Visibility forecast in Jiangsu province based on the GCN-GRU model.

Low visibility weather easily leads to traffic accidents, posing threats to human life and property. To accurately forecast visibility, we conduct an empirical study focusing on Jiangsu Province. Firstly, we collect the monitoring data from meteorological stations and environmental stations for 2017-2018. Secondly, we analyze the changes in visibility from both spatial and temporal perspectives. Next, the maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select factors affecting visibility, finding that humidity and P M 2.5 concentrations are the primary factors. Finally, we propose GCN-GRU (Graph Convolutional Network and Gated Recurrent Unit) model for short-term visibility forecasting, which employs GCN to capture the interactions between stations and uses GRU to learn the interactions between times. Experimental results indicate that GCN-GRU outperforms the standalone GRU model and three machine learning models regarding 6-hour visibility forecasting. Compared to the best competitor, GCN-GRU achieves an average increase of 3.32% in Correlation Coefficient (CORR), a decrease of 17.52% in Root Mean Square Error (RMSE), a reduction of 26.62% in Mean Absolute Percentage Error (MAPE), and a decline of 16.53% in Mean Absolute Error (MAE).

Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province.

Landslides are highly destructive geological disasters that pose a serious threat to the safety of people's lives and property. In this study, historical records of landslides in Yunnan Province, along with eight underlying factors of landslide (elevation, slope, aspect, lithology, land cover type, normalized difference vegetation index (NDVI), soil type, and average annual precipitation (AAP)), as well as historical rainfall and current rainfall data were utilized. Firstly, we analyzed the sensitivity of each underlying factor in the study area using the frequency ratio (FR) method and obtained a landslide susceptibility map (LSM). Then, we constructed a regional rainfall-induced landslides (RIL) probability forecasting model based on machine learning (ML) algorithms and divided warning levels. In order to construct a better RIL prediction model and explore the effects of different ML algorithms and input values of the underlying factor on the model, we compared five ML classification algorithms: extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms and three representatives of the input values of the underlying factors. The results show that among the obtained forecasting models, the LSM-based RF model performs the best, with an accuracy (ACC) of 0.906, an area under the curve (AUC) of 0.954, a probability of detection (POD) of 0.96 in the test set, and a prediction accuracy of 0.8 in the validation set. Therefore, we recommend using RF-LSM model as the RIL forecasting model for Yunnan Province and dividing warning levels.

Nocturnal surface radiation cooling modulated by cloud cover change reinforces PM2.5 accumulation: Observational study of heavy air pollution in the Sichuan Basin, Southwest China.

Surface radiation is crucial to atmospheric boundary layer development and air pollution formation. Several studies have revealed that surface radiation plays a vital role in developing the daytime convective boundary layer that controls the explosive growth of PM2.5 concentration; however, less attention has been paid to the effects of changing nighttime surface radiation on the near-surface temperature inversion layer and PM2.5 accumulation. In this study, we used long-term observations of meteorological and environmental data and atmospheric boundary layer measurements during a severe PM2.5 pollution event to investigate the effect of changes in nocturnal surface radiation on the increase in PM2.5 concentrations. The results showed that surface radiation cooling was enhanced (weakened) by decreased (increased) cloud cover fraction by changing longwave radiation at night; this strengthened (weakened) near-surface temperature inversion intensity and promoted (prevented) the accumulated increase in PM2.5. This observational study using 5-year data further confirmed the cloud radiative effect on the nighttime accumulation of PM2.5 with a significant negative correlation between nighttime averages of surface PM2.5 concentrations and cloud cover fractions. This reveals an important mechanism for the impact of surface radiation cooling modulated by cloud cover change on the nighttime accumulated increase in PM2.5. This finding extends our understanding of air pollutant accumulation at night with potential implications for atmospheric environment change.

Drivers of the rapid rise and daily-based accumulation in PM1.

