[Estimation of Near-surface Ozone Concentration in the Beijing-Tianjin-Hebei Region Based on XGBoost-LME Model].

High spatiotemporal resolution data on near-surface ozone concentration distribution is of great significance for monitoring and controlling atmospheric ozone pollution and improving the living environment. Using TROPOMI-L3 NO2, HCHO products, and ERA5-land high-resolution data as estimation variables, an XGBoost-LME model was constructed to estimate the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. The results showed that： ① Through correlation analysis, surface 2 m temperature （T2M）, 2 m dewpoint temperature （D2M）, surface solar radiation downwards （SSRD）, tropospheric formaldehyde （HCHO）, and tropospheric nitrogen dioxide （NO2） were important factors affecting the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. Among them, T2M, SSRD, and D2M had strong correlations, with correlation coefficients of 0.82, 0.75, and 0.71, respectively. ② Compared with that of other models, the XGBoost-LME model had the best performance in terms of various indicators. The ten-fold cross-validation evaluation indicators R2, MAE, and RMSE were 0.951, 9.27 µg·m-3, and 13.49 µg·m-3, respectively. At the same time, the model performed well at different time scales. ③ In terms of time, there was a significant seasonal difference in near-surface ozone concentration in the Beijing-Tianjin-Hebei Region in 2019, with the concentration changing in the order of summer > spring > autumn > winter. The monthly average ozone concentration in the region showed an inverted "V" trend, with a slight increase in September. The highest value occurred in July, whereas the lowest value occurred in December. In terms of spatial distribution, the near-surface ozone concentrations in the Beijing-Tianjin-Hebei Region during the months of February and March were generally at the same levels. In January, November, and December, there was a relatively insignificant trend of higher concentrations in the north and lower concentrations in the south. For the remaining months, the spatial distribution of near-surface ozone concentrations in this area predominantly exhibited a pattern of higher concentrations in the south and lower concentrations in the north. High-value areas were predominantly found in the plain regions of the southern part with lower altitudes, dense population, and higher industrial emissions； low-value areas, on the other hand, were primarily located in mountainous areas of the northern part with higher altitudes, sparse population, higher vegetation coverage, and lower industrial emissions.

[Estimation of PM2.5 Hourly Concentration in Sichuan Province Based on GTWR-XGBoost Model].

Aerosol optical depths of satellites and meteorological factors have been widely used to estimate concentrations of surface particulate matter with an aerodynamic diameter ≤ 2.5 µm. Research on a high time resolution and high-precision PM2.5 concentration estimation method is of great significance for timely and accurate air quality prediction and air pollution prevention and mitigation. Himawari-8 AOD hour product and ERA5 meteorological reanalysis data were used as estimation variables, and a GTWR-XGBoost combined model was proposed to estimate hourly PM2.5 concentration in Sichuan Province. The results showed that:① the performance of the proposed combination model was better than that of the KNN, RF, AdaBoost, GTWR, GTWR-KNN, GTWR-RF, and GTWR-AdaBoost models in the full dataset; the fitting accuracy indexes R2, MAE, and RMSE were 0.96, 3.43 µg·m-3, and 5.52 µg·m-3, respectively; and the verification accuracy indexes R2, MAE, and RMSE were 0.9, 4.98 µg·m-3, and 7.92 µg·m-3, respectively. ② The model had a high goodness of fit (R2 of the whole dataset was 0.96, and R2 of different times ranged from 0.91 to 0.98) when applied to the estimation of PM2.5 concentration hour. It showed that the model had good time stability for hourly estimation and could provide accurate estimation information for regional air quality assessment. ③ In terms of time, the annual average PM2.5hourly concentration estimation showed an inverted U-shaped trend. It began to increase gradually at 09:00 am to a peak of 44.56 µg·m-3 at 11:00 and then gradually decreased. Moreover, the seasonal variation was very obvious, with winter>spring>autumn>summer. ④ In terms of spatial distribution, it showed the characteristics of high in the east and low in the west and a high degree of local pollution.

Differential protein expression in Phalaenopsis under low temperature.

A comparative proteomic analysis was carried out to explore the molecular mechanisms of responses to cold stress in Phalaenopsis after treated by low temperature (13/8 °C day/night) for 15 days. Differentially expressed proteins were examined using two-dimensional electrophoresis (2-DE) and matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-TOF/MS). Among 85 differentially expressed proteins, 73 distinct proteins were identified. Comparative analysis revealed that the identified proteins mainly participate in photosynthesis, protein synthesis, folding and degradation, respiration, defense response, amino acid metabolism, energy pathway, cytoskeleton, transcription regulation, signal transduction, and seed storage protein, while the functional classification of the remaining four proteins was not determined. These data suggested that the proteins might work cooperatively to establish a new homeostasis under cold stress; 37 % of the identified cold-responsive proteins were associated with various aspects of chloroplast physiology, and 56 % of them were predicted to be located in the chloroplasts, implying that the cold stress tolerance of Phalaenopsis was achieved, at least partly, by regulation of chloroplast function. Moreover, the protein destination control, which was mediated by chaperones and proteases, plays an important role in tolerance to cold stress.

Evaluation of internal control for gene expression in Phalaenopsis by quantitative real-time PCR.

The selection of appropriate reference genes is one of the most important steps to obtain reliable results for normalizing quantitative real-time reverse transcriptase polymerase chain reaction (qRT-PCR) of MADS-box gene in Phalaenopsis. In this study, we cloned 12 candidate reference genes including 18S ribosomal RNA (18S), elongation factor 1 alpha (EF1α), cytoskeletal structural protein actin (ACT1, ACT2, ACT3, ACT4, ACT5), ubiquitin protein (UBQ1 and UBQ2), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and the cytoskeletal structural proteins α-tubulin (TUA) and ß-tubulin (TUB) in Phalaenopsis and evaluated their expression reliability. The expression of these candidate reference genes was analyzed using geNorm and normFinder software packages; the results showed that ACT2 and ACT4 were the highest stability reference genes for all experiment sets based on normFinder, followed by ACT1 or ACT3, while ACT3 and ACT4 were the highest stability reference genes for most experiment sets based on geNorm, then TUB or others. Taken together, Actin genes were the higher stability reference genes for all tissues at total developmental stages, and similar results came from analysis by normFinder. According to geNorm analysis, ACT3 and ACT4 were the most stable reference genes for all tissues tested and tissues at reproductive stages; TUB and ACT5 or ACT4 were the most stable reference genes for vegetative tissues or roots. The most stable reference genes for all vegetative tissues and only leaves were ACT4 and ACT5, ACT2 and ACT3, respectively; ACT1 and ACT3 were the most stable genes and sufficient for reliable normalization of flower tissues. While EF1α, UBQ1, UBQ2, and GAPDH were found to be unsuitable as a reference gene in our analysis for flower tissues, total tissues, and reproductive stages; UBQ2 and 18S were identified as the least stable reference genes for vegetative tissues at different stages, different tissues at vegetative stages; TUA and 18S were the least reliable reference genes for the samples from roots at all developmental stages. This is the first systematic report on the selection of reference genes in Phalaenopsis, and these data will facilitate future work on gene expression in orchid.