Northern Henan Province Research Articles

Short-term exposure to air pollution on peripheral white blood cells and inflammation biomarkers: a cross-sectional study on rural residents

Effects of short-term exposure to ambient air pollution on systemic immunological and inflammatory biomarkers in rural population have not been adequately characterized. From May to July 2021, 5816 participants in rural villages of northern Henan Province, China, participated in this cross-sectional study. Blood biomarkers of systemic inflammation were determined including peripheral white blood cells (WBC), eosinophils (EOS), basophils (BAS), monocytes (MON), lymphocytes (LYM), neutrophils (NEU), neutrophil-lymphocyte ratio (NLR), and serum high-sensitivity C-reactive protein (hs-CRP). The concentrations of ambient fine particulate matter (PM2.5), PM10, nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were assessed up to 7 days prior to the blood draw. A generalized linear model was used to analyze the associations between air pollution exposure and the above-mentioned blood biomarkers. Significantly positive associations were revealed between PM2.5, CO and WBC; CO, O3 and LYM; PM2.5, PM10, SO2, CO and NEU; PM2.5, PM10, SO2, CO and NLR; PM2.5, PM10, SO2, NO2, CO, O3 and hs-CRP. Meanwhile, negative associations were found between SO2 and WBC; PM2.5, PM10, NO2, CO, or O3 and EOS; PM2.5, SO2, or CO and BAS; SO2, NO2 or O3 and MON; PM2.5, PM10, SO2, or NO2 and LYM. Moreover, men, individuals with normal body mass index (BMI), current smokers, and those older than 60 years were found vulnerable to air pollution effects. Taken together, short-term exposure to air pollution was associated with systemic inflammatory responses, providing insight into the potential mechanisms for air pollution-induced detrimental systemic effects in rural residents.

Monitoring Analysis of Urban Subsidence in Northern Henan Province Based on TS-InSAR Technology

The protracted and pervasive incidence of land subsidence emerges as a pivotal factor exerting a substantial impact on the sustainable development of urban landscapes. A nuanced comprehension of the spatiotemporal evolution characteristics of land subsidence within the Northern Henan Plain assumes paramount significance in the context of mitigating potential urban geological disasters. This study endeavors to redress the deficiency in information concerning the temporal and spatial evolution characteristics of enduring deformation in cities within the northern plain of Henan Province. To this end, the authors leveraged Sentinel-1A radar data processed through persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to elucidate the distribution patterns of ground deformation and temporal evolution characteristics within the expansive 24-scene coverage research area. Empirical findings illuminate conspicuous surface deformation in Anyang, Puyang, and Hebi throughout the monitoring period. Spatially, land subsidence in the study area predominantly clusters in the suburban peripheries of the cities, with Hebi and Puyang registering a maximum subsidence rate exceeding 25 mm per annum. Temporally, land subsidence manifests predominantly during autumn and winter, whereas spring and summer display relatively stable land subsidence interspersed with a slight ground uplift. In order to rectify the spatial disparities observed between leveling data and PS-InSAR monitoring data, this experiment employed an averaging procedure on the PS-InSAR monitoring data, subsequently subjecting it to comparative analysis with the leveling data. Additionally, through the integration of the singular spectrum analysis (SSA) method and the time series deformation model, this study aspires to attain a comprehensive understanding of the temporal dynamics manifested in the PS-InSAR monitoring outcomes, while concurrently elucidating the factors influencing the observed deformations. Ultimately, this analysis discloses that the monitoring outcomes derived via PS-InSAR technology exhibit a root mean square error of ±12.9 mm and a standard deviation of ±13.31 mm. These statistical metrics furnish valuable insights into the precision and consistency of the PS-InSAR monitoring data. Drawing upon a comparative scrutiny of on-site data and historical remote sensing imagery within the study area, it has been discerned that excessive groundwater extraction and expansive surface engineering initiatives stand as the principal instigators of land subsidence in the research domain. Consequently, this experiment assumes the role of a salient reference for the mitigation of urban ground subsidence within the study area.