Submicron particle matter (PM1) that rapidly reaches exceedingly high levels in several or more hours in the North China Plain (NCP) has been threating~400 million individuals' health for decades. The precise cause of the rapid rise in PM1 remains uncertain. Based on sophisticated measurements in PM1 characterizations and corresponding boundary-layer (BL) meteorology in the NCP, it demonstrates that this rising is mainly driven by BL meteorological variability. Large increases in near-ground inversions and decreases in vertical heat/momentum fluxes during the day-night transition result in a significant reduction in mixing space. The PM1 that is vertically distributed before accumulates at the near-ground and then experiences a rapid rise. Besides meteorological variability, a part of the rise in organics is ascribed to an increase of coal combustion at midnight. The daily-based accumulation of PM1 is attributed to day-to-day vertical meteorological variability, particularly diminishing mixing layer height exacerbated by aerosol-radiation feedback. Resolved by a multiple linear regression model, BL meteorological variability can explain 71% variances of PM1. In contrast, secondary chemical reactions facilitate the daily-based accumulation of PM1 rather than the rapid rise. Our results show that BL meteorological variability plays a dominant role in PM1 rising and day-to-day accumulation, which is crucial for understanding the mechanism of heavy pollution formation.

Temporal characteristics of carbon dioxide and ozone over a rural-cropland area in the Yangtze River Delta of eastern China.

In this study, rural atmospheric carbon dioxide (CO2) and ozone (O3) were measured from January 2015 to December 2018 to investigate characteristics of greenhouse gases in eastern China. Results showed that the annual average CO2 (O3) concentration in 2018 decreased by 2% (increased by 19%) when compared with that in 2015. CO2 concentrations exhibited monthly variability, peaking in February (443.7 ppm) and reaching their lowest levels in July (363.0 ppm); whereas, monthly O3 showed a bimodal pattern with peaks in June (51.3 ppb) and September (34.5 ppb). Regarding the diurnal variation, the maximum CO2 (O3) concentration occurred at nighttime (in the daytime) and a minimum CO2 (O3) in the daytime (at nighttime). As demonstrated by correlation analysis, CO2 and O3 variations were partly modulated by NOx and PM2.5. Furthermore, CO2 showed significant positive correlations with relative humidity in winter, while O3 showed strong positive correlations with temperature in spring. CO2 was accumulated from local sources under calm conditions (< 2 m s-1) and derived from remote sources at high wind speeds (> 4 m s-1), while O3 concentrations were peaking at medium wind speeds of 2-4 m s-1. CO2 was found to derive from long-distance (short-distance transport) transport in spring (the other three seasons), whereas O3 is mainly from long-distance (short-distance) transport in winter (the other three seasons). This work sheds light on the temporal characteristics of CO2 and O3, which has important implications for implementing practices to mitigate source emissions over cropland areas.

Modulations of surface thermal environment and agricultural activity on intraseasonal variations of summer diurnal temperature range in the Yangtze River Delta of China.

Compared with interdecadal, interannual, or seasonal scales, the variations of diurnal temperature range (DTR) at the intraseasonal scale and their driving forces are less understood. Using surface meteorological observations and multi-source satellite retrievals during 2013-2017, together with Random Forest modeling, this study examines the intraseasonal variation of summer DTR in the Yangtze River Delta (YRD) region, China, and determines its potential driving factors [i.e., daily maximum/minimum surface air temperature (SATmax/SATmin), sunshine duration (SSD), rainfall, altitude, land vegetation cover, and land surface thermal environment including daytime/nighttime land surface temperature (LSTD/LSTN) and anthropogenic heat flux (AHF)]. It is found that the intraseasonal variation of DTR at both 8-day and monthly scales in the YRD exhibits regional differences and is modulated by different primary factors across the region. The evident intraseasonal variation of DTR, with a peak in June, in the northern YRD, is largely attributable to nighttime temperatures (SATmin and LSTN), which in turn are mainly attributable to different LSTN responses to the underlying surface cover changes associated with crop rotation. In contrast, as the YRD metropolitan area (MYRD) is covered by a large proportion of built-up surfaces, and the weather stations there are surrounded by a higher surface thermal environment and AHF, the MYRD has stably higher LST and SATmin in the whole summer season. Thus, the summer DTR in the MYRD exhibits marginal intraseasonal variations. In the southern YRD, there is also a distinct DTR characteristic, with a maximum in July and minimum in June, since this region is largely covered by forests with constantly high-density vegetation cover, and its DTR variation is mainly forced by SSD, which directly affects SATmax. The findings reported here have important implications for understanding the influences of human activities on regional climate and environmental change for other regions of the world that experience various external forcings.

