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Particle-bound elements have attracted increasing attentions due to their health effects and atmospheric catalytic reactivity. However, elements in atmospheric fine particulate matter (PM2.5) have not been well investigated even in some highly polluted area. In this study, 22 elements in PM2.5 were measured by a multi-metal monitor in ten prefecture-level and county-level cities in Hebei province, one of the most polluted provinces in China, during the heating and non-heating seasons. Source apportionment of PM2.5-bound elements were conducted, and health risks of individual elements and different sources were assessed. The results showed that, total elements (TEs) measured contributed to 2%-7% of the PM2.5 mass, with potassium (K), calcium (Ca), iron (Fe), and zinc (Zn) as the most abundant elements, accounting for about 71%- 87% of TEs mass. Concentrations of chromium (Cr), arsenic (As), and cadmium (Cd) were more likely to exceed the World Health Organization (WHO) limits. Source apportionment results indicated that PM2.5-bound elements were primarily from coal combustion, dust, traffic, ferrous metal smelting and oil combustion, and other industrial related sources. Therein, ferrous metal smelting and oil combustion, coal combustion and industry were the predominant source of Cr, As and Cd, respectively. Health risk assessment indicated that the carcinogenic and non-carcinogenic risks of As for children could exceed the precautionary criteria, and coal combustion source had the highest carcinogenic and non-carcinogenic risks. This study suggested that attentions should be paid not only on PM2.5 mass but also PM2.5-bound compounds especially heavy metals and metalloids to reduce health risks in the future.

Gossypium hirsutum races are believed to be potential reservoirs of desirable traits, which can play crucial roles to overcome the existing narrow genetic base of modern Upland cotton cultivars. However, prior to utilizing the races in cotton improvement programs, understanding their genetic constitutions is needed. Thus, this study used molecular and morphological techniques to characterize 110 G. hirsutum germplasm including 109 semi-wild accessions and one Upland cotton cultivar, CRI12. In the study, 104 SSR markers detected 795 alleles, with an average of 7.64 alleles per marker, ranging from 3 to 14, and average polymorphism information content (PIC) value of 0.71. And 96 of the markers were found to be highly informative, with PIC value≥0.50. Pairwise genetic similarity coefficient across the accessions ranged from 0.19 to 1.00, with an average value of 0.46. Morphological characterization was done using fiber length, fiber strength, micronaire, fiber uniformity index, and fiber elongation. Pairwise taxonomic distance within the accessions ranged from 0.17 to 3.41, with a mean of 1.33. The SSR and fiber quality traits data set based unweighted pair group method of arithmetic mean (UPGMA) analysis grouped the accessions into 7 and 12 distinct clusters, respectively, that corresponds well with the results of principal component analysis (PCA). Our study revealed the existence of vast molecular and morphological diversities within the accessions and provided valuable information on each semi-wild accession for quick and better informed germplasm utilization in cotton breeding programs.

In plants, aldehyde dehydrogenases (ALDHs) function as 'aldehyde scavengers' by removing reactive aldehydes and thus play important roles in stress responses. To date, 30 ALDHs have been identified in Gossypium raimondii, whereas ALDHs have not been studied in Gossypium arboreum or in tetraploid cotton. In this study, we identified 30, 59 and 59 aldehyde dehydrogenase (ALDH) genes from G. arboreum, G. hirsutum and G. barbadense, respectively. Gene structure analysis revealed that members of the same family exhibit similar exon-intron structures and structural domains, and all members of the ALDH18 family possess a distinct AA-kinase domain. Synteny analysis showed that segmental and tandem duplications have played an important role in the expansion and evolution of ALDHs in cotton. Phylogenetic and synteny analysis between G. arboreum and G. raimondii demonstrated that all GaALDHs and GrALDHs are orthologous and that most GaALDHs are located in syntenic blocks corresponding to those of G. raimondii, implying that these genes appeared before the divergence of G. arboreum and G. raimondii and that no expansion of the ALDH superfamily has occurred in these two cotton species. Quantitative real-time PCR analysis revealed that the majority of GaALDHs and GhALDHs are up-regulated under conditions of high salinity and drought, indicating that these genes may be stress responsive. The findings of this study, based on genome-wide identification of ALDHs in Gossypium and analysis of their evolution and expression, provide a foundation for further analysis of ALDHs and suggest potential target genes for improving stress resistance in cotton.

In recent years, low-cost air sensors designed to measure ambient particulate matters and trace gases have been changing the world of air quality monitoring. Although low-cost sensors have the capability of gathering high temporally and spatially-resolved air quality information, they tend to have more severe data quality issues compared to the conventional monitoring instruments. To enhance the data quality of low-cost sensors, different calibration methods have been developed. In this study, based on the laboratory and field data collected in mainland China, the performances of PM2.5, PM10, CO, NO2, SO2, and O3 sensors were estimated. Then, the sensors were calibrated by different methods and their output data were compared to the reference data to determine the validity and performance of these calibration methods. The results indicated that 1) field calibration with supervised learning technique is an effective technique to decrease the error, and 2) mobile air quality monitoring stations can calibrate the sensors at where standard air quality monitoring stations are not available. In conclusion, a complete laboratory and field calibration system (four-stage calibration) covering the whole life cycle of the sensor is proposed in this paper.