A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China

Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research.

Analysis of the information ability of nurses in county hospitals in northern Henan province: a survey

ObjectiveTo investigate the status quo of nursing information ability of nurses in county-level hospitals and analyze its influencing factors.MethodsIn June 2022, a total of 303 on-the-job clinical nurses from 3 county-level hospitals in Hebi City, Henan Province were selected as subjects by convenience sampling method. General data questionnaire and self-rating nursing information ability scale were used to investigate them.ResultsThe total score of nursing information ability of 303 nurses in county hospitals of Hebi City, Henan Province was (77.72 ± 18.76). There were statistically significant differences in the scores of nursing information ability among different ages, working years, positions, education, marriage, monthly income, whether they had learned computer-related knowledge and skills, and whether they had participated in the learning or training of nursing information system (all p < 0.05).Multiple linear regression analysis showed that age, years, position, monthly income and whether they had learned computer-related knowledge and skills were the main influencing factors of nursing information ability of county-level nurses (all p < 0.05).ConclusionsThe nursing information ability of nurses in county-level hospitals in northern Henan is at a medium level. The government or society should provide training and guidance on nursing information ability, so as to provide more opportunities for nurses in county-level hospitals to participate in and learn nursing information technology to improve their ability.

Modeling potential arsenic enrichment and distribution using stacking ensemble learning in the lower Yellow River Plain, China

In the high arsenic area of the northern Henan Plain in the lower Yellow River, the interaction between surface water and groundwater is frequent due to the control of groundwater over exploitation and the ecological replenishment of the rivers. It is not clear that what leads to the cause and dynamic change in the mechanism of arsenic pollution in shallow groundwater. Using machine learning algorithms to model the risk of high arsenic occurrence in the groundwater can help analyze mechanism of arsenic in groundwater. In this study, a stacking ensemble learning model was constructed to predict the risk distribution of shallow high arsenic groundwater in the lower reaches of the Yellow River in northern Henan Province using multi-variate parameters. Furthermore, we analyzed the potential areas at risk of high arsenic enrichment under different groundwater levels by control. The results show that the groundwater arsenic exceedance rate was 16.76%, and the high arsenic groundwater distributed from northeast to southwest, while that in the middle and south mainly distributed in the alluvial pre-fan depressions and the Yellow River crevasse splay. Compared with the single model, the stacking ensemble learning model has the Area Under the Curve (0.87), accuracy (0.82), and specificity (0.88) and sensitivity (0.77). With the best overall performance, the predicted risk distribution of groundwater arsenic is highly consistent with the observed results, and the potential groundwater area with high arsenic risk accounts for 19.67% of the total area. The impact of the Yellow River burst, average annual temperature, annual precipitation, ground elevation, and hydraulic gradient as the most significant indicator factors are affecting groundwater arsenic enrichment in the study area. Among these factors, the sedimentary environment accounts for 27.10% in the process of arsenic enrichment. Groundwater also plays an important role, with a relative share of 13.69%. In the simulation of groundwater rebound process, it was found that when the groundwater level raised in the range of 1 m −3 m, the high arsenic area could be reduced by up to 19.52%. The methods and findings in this work can support the management and control of arsenic enrichment and distribution in shallow groundwater systems around the world.

Comparison and implications of the carbonaceous fractions under different environments in polluted central plains in China: Insight from the lockdown of COVID-19 outbreak

