Spatial distribution and community composition of endophytic fungi within Mussaenda pubescens stems.

Endophytic fungi, pivotal in facilitating plant co-evolution, significantly enhance plant growth, stress resistance, and environmental adaptability. Despite their importance, the spatial distribution of stem endophytic fungi (SEF) within host plants remains poorly characterized. Here, we employed high-throughput sequencing to conduct a comparative analysis of SEF communities in Mussaenda pubescens on a regional scale. Our findings reveal that whole-SEF communities were overwhelmingly dominated by members of the phylum Ascomycota, accounting for 85.9 %, followed by Basidiomycota at 13.9 %, and that alpha diversity within the whole-SEF community of M. pubescens remains relatively consistent across sampling sites. However, significant variation was observed within conditionally abundant taxa (CAT), conditionally rare or abundant taxa (CRAT), and conditionally rare taxa (CRT). Climatic factors emerged as the primary influence on SEF community distribution, followed by spatial distance and stem chemical properties. Neutral community modeling results suggested that both stochastic and deterministic processes play a role in shaping whole-SEF communities, with deterministic processes having a stronger influence on CRT subcommunities. Furthermore, the CRT co-occurrence network exhibited a more complex structure, characterized by higher values of network betweenness and degree relative to CAT and CRAT subcommunities. These findings enhance our understanding of community assembly and ecological interactions between stem fungal endophytes, presenting opportunities for harnessing fungal resources for the benefit of humanity.

Aerosol in global oceanic regions: Four-decade trends, spatial patterns, and policy implications.

High aerosol loadings are observed not only in megacities on continents but also in oceanic regions like the Bohai Sea. This work provides a comprehensive analysis of the spatial and temporal variations in Aerosol Optical Depth (AOD) across different ocean regions worldwide over the past four decades, using remote sensing reanalysis data. The mean AOD value across all oceanic grids is approximately 0.112, with higher levels recorded in the Central Atlantic (~0.206), followed by the North Indian Ocean (~0.201), and the Western North Pacific (~0.197). A latitudinal analysis reveals that high AOD values are predominantly found in the Northern Hemisphere's oceanic regions, especially between latitudes 0° and 70° N. Except for the Gulf of California and Hudson Bay, AOD values in the other fourteen surveyed inland seas surpass the mean levels found at similar latitudes in oceanic regions. Among which, the Bohai Sea stands out as the most polluted oceanic region with AOD value of 0.35. Over the last four decades, AOD trends have revealed a significant decrease across about 89.5 % of global oceanic grids, while an increase in AOD is observed in low-latitude oceanic areas (30° S-30° N). Investigation into inland seas shows that nearly two-thirds have experienced a declining AOD trend, while sharply upward trends in AOD are primarily found in Asia. The Bohai Sea shows the largest increase in AOD, with an annual growth rate of 1.4 %. The turning-points of the AOD in each inland sea confirm the success of regional emission control policies initiated on the adjacent continents. To improve air quality in inland seas like the Bohai Sea, adjusting industrial layouts, such as relocating heavy industries from the surrounding coastal cities' proximities to areas near open seas, could significantly benefit public health.

Quantification of NOx sources contribution to ambient nitrate aerosol, uncertainty analysis and sensitivity analysis in a megacity.

Dual isotopes of nitrogen and oxygen of NO3- are crucial tools for quantifying the formation pathways and precursor NOx sources contributing to atmospheric nitrate. However, further research is needed to reduce the uncertainty associated with NOx proportional contributions. The acquisition of nitrogen isotopic composition from NOx emission sources lacks regulation, and its impact on the accuracy of contribution results remains unexplored. This study identifies key influencing factors of source isotopic composition through statistical methods, based on a detailed summary of δ15N-NOx values from various sources. NOx emission sources are classified considering these factors, and representative means, standard deviations, and 95 % confidence intervals are determined using the bootstrap method. During the sampling period in Tianjin in 2022, the proportional nitrate formation pathways varied between sites. For suburban and coastal sites, the ranking was [Formula: see text] (NO2 + OH radical) > [Formula: see text] (N2O5 + H2O) > [Formula: see text] (NO3 + DMS/HC), while the rural site exhibited similar fractional contributions from all three formation pathways. Fossil fuel NOx sources consistently contributed more than non-fossil NOx sources in each season among three sites. The uncertainties in proportional contributions varied among different sources, with coal combustion and biogenic soil emission showing lower uncertainties, suggesting more stable proportional contributions than other sources. The sensitivity analysis clearly identifies that the isotopic composition of 15N-enriched and 15N-reduced sources significantly influences source contribution results, emphasizing the importance of accurately characterizing the localized and time-efficient nitrogen isotopic composition of NOx emission sources. In conclusion, this research sheds light on the importance of addressing uncertainties in NOx proportional contributions and emphasizes the need for further exploration of nitrogen isotopic composition from NOx emission sources for accurate atmospheric nitrate studies.