Spatiotemporal variability in long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across the Yangtze River Delta (YRD) region over 2010-2016: A multistage approach.

The Yangtze River Delta region (YRD) is one of the most densely populated regions in the world, and is frequently influenced by fine particulate matter (PM2.5). Specifically, lung cancer mortality has been recognized as a major health burden associated with PM2.5. Therefore, this study developed a multistage approach 1) to first create dasymetric population data with moderate resolution (1 km) by using a random forest algorithm, brightness reflectance of nighttime light (NTL) images, a digital elevation model (DEM), and a MODIS-derived normalized difference vegetation index (NDVI), and 2) to apply the improved population dataset with a MODIS-derived PM2.5 dataset to estimate the association between spatiotemporal variability of long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across YRD during 2010-2016 for microscale planning. The created dasymetric population data derived from a coarse census unit (administrative unit) were fairly matched with census data at a fine spatial scale (street block), with R2 and RMSE of 0.64 and 27,874.5 persons, respectively. Furthermore, a significant urban-rural difference of population exposure was found. Additionally, population exposure in Shanghai was 2.9-8 times higher than the other major cities (7-year average: 192,000 µg·people/m3·km2). More importantly, the relative risks of lung cancer mortality in high-risk areas were 28%-33% higher than in low-risk areas. There were 12,574-14,504 total lung cancer deaths attributable to PM2.5, and lung cancer deaths in each square kilometer of urban areas were 7-13 times higher than for rural areas. These results indicate that moderate-resolution information can help us understand the spatiotemporal variability of population exposure and related health risk in a high-density environment.

Atmospheric boundary layer turbulence structure for severe foggy haze episodes in north China in December 2016.

This paper aims to identify the atmospheric boundary layer turbulence structure and its effect on severe foggy haze events frequently occurring in Northern China. We use data collected from a ground eddy covariance system, meteorology tower, and a PM2.5 collector in Baoding, China during December 2016. The data shows that 73.5% of PM2.5 concentration is greater than 100 µg m-3 with a maximum of 522 µg m-3. Analyses on vertical turbulence spectrum also reveal that 1) during the pollution period, lower wind can suppress large-scale turbulence eddies, which are more likely inhomogeneous, breaking into small-scale eddies, and 2) the air pollutant scattering effect for radiation could decrease the air temperature near the ground and generate weak vertical turbulence during the daytime. At night, air pollutants suppress the land surface cooling and decrease the air temperature difference as well as the vertical turbulence intensity difference. The vertical turbulence impact analysis reveals that the percentage of large-scale turbulence eddies can also change the atmospheric vertical mixing capacity. During the daytime, the air pollution evolution is controlled by the wind speed and vertical turbulence intensity. While at night, the vertical turbulence is weak and the atmospheric vertical mixing capacity is mainly controlled by the large-scale eddies' percentage. The increased number of large-scale turbulence eddies led by low wind at night could increase the vertical mixing of air pollutants and decrease its concentration near the ground.

Basin-wide responses of the South China Sea environment to Super Typhoon Mangkhut (2018).

Relative to the open Northwest Pacific, the basin-scale South China Sea (SCS) is smaller and semi-enclosed, and the impacts of frequent super typhoons on the entire SCS basin have yet to be fully understood. Using multi-satellite observations and reanalysis data, this study explored biophysical responses of the upper ocean of the SCS induced by a typical super typhoon, Mangkhut (2018), and their regional differences with potential mechanisms. There were three different significant-response regions in the whole SCS, as follows: (1) In the ocean area around the typhoon path, strong vertical mixing, upwelling and cooling were induced, resulting in the surface chlorophyll-a (chl-a) concentration enhancing markedly (three-fold). Particularly, asymmetric distribution of typhoon rainfall induced asymmetric sea surface salinity change over along the path the nearshore. Diurnal peak of chl-a concentration increased obviously, and the daily growth rate of chl-a sped up considerably in non-shore areas after Mangkhut's passage. (2) In the Beibu Gulf (BBG), the peripheral winds of Mangkhut caused a change in direction of the sea surface flow field, transporting the high-temperature and high-salinity surface seawater from the southeastern area to the BBG. This induced dramatic increases in sea surface temperature, salinity and height, and a decrease in chl-a, in most areas of BBG. (3) In the southwest SCS, the southwest monsoon-induced eastward offshore upwelling jet was weakened by the opposite large-scale peripheral wind vector of Mangkhut and gradually disappeared, resulting in accumulation and enhancement of chl-a in the nearshore. In addition, Mangkhut peripheral winds also intensified (weakened), shifting the cold (warm) eddy to the north (south) and blocking the horizontal eastward transport belt of the high-concentration chl-a. In general, our present work sheds light on the new evidence that a supper typhoon can cause basin-wide anomalies in the SCS, which has broad implications for marine biophysical environment.

Parabolic dependence of the drag coefficient on wind speed from aircraft eddy-covariance measurements over the tropical Eastern Pacific.

In this study, we examine and present the relationship between drag coefficient and wind speed. We used an observational dataset that consists of 806 estimates of the mean flow and fluxes from aircraft eddy-covariance measurements over the tropical Eastern Pacific. To estimate the saturated wind speed threshold, we regressed the drag coefficients for wind speed scope from 10 ms-1 to 28 ms-1. Results show that the relationship between drag coefficient and wind speed is parabolic. Additionally, the saturated wind speed threshold is 22.33 ms-1 when regressed from drag coefficient, and it is 22.65 ms-1 when regressed from the medium number of drag coefficient for each bin.

Impacts of the near-surface urban boundary layer structure on PM2.5 concentrations in Beijing during winter.

The Urban Boundary layer (UBL) structure plays an important role in the accumulation of air pollutants in cities. To understand how the near-surface UBL structure affects air pollutants, we analyzed 40-day vertical observations collected at a 325-m meteorology tower in Beijing from 1 December 2016 to 9 January 2017. The occurrences of heavy pollution episodes (HPEs) in the study period were closely associated with weak wind speed (WS), high temperature, high relative humidity, weak fraction velocity (u∗) and weak turbulence kinetic energy (TKE) in near-surface UBL. In particular, the thickness and intensity of the temperature inversions were enhanced during all HPEs at nighttime. In addition, the PM2.5 concentration at the ground was significantly negative/positive correlation with vertical dynamic factors (e.g., WS, u∗ and TKE)/temperature inversion in the near-surface UBL. Diurnal variations in the vertical WS, potential temperature (θ), u∗ and TKE were less evident on the 23 polluted days than those on the 14 clean days; specifically, there were larger differences in the WS and θ between polluted and clean days at higher levels. Note that the varying quantitative relationships between the observed PM2.5 concentration and UBL dynamic factors during the daytime were much more significant than those at nighttime at all vertical levels. Compared with the WS, u∗ and TKE, the PM2.5 concentration showed a much more sensitive change with u∗ (WS) during the daytime (at nighttime).

An urban-rural and sex differences in cancer incidence and mortality and the relationship with PM2.5 exposure: An ecological study in the southeastern side of Hu line.

This study investigates the urban-rural and sex differences in the increased risks of the ten most common cancers in China related to high PM2.5 concentration in the southeastern side of Hu line. Pearson correlation coefficient is estimated to reveal how the cancers closely associated with PM2.5 long-term exposure. Then linear regression is conducted to evaluate sex- and area-specific increased risks of those cancers from high level PM2.5 long-term exposure. The major finding is with the increase of every 10 µg/m3 of annual mean PM2.5 concentration, the increase of relative risks for lung cancer incidence and mortality are 15% and 23% for males, and 22% and 24% for females in rural area. For urban area, the increase of relative risk for ovarian cancer incidence is 9% for females, while that for prostatic cancer increases 17% for males. For leukemia, the increase of relative risks for incidence and mortality are 22% and 19% for females in rural area, while in urban area the increase of relative risk for mortality is 9% for males and for incidence is 6% for females. It is also found that with increased PM2.5 exposure, the risks for ovarian and prostatic cancer rise significantly in urban area, while risks for lung cancer and leukemia rise significantly in rural area. The results demonstrate the higher risks for lung cancer and leukemia with increased PM2.5 exposure are more significant for female. This study also suggests that the carcinogenic effects of PM2.5 have obvious sex and urban-rural differences.

Comparison of community structures of Candidatus Methylomirabilis oxyfera-like bacteria of NC10 phylum in different freshwater habitats.

Methane oxidation coupled to nitrite reduction is mediated by 'Candidatus Methylomirabilis oxyfera' (M. oxyfera), which belongs to the NC10 phylum. In this study, the community composition and diversity of M. oxyfera-like bacteria of NC10 phylum were examined and compared in four different freshwater habitats, including reservoir sediments (RS), pond sediments (PS), wetland sediments (WS) and paddy soils (PAS), by using Illumina-based 16S rRNA gene sequencing. The recovered NC10-related sequences accounted for 0.4-2.5% of the 16S rRNA pool in the examined habitats, and the highest percentage was found in WS. The diversity of NC10 bacteria were the highest in RS, medium in WS, and lowest in PS and PAS. The observed number of OTUs (operational taxonomic unit; at 3% cut-off) were 97, 46, 61 and 40, respectively, in RS, PS, WS and PAS. A heterogeneous distribution of NC10 bacterial communities was observed in the examined habitats, though group B members were the dominant bacteria in each habitat. The copy numbers of NC10 bacterial 16S rRNA genes ranged between 5.8 × 10(6) and 3.2 × 10(7) copies g(-1) sediment/soil in the examined habitats. These results are helpful for a systematic understanding of NC10 bacterial communities in different types of freshwater habitats.

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision.

Evidence for anaerobic ammonium oxidation process in freshwater sediments of aquaculture ponds.

The anaerobic ammonium oxidation (anammox) process, which can simultaneously remove ammonium and nitrite, both toxic to aquatic animals, can be very important to the aquaculture industry. Here, the presence and activity of anammox bacteria in the sediments of four different freshwater aquaculture ponds were investigated by using Illumina-based 16S rRNA gene sequencing, quantitative PCR assays and (15)N stable isotope measurements. Different genera of anammox bacteria were detected in the examined pond sediments, including Candidatus Brocadia, Candidatus Kuenenia and Candidatus Anammoxoglobus, with Candidatus Brocadia being the dominant anammox genus. Quantitative PCR of hydrazine synthase genes showed that the abundance of anammox bacteria ranged from 5.6 × 10(4) to 2.1 × 10(5) copies g(-1) sediment in the examined ponds. The potential anammox rates ranged between 3.7 and 19.4 nmol N2 g(-1) sediment day(-1), and the potential denitrification rates varied from 107.1 to 300.3 nmol N2 g(-1) sediment day(-1). The anammox process contributed 1.2-15.3% to sediment dinitrogen gas production, while the remainder would be due to denitrification. It is estimated that a total loss of 2.1-10.9 g N m(-2) per year could be attributed to the anammox process in the examined ponds, suggesting that this process could contribute to nitrogen removal in freshwater aquaculture ponds.

Distribution and activity of anaerobic ammonium-oxidising bacteria in natural freshwater wetland soils.

Anaerobic ammonium oxidation (anammox) process plays a significant role in the marine nitrogen cycle. However, the quantitative importance of this process in nitrogen removal in wetland systems, particularly in natural freshwater wetlands, is still not determined. In the present study, we provided the evidence of the distribution and activity of anammox bacteria in a natural freshwater wetland, located in southeastern China, by using (15)N stable isotope measurements, quantitative PCR assays and 16S rRNA gene clone library analysis. The potential anammox rates measured in this wetland system ranged between 2.5 and 25.5 nmol N2 g(-1) soil day(-1), and up to 20% soil dinitrogen gas production could be attributed to the anammox process. Phylogenetic analysis of 16S rRNA genes showed that anammox bacteria related to Candidatus Brocadia, Candidatus Kuenenia, Candidatus Anammoxoglobus and two novel anammox clusters coexisted in the collected soil cores, with Candidatus Brocadia and Candidatus Kuenenia being the dominant anammox genera. Quantitative PCR of hydrazine synthase genes showed that the abundance of anammox bacteria varied from 2.3 × 10(5) to 2.2 × 10(6) copies g(-1) soil in the examined soil cores. Correlation analyses suggested that the soil ammonium concentration had significant influence on the activity of anammox bacteria. On the basis of (15)N tracing technology, it is estimated that a total loss of 31.1 g N m(-2) per year could be linked the anammox process in the examined wetland.

[Impact of Phosphogypsum Wastes on the Wheat Growth and CO2 Emissions and Evaluation of Economic-environmental Benefit].

Phosphogypsum is a phosphorus chemical waste which has not been managed and reused well, resultantly, causing environmental pollution and land-occupation. Phosphogypsum wastes were used as a soil amendment to assess the effect on wheat growth, yield and CO2 emissions from winter wheat fields. Its economic and environmental benefits were analyzed at the same time. The results showed that wheat yield was increased by 37.71% in the treatment of phosphogypsum of 2 100 kg x hm(-2). Compared with the control treatment, throughout the wheat growing season, CO2 emission was accumulatively reduced by 3% in the treatment of phosphogypsum waste of 1050 kg x hm(-2), while reduced by 8% , 10% , and 6% during the jointing stage, heading date and filling period of wheat, respectively; while CO2 emission was accumulatively reduced by 7% in the treatment of phosphogypsum waste of 2 100 kg x hm(-2) throughout the wheat growing season, as reduced by 11% , 4% , and 12% during the reviving wintering stage, heading date and filling period of wheat, respectively. It was better for CO2 emission reduction in the treatment of a larger amount of phosphogypsum waste. In the case of application of phosphogypsum waste residue within a certain range, the emission intensity of CO2 ( CO2 emissions of per unit of fresh weight or CO2 emissions of per unit of yield) , spike length, fresh weight and yield showed a significantly negative correlation--the longer the ear length, the greater fresh weight and yield and the lower the CO2 emissions intensity. As to the carbon trading, phosphogypsum utilization was of high economic and environmental benefits. Compared with the control, the ratio of input to output changed from 1: 8.3 to 1: 10.7, which in the same situation of investment the output could be increased by 28.92% ; phosphogypsum as a greenhouse gas reducing agent in the wheat field, it could decrease the cost and increase the environmental benefit totally about 290 yuan per unit of ton. The results demonstrated phosphogypsum wastes could obviously decrease the CO2 emission from field soil and had a great potential to control agricultural greenhouse gases. Hopefully it has an important application perspective for the low-carbon, ecological and sustainable agricultural development.

Real-Time Characterization of Aerosol Particle Composition above the Urban Canopy in Beijing: Insights into the Interactions between the Atmospheric Boundary Layer and Aerosol Chemistry.

Despite extensive efforts into the characterization of air pollution during the past decade, real-time characterization of aerosol particle composition above the urban canopy in the megacity Beijing has never been performed to date. Here we conducted the first simultaneous real-time measurements of aerosol composition at two different heights at the same location in urban Beijing from December 19, 2013 to January 2, 2014. The nonrefractory submicron aerosol (NR-PM1) species were measured in situ by a high-resolution aerosol mass spectrometer at near-ground level and an aerosol chemical speciation monitor at 260 m on a 325 m meteorological tower in Beijing. Secondary aerosol showed similar temporal variations between ground level and 260 m, whereas much weaker correlations were found for the primary aerosol. The diurnal evolution of the ratios and correlations of aerosol species between 260 m and the ground level further illustrated a complex interaction between vertical mixing processes and local source emissions on aerosol chemistry in the atmospheric boundary layer. As a result, the aerosol compositions at the two heights were substantially different. Organic aerosol (OA), mainly composed of primary OA (62%), at the ground level showed a higher contribution to NR-PM1 (65%) than at 260 m (54%), whereas a higher concentration and contribution (15%) of nitrate was observed at 260 m, probably due to the favorable gas-particle partitioning under lower temperature conditions. In addition, two different boundary layer structures were observed, each interacting differently with the evolution processes of aerosol chemistry.

Occurrence and importance of anaerobic ammonium-oxidising bacteria in vegetable soils.

The quantitative importance of anaerobic ammonium oxidation (anammox) has been described in paddy fields, while the presence and importance of anammox in subsurface soil from vegetable fields have not been determined yet. Here, we investigated the occurrence and activity of anammox bacteria in five different types of vegetable fields located in Jiangsu Province, China. Stable isotope experiments confirmed the anammox activity in the examined soils, with the potential rates of 2.1 and 23.2 nmol N2 g(-1) dry soil day(-1), and the anammox accounted for 5.9-20.5% of total soil dinitrogen gas production. It is estimated that a total loss of 7.1-78.2 g N m(-2) year(-1) could be linked to the anammox process in the examined vegetable fields. Phylogenetic analyses showed that multiple co-occurring anammox genera were present in the examined soils, including Candidatus Brocadia, Candidatus Kuenenia, Candidatus Anammoxoglobus and Candidatus Jettenia, and Candidatus Brocadia appeared to be the most common anammox genus. Quantitative PCR further confirmed the presence of anammox bacteria in the examined soils, with the abundance varying from 2.8 × 10(5) to 3.0 × 10(6) copies g(-1) dry soil. Correlation analyses suggested that the soil ammonium concentration had significant influence on the activity and abundance of anammox bacteria in the examined soils. The results of our study showed the presence of diverse anammox bacteria and indicated that the anammox process could serve as an important nitrogen loss pathway in vegetable fields.

Distribution and environmental significance of nitrite-dependent anaerobic methane-oxidising bacteria in natural ecosystems.

Nitrite-dependent anaerobic methane oxidation (N-DAMO) is a recently discovered process that is performed by "Candidatus Methylomirabilis oxyfera" (M. oxyfera). This process constitutes a unique association between the two major global elements essential to life, carbon and nitrogen, and may act as an important and overlooked sink of the greenhouse gas methane. In recent years, more and more studies have reported the distribution of M. oxyfera-like bacteria and the occurrence of N-DAMO process in different natural ecosystems, including freshwater lakes, rivers, wetlands and marine ecosystems. Previous studies have estimated that a total of 2%-6% of current worldwide methane flux in wetlands could be consumed via the N-DAMO process. These findings indicate that N-DAMO is indeed a previously overlooked methane sink in natural ecosystems. Given the worldwide increase in anthropogenic nitrogen pollution, the N-DAMO process as a methane sink in reducing global warming could become more important in the future. The present mini-review summarises the current knowledge of the ecological distribution of M. oxyfera-like bacteria and the potential importance of the N-DAMO process in reducing methane emissions in various natural ecosystems. The potential influence of environmental factors on the N-DAMO process is also discussed.