Before and during the COVID-19 outbreak in the heated winter season of 2019, the carbonaceous fractions including organic carbon (OC), elemental carbon (EC), OC1–4, and EC1–5 were investigated between normal (November 1, 2019, to January 24, 2020) and lockdown (January 25, to February 29, 2020) periods in polluted regions of northern Henan Province. In comparison to urban site, four rural sites showed higher concentrations of carbonaceous components, especially secondary OC (SOC); the concentration of SOC in rural sites was 1.5–3.4 times that in the urban site. During the lockdown period, SOC in urban site decreased slightly, while it increased significantly in rural sites. NO2 has a significant effect on SOC generation, particularly in normal period when NO2 concentrations were high. Nevertheless, NO2 significantly decreased, and the elevated O3 (increased by 103–138%) contributed considerably to the generation of SOC during lockdown. Relative humidity (RH) promoted SOC production when RH was below 60%, but SOC was negatively correlated or uncorrelated with RH when RH exceeded 60%. Additionally, RH has a more pronounced effect on SOC during lockdown. The contribution of gasoline vehicle emissions decreases significantly in both urban and rural sites (3–12%) due to the significant reduction of anthropogenic activities during lockdown, although the urban site remained with the biggest contributions (37%). These results provide innovative insights into the variations in carbonaceous aerosols and SOC generation during the unique time when anthropogenic sources were significantly reduced and illustrate the differences in pollution characteristics and sources of carbonaceous fractions in different environments.

A simple, rapid method for simultaneous determination of multiple elements in serum by using an ICP-MS equipped with collision cell

We developed an inductively coupled plasma mass spectrometry method for testing 23 elements, namely, Mg, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Sn, Sb, Ba, W, Tl, Pb, and U, in human serum. The serum samples were analyzed after diluting 1/25 with 0.5% nitric acid, 0.02% Triton-X-100, and 2% methanol. Sc, In, Y, Tb, and Bi were assigned internal standards to correct the baseline drift and matrix interference. The kinetic energy discrimination mode of the instrument with helium gas as the collision gas eliminated polyatomic interference. All 23 elements exhibited excellent linearity in their testing range, with a coefficient of determination ≥ 0.9996. The limits of detection of the 23 elements were within the range of 0.0004–0.2232 µg/L. The intra- and inter-day precision (relative standard deviation) were < 12.19%. The recoveries of the spiked standard for all elements were 88.98–109.86%. Among the 23 elements of the serum reference materials, the measured results of Mg, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Se were within the specified range of the certificate, and the results of the other elements were also satisfactory. The developed method was simple, rapid, and effective, and only 60 μL sample was consumed. A total of 1000 serum samples from healthy individuals were randomly selected from the Henan Rural Cohort, which reflects the status of serum elements in rural adults from the Northern Henan province of central China.

Effects on Frequency Stability of Power System for Photovoltaic High-Penetration Ratio Grid-Connected Power Generation

In this paper, the effects of three typical operation modes, namely short-circuit fault, load change, and chemical energy storage on the frequency of a regional power grid after photovoltaic asynchronous interconnection were studied with different penetration ratios, taking the power grid in Northern Henan Province as the research object. It was found that with an increase in the photovoltaic penetration ratio, the maximum value of the system frequency and the fluctuation amplitude gradually increased, and the power grid system in Northern Henan became less and less stable. With an increase in the penetration ratio, the peak value of the system frequency at the corresponding node gradually increased, and the valley value gradually decreased. With an increase in load, the peak value of the frequency curve gradually increased, and the valley value gradually decreased. When photoelectricity was connected to the grid through chemical energy storage, the system stability during a short-circuit fault and load change operation was significantly improved. Compared with that before energy storage, the frequency amplitude of the system after energy storage was reduced to approximately one tenth of the original. Compared with the case before energy storage, when the load changed, the frequency amplitude of the system decreased to approximately a quarter of the original.

Differences in PM2.5 Components Between Urban and Rural Sites During Heavy Haze Event in Northern Henan Province

