and prediction and Harkat et al. suggests enhanced data validation strategy for air quality monitoring networks. Liang et al. evaluates a data fusion approach to estimated PM2.5 levels in North China. We’d like to thank the Editor-in-Chief, Dr. Jose L. Domingo, and associate editor, Dr. Nancy B. Mai, for their support of this special issue. We also thank the authors and reviewers for their contribution to this issue. We hope the issue helps the utilization of data from air quality monitoring networks to improve air quality and protect human health for all regions. Any comments or suggestions for future research communication and collaboration are welcome.

Synoptic meteorological modes of variability for fine particulate matter (PM 2.5 ) air quality in major metropolitan regions of China

In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ∼ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ∼ 0.5 μg m−3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small “climate benefit”, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.