Characteristics of number concentration, size distribution and components of particulate matter emitted from a typical large civil airport.

Civil airports are recognized as significant contributors to fine particulate matter, especially ultra-fine particulate matter (UFP). The pollutants from airport activities have a notable adverse impact on global climate, urban air quality, and public health. However, there is a lack of practical observational studies on the characterization of integrated pollutant emissions from large civil airports. This study aims to focus on the combined emission characteristics of particulate number concentration (PNC), size distribution, and components at a large civil airport, especially UFP. The findings reveal that airport activities significantly contribute to elevated PNC levels during aircraft activity in downwind conditions (four times higher than background levels) and upwind conditions (7.5 times higher). UFP dominates the PNC around the airport. The particle size distribution shows two peaks occurring around 10-30 nm and 60-80 nm. Notably, particles within the ranges of 17-29 nm and 57-101 nm account for 65.9 % and 12.0 % of the total PNC respectively. Aircraft landing has the greatest impact on particles sized between 6 and 17 nm while takeoff affects particles sized between 29 and 57 nm resulting in a respective increase in PNC by factors of approximately 3.27 and 35.4-fold increase compared to background levels. Different aircraft types exhibit varying effects on PNC with A320 and A321 showing more pronounced effects during takeoff and landing.The presence of airports leads to roughly five-fold rise in elemental component concentrations with Si being highest followed by OC, Ca, Al, Fe, Ca2+, EC, and Mg2+. The OC/EC ratio under high aircraft activity in downwind conditions falls within range of approximately 2.5-3.5. These characteristic components and ratio can be considered as identifying species for civil airports. PMF model show about 75 % of the particulate emissions at the airport boundary were related to airport activities.

Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model.

Ambient particulate matter (PM2.5 and PM10), has been extensively monitored in numerous urban areas across the globe. Over the past decade, there has been a significant improvement in PM2.5 air quality, while improvements in PM10 levels have been comparatively modest, primarily due to the limited reduction in coarse particle (PM2.5-10) pollution. Unlike PM2.5, PM2.5-10 predominantly originates from local emissions and is often characterized by pronounced spatial heterogeneity. In this study, we utilized over one million data points on PM concentrations, collected from >100 monitoring sites within a Chinese megacity, to perform spatial source apportionment of PM2.5-10. Despite the widespread availability of such data, it has rarely been employed for this purpose. We employed an enhanced positive matrix factorization approach, capable of handling large datasets, in conjunction with a Bayesian multivariate receptor model to deduce spatial source impacts. Four primary sources were successfully identified and interpreted, including residential burning, industrial processes, road dust, and meteorology-related sources. This interpretation was supported by a considerable body of prior knowledge concerning emission sources, which is usually unavailable in most cases. The methodology proposed in this study demonstrates significant potential for generalization to other regions, thereby contributing to the development of air quality management strategies.

High aerosol loading over the Bohai Sea: Long-term trend, potential sources, and impacts on surrounding cities.

Air pollution over the oceans has received less attention compared to densely populated urban areas of continents. The Bohai Sea, a semi-enclosed sea in northern China, is surrounded by thirteen industrial cities that have experienced significant improvements in air quality over the past decade. However, the changes in air pollution over the Bohai Sea and its impacts on surrounding cities remain poorly understood. To address this, this study investigated the evolution of air pollution and its chemical composition in the Bohai Sea over four decades, utilizing satellite remote sensing data, reanalysis datasets, emissions inventories, and statistical modeling. Historically, the region has suffered from severe air pollution, resulting from a combination of continental emissions and marine inputs (e.g., sea salt, ports and maritime vessel activities). The aerosol optical depth (AOD) over the sea was higher than the mean levels observed in its surrounding coastal cities. Statistically, 45% of the air masses reaching the Bohai Sea are associated with natural sources (dust- and marine-rich), while the remainder carry anthropogenic pollutants from continental regions. With the exception of Cangzhou city, these coastal cities suffer from air pollutants originating from the Bohai Sea. Cities in the northern region of the sea, spanning from Tianjin to Yingkou, are particularly impacted. The majority of the surrounding cities are affected by a large proportion of anthropogenic aerosol types transported through air masses from the Bohai Sea, including those from biomass burning and industrial activities. These findings emphasize the considerable influence of human-induced sources in the Bohai Sea on neighboring urban areas. Furthermore, being a maritime region, natural sources like sea salt and dust from the sea may also exert a discernible impact on the neighboring environment.

Efficient spatial and channel net for lane marker detection based on self-attention and row anchor.

Lane detection is an important component of advanced driving aided system (ADAS). It is a combined component of the planning and control algorithms. Therefore, it has high standards for the detection accuracy and speed. Recently several researchers have worked extensively on this topic. An increasing number of researchers have been interested in self-attention-based lane detection. In difficult situations such as shadows, bright lights, and nights extracting global information is effective. Regardless of channel or spatial attention, it cannot independently extract all global information until a complicated model is used. Furthermore, it affects the run-time. However trading in this contradiction is challenging. In this study, a new lane identification model that combines channel and spatial self-attention was developed. Conv1d and Conv2d were introduced to extract the global information. The model is lightweight and efficient avoiding difficult model calculations and massive matrices, In particular obstacles can be overcome under certain difficult conditions. We used the Tusimple and CULane datasets as verification standards. The accuracy of the Tusimple benchmark was the highest at 95.49%. In the CULane dataset, the proposed model achieved 75.32% in F1, which is the highest result, particularly in difficult scenarios. For the Tusimple and CULane datasets, the proposed model achieved the best performance in terms of accuracy and speed.

[Characteristics and Driving Factors of O3 Pollution During 13th Five-Year Period in Tianjin].

The spatial distribution, accumulation features, and driving factors of O3 pollution were analyzed using spatial autocorrelation and hotspot analysis and the STIRPAT model based on the high spatiotemporal resolution online monitoring data from 2016 to 2020 in Tianjin. The results showed that the variation characteristics of O3 concentration in Tianjin from 2016 to 2020 had the trend of pollution occurring in advance and the scope of the pollution expanding. The distribution of O3 pollution showed significant aggregation from June to October. High-high value clustering areas included six urban districts, Beichen District, Jinnan District, and Jinghai District. O3 concentration formed high value hot spots in the southwest and low value cold spots in the northeast. Meteorological factors such as temperature, breeze percentage, and sunshine duration, as well as social factors such as NOx emission, VOCs emission, and motor vehicle ownership had significant effects on O3 concentration. The regression fitting effect of the integrated drive STIRPAT model was better than that of the single meteorological factor or social factor models. In order to promote scientific and efficient prevention and control of ozone pollution during the 14th Five-Year Plan period, meteorological conditions require attention; under the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," it is necessary for Tianjin to further improve the emission performance of steel, petrochemicals, thermal power, building materials, and other industries, Additionally, clean upgrading, transformation, and green development should be guided for enterprises to reduce VOCs and NOx emissions. At same time, the increase in fuel vehicle numbers should be controlled, and new energy vehicles should be vigorously promoted to reduce vehicle emissions.

Comprehensive performance evaluation of coordinated development of industrial economy and its air pollution control.

Exploring coordinated pathways that can promote not only the sustainable development of the industrial economy but also air quality is of great significance for the prevention and control of air pollution in China. Currently, the joint development pathways of the industrial economy-environment nexus remain unclear and poorly evaluated. In this study, we proposed a comprehensive performance evaluation combining objective and subjective weighting to identify industrial enterprises' economic-environment nexus benefits. It would be one of the most important steps to explore the coordinated pathways. Based on data envelopment analysis (DEA), the proposed method integrated with the index integration was used to evaluate the comprehensive performances of 41 industrial sectors in China's 13th five-year plan (2016-2020). Evaluation results showed that the comprehensive performances of the economy-environment nexus of the industrial sectors varied significantly, with the five-year average comprehensive technical efficiency (TE) of 0.11-1. Overall, the best two performances were realized by the industries of equipment manufacturing and living consumption, whereas the worst one belonged to the industry of bulk raw materials, with average comprehensive TE values of 0.50, 0.43, and 0.19, respectively. The results of the quantitative evaluation were consistent with those of the qualitative analysis in terms of the developmental status of the industrial sectors. According to the analyses of pure technical efficiency and scale effect, the proposed method identified the industrial sectors with the highest developmental value and with the highest need to control air pollution. Compared with those of the original DEA model, the results of the proposed method showed pronounced differences in terms of the performances of industrial sectors with high energy consumption and high particulate matter (PM) emissions and with low energy consumption and low PM emissions. The proposed evaluation method combining the weighting was suitable for identifying the comprehensive performance of the industrial economy-environment nexus and provides the basis for the prevention and control of air pollution.

Retirement is not the end of the road: Essentialist beliefs about aging moderate the association between future time perspective and retirement adjustment.

Objective: The present study aims to investigate whether changes in future time perspective could influence individual's retirement process and adjustment. Moreover, we would also like to test moderation effect of essentialist beliefs about aging on the association between changes in future time perspective and retirement adjustment. Method: 201 participants were recruited 3 months before retirement and followed for 6 months. Future time perspective was measured before and after retirement. Essentialist beliefs about aging was measured before retirement. Other demographics as well as life satisfaction were also measured as covariates. Results: Multiple regressions were conducted, and the results revealed that (1) retirement could lead to limited future time perspective, but individual differences regarding the influence of retirement on future time perspective also exists; (2) increase in future time perspective was positively associated with retirement adjustment; and moreover, (3) such association was moderated by fixed views of essentialist beliefs, such that retirees holding a more fixed view of essentialist beliefs about aging exhibited a stronger association between future time perspective change and retirement adjustment, while those who held less fixed view of essentialist beliefs about aging did not show such association. Discussion: The present study contributes to the literature by showing that future time perspective could be influence by retirement, and such changes could in turn further impact adjustment. And the association between changes in future time perspective and retirement adjustment was only effective among retirees holding more fixed views of essentialist beliefs about aging. Findings would also provide important practical implications to improve retirement adjustment. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-023-04731-w.

[Spatial-temporal Variation and Driving Factors of Ozone in China from 2019 to 2021 Based on EOF Technique and KZ Filter].

Based on the hourly O3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O3 and meteorological factors. Ultimately, the long-term component of O3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O3 concentration showed a convergent change, that is, the volatility of O3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.

Trends of source apportioned PM2.5 in Tianjin over 2013-2019: Impacts of Clean Air Actions.

A long-term (2013-2019) PM2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013-2017 and 2018-2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM2.5 air quality was observed in Tianjin with decreases in PM2.5 at an annual rate of 6.6%/y. PM2.5 from CC decreased by 4.1%/y. The reductions in SO2 concentration, PM2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO2, CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NOX emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NOX controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards.

Comparative analysis of nitrate evolution patterns during pollution episodes: Method development and results from Tianjin, China.

Particulate nitrate plays an increasingly important role in the formation of air pollution process, while the main mechanisms of nitrate concentration change are different in each stage, same as the driving factors. In this study, we proposed an episode-based analysis to illustrate the typical nitrate evolution patterns and identify the possible impacting factors in different evolution stages. Applying into twelve air pollution episodes, three typical patterns of nitrate evolution were abstracted, and the corresponding conceptual models were constructed. All the pollution episodes were grouped by their evolving shapes, which were driven by physical and chemical processes. Episodes started slowly typically arose from gradual pollutant accumulation, both locally and regionally, and chemical formation under high humidity. Type 1 ("hump-shaped type"), accounting for 66.3 % of the total episode durations, including two "peak" concentrations, displays a rapid growth rate which could up to 4.6 µg m-3 h-1 in average, mainly relying on the sharp drop in the planetary boundary layer height. Short scavenging processes and thoroughly dissipated stages of the pollution episodes always accompanied by strong north wind affected by Siberia-Mongolia cold current. Type 2 ("triangle-shaped type", 24.3 %) shows a gentle growth rate and short duration. Compared with Type 1, chemical process may be more important "source" for the increase of nitrate concentration during Type 2. Type 3 ("trapezoid-shaped type", 9.4 %) presents a long platform stage, during which high humidity (RH > 90 %) provides favorable conditions for wet removal and secondary production, and the updraft can carry pollutants to high altitude. The source and sink are roughly balanced for Type 3. Our study highlights the importance of pattern identification for understanding the nitrate evolution behavior, it may also provide insights for pollution prediction and scientific mitigation strategies.

The role of sources and meteorology in driving PM2.5-bound chlorine.

The role of chloride in atmospheric chemistry received increased attention over recent years. Given the primary and chemical-active nature of PM2.5-bound chlorine (p-Cl-), it makes sense to get to know the sources and processes of p-Cl-. The temporal behavior of observed p-Cl- concentration based on 1-h high resolution exhibited seasonal variation of high in winter, low in summer and diurnal variation of high in the morning, low in afternoon. Meteorological normalization technique based on random forest was used to disentangle the effects of emission changes which affected the seasonal variation and meteorology which was related to diurnal variation on p-Cl-. Generalized additive model (GAM) identified RH and temperature as the key meteorological factors of p-Cl- generation, and p-Cl- pollution was serious under the condition of low temperature and high RH. Dispersion-normalized positive matrix factorization (DN-PMF) was used to apportion the p-Cl- to its sources, finding that coal combustion was the main source of p-Cl-, followed by biomass burning and industrial process emissions. Our results will provide the basis for further analysis the causes of p-Cl- pollution and composite air pollution control strategies.

Variation in community structure and network characteristics of spent mushroom substrate (SMS) compost microbiota driven by time and environmental conditions.

Global mushroom production is growing rapidly, raising concerns about polluting effects of spent mushroom substrate (SMS) and interest in uses in composts. In this study, SMS composting trials and high-throughput sequencing were carried out to investigate to better understand how the structure, co-occurrence patterns, and functioning of bacterial and fungal communities vary through compost time and across environmental conditions. The results suggested that both bacterial and fungal microbiota displayed significant variation in community composition across different composting stages. Enzyme activity levels showed both directional and fluctuating changes during composting, and the activity dynamics of carboxymethyl cellulase, polyphenol oxidase, laccase, and catalase correlated significantly with the succession of microbial community composition. The co-occurrence networks are "small-world" and modularized and the topological properties of each subnetwork were significantly influenced by the environmental factors. Finally, seed germination and seedling experiments were performed to verify the biosafety and effectiveness of the final composting products.

Evaluating the performance of chemical transport models for PM2.5 source apportionment: An integrated application of spectral analysis and grey incidence analysis.

Evaluating the performance of source apportionment (SA) models is difficult due to the non-observable nature of source contribution in reality. Here we propose a new approach to assess the performance of Chemical Transport Models (CTMs) for SA based on wavelet time-frequency spectral analysis and Grey Incidence Analysis (GIA). For each source category, certain species that better reflect the periodic characteristics of the emission sources were selected as the chemical tracers. The consistency of the time series between the simulated source contributions and the observed source-specific chemical tracers was then examined using a GIA model based on the perspective of similarity, and characterized by the GIA scores. By applying this method to six typical pollution episodes, we evaluated the performance of the Comprehensive Air Quality Model with Extensions-Particle Source Apportionment Technology (CAMx-PSAT) model for PM2.5 SA from different temporal and spatial scales. The source- and episode-dependent optimal average time and main source regions were obtained. This approach is robust for facilitating a relatively meticulous evaluation of the performance of CTMs for PM2.5 SA, and provides additional insight for decision-making for heavy pollution emergencies.

[Main Problems and Refined Solutions of Urban Fugitive Dust Pollution in China].

Fugitive dust poses an important contribution to urban air particulate matter in China. To further improve the level of dust pollution prevention and control, the emission and contribution characteristics of urban fugitive dust were summarized; the main causes of dust pollution were analyzed; and the key links, key indicators, and main measures for prevention and control were clarified, so as to further improve the concept of "accurate dust control." Among all types of fugitive dust sources, road dust and construction dust were the main emission and contribution sources, among which road dust was more prominent. Production activities, vehicle disturbances, and wind erosion were the main dust-generating links of various dust sources. Silt loading was taken as the key control index for road dust prevention and control, whereas silt loading and bare soil (or material) areas were taken as the key control index for construction and other dust sources. Around the key indicators, three main ways to control the road dust and six main measures to control the construction and other dust sources were defined. In addition, some suggestions on the necessary supporting measures for dust control were put forward, so as to provide a comprehensive and beneficial reference for the practical application of dust control in Chinese cities.

Impact of sand and dust storms on the atmospheric environment and its source in Tianjin-China.

Sand and dust storms (SDS) frequently hit northern China and adversely impact both environment and health. The carbonaceous components, inorganic elements, water-soluble ions, and meteorological parameters of several severe SDS episodes have been measured in a supersite in Tianjin, which is a big and representative city located in SDS transmission pathway in northern China. Six SDS episodes were identified in Spring, 2021. The maximum PM10 mass concentration was 2684 and 1664 µg/m3 in SDS1 and SDS3, respectively. North and northwest wind was dominant and significant differences were found in wind speed and RH between the SDS and non-SDS episodes. North dust from Inner Mongolia and Mongolia was determined by back trajectory analysis as the probable source region. The mass concentration of SO42-, NO3-, and NH4+ decreased in PM2.5. Increase of Na+ and K+ and low SO42-SDS/ SO42-non-SDS indicate dust source for short length SDS transmission in northern China. The ratio of elements could also be used to distinguish SDS and non-SDS episodes identify north and northwest source for the SDS episodes. Pb/Al, Zn/Al, and Si/Al could be regarded as indicators for SDS and non-SDS episodes, Ca/Al and Ca/Si can help to indicate SDS source direction. This study provides a variety of evidences for the dust source identification and reveals the characteristics of the most severe SDS episodes of the decade in Tianjin during Spring 2021.

Nitrite removal by Acinetobacter sp.TX: a candidate of curbing N2O emission.

The nitrite removal pathway in Acinetobacter sp. TX5 was explored through the key gene identification and the corresponding enzyme purification, after which the capability to reduce nitrite by immobilized beads was investigated in a fixed-bed reactor. Results revealed that a nosZ gene encoding nitrous oxide reductase (N2OR) exists in TX5 cells, and a N2OR responsible for the reduction of N2O to N2 was purified successfully with a molecular weight of 70.05 kDa, a purification fold of 16.30 and a recovery rate of 5.17%. For TX5 immobilization, the optimal values of polyvinyl alcohol (PVA), spent mushroom substrate (SMS) and Aci (TX5) obtained by response surface methodology (RSM) were 6.32%, 2.92% and 4.57%, respectively. In a fixed-bed reactor packed with immobilized TX5, the removal efficiency (RE) achieved 90% (at 50 h) for NO2--N and 85% (at 96 h) for total nitrogen (TN). On the basis of these results, a nitrite removal pathway in TX5 was proposed. Overall, Acinetobacter sp. TX5 might be a promising candidate for nitrite removal with an ability to suppress N2O accumulation.

Size distribution and chemical characteristics of particles from crop residue open burning in North China.

Crop residue open burning is an important emission source of ambient particles in China. This study analyzed the particle emission characteristics of crop residue open burning through combustion experiments with a novel open combustion simulation device using three typical crop straws in north China (corn, wheat, and rice). Particle samples size ranging from 0.006-9.890 µm were collected by an Electrical Low Pressure Impactor plus, a high size-resolution instrument capable of dividing particles into 14 size stages. The size distributions of organic carbon (OC), elemental carbon (EC), water-soluble ions, and elements were analyzed, and source chemical profiles were constructed for PM0.1, PM1, PM2.5, and PM10. The number concentration of particles was concentrated in the Aiken nuclei mode (0.006-0.054 µm), accounting for 75% of the total number, whereas the mass concentration was concentrated in the accumulation mode (0.054-0.949 µm), accounting for 85.43% of the mass loading. OC, EC, Cl-, and K(include total K and water-soluble K) were the major chemical components of the particles, whose mass percentage distributions differed from those of other components. These five main components exhibited a bell-shaped size distribution in the 0.006-9.890 µm range, whereas the other components exhibited a U-shaped distribution. Among the chemical profiles for PM0.1-PM10, OC was the most important component at 10-30%, followed by EC at 2%-8%. The proportions of K+, Cl-, and K varied substantially in different experimental groups, ranging from 0-15%, and K+ and Cl- were significantly correlated (r = 0.878, α = 0.000).