In recent years, the Beijing-Tianjin-Hebei region and its surrounding areas have experienced multiple haze pollution processes. Owing to the limitation of observational instruments, there has not been a comparative study of haze pollution between urban and rural areas in northern Henan province. A series of high-time-resolution instruments were used during a regional heavy pollution process (January 12-25, 2018) at two urban sites and three rural sites. The results showed that SO42-, NO-3, and NH+4 (SNA) were the components with the highest proportion in PM2.5 at the five sites during the haze event with a range of 53%-63%, of which nitrate was the most important, accounting for 24%-32%, followed by sulfate, ranging from 13%-17%. Compared with urban sites, rural sites were more affected by organic matter, especially at night. With the aggravation of pollution, the proportion of SNA increased, reaching 67% during periods of heavy pollution. When the area was affected by the air mass transported from the south, the proportion of NO-3 in PM2.5 increased, and when the area was affected by the air transport in the north, the proportions of SO42- and organic matter increased. Ammonium nitrate was the most important component that led to the decrease in atmospheric visibility during the haze process. Moreover, the contributions of ammonium nitrate and ammonium sulfate at the urban sites were higher than those at the rural sites. To summarize, there were significant differences in PM2.5 components between the urban and rural sites. Urban areas need to continue to strengthen the reduction in gaseous precursors, and rural areas need to pay attention to the sources of carbonaceous aerosol.

Monitoring the Catastrophic Flood With GRACE-FO and Near-Real-Time Precipitation Data in Northern Henan Province of China in July 2021

Zhengzhou and its surrounding areas, located in northern Henan Province, China, receive continuous extreme rainfall from July 17 to July 22, 2021. Northern Henan Province experiences extensive flash floods and urban floods, causing severe casualties and property damage. Understanding the variation of hydrologic features during this flood event could be valuable for future flood emergency response work and flood risk management. This study first demonstrates the rainstorm process based on near-real-time precipitation data from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0). To meet the temporal resolution required for monitoring this short-term flood event, reconstructed daily terrestrial water storage anomalies (TWSAs) based on GRACE and GRACE-FO data and CLDAS-V2.0 datasets are first introduced. The spatial and temporal evolution of the reconstructed daily TWSA is analyzed in the study area during this heavy rainfall event. We further employ a wetness index based on the reconstructed daily TWSA for flood warnings. Furthermore, the modeled soil moisture data and daily runoff data are used for flood monitoring. Results show that the reconstructed daily TWSA increases by 437.7 mm in just six days (from July 17 to July 22, 2021), with a terrestrial water storage increment of 9.4 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Compared with ITSG-Grace2018, the reconstructed daily TWSA has better potential for near-real-time flood monitoring for short-term events in a small region. The wetness index derived from reconstructed daily TWSA is potential for flood early warning.

The Phonetic Analysis of Z Rime Alternation Phenomena in Changyuan Dialect of Henan Province

By analyzing the Z rime alternation phenomena in Changyuan Dialect of Henan Province, the present study argues that the phenomenon of one word having two different kinds of Z rime alternation forms in this dialect is due to it that the basic rime of the word preserves the Z rime alternation form that is formed before and after the sound change of the basic rime respectively. At the mean time, the development speed of Z rime alternation forms is slower than that of the basic rimes, so the Z rime alternation form preserves the glide of its corresponding basic rime of the early period and that’s exactly what leads to the differences between the glide of the present basic rime and that of the corresponding Z rime alternation form. Besides, the monophthongization phenomenon occurs between the main vowel and the [-u] ending in the compound final that is formed by sound combination during Z rime alternation process generally appears only in Jin language area of Northern Henan Province and doesn’t appear in Mandarin-speaking area of Henan Province.

Molecular prevalence and subtypes distribution of Blastocystis sp. among outpatients and inpatients in north and south areas of Henan Province, China.

Blastocystis sp. is one of the most common intestinal parasites in humans and many animals. To further understand the infection of Blastocystis hominis (B. hominis) and the distribution of its genotype in some areas of Henan Province, China, 793 stool samples from outpatients and inpatients in Xinxiang City and Xinyang City, Henan Province were collected from April 2020 to July 2022. The samples were detected by polymerase chain reaction and analyzed by univariate analysis and logistic regression analysis. The results showed that the infection rates of B. hominis in Xinxiang and Xinyang were 10.97% (51/465) and 10.98% (36/328), respectively. Although there were no significant differences in B. hominis infection between gender, age, residence, and disease background, the incidence of hematochezia significantly differed from the incidence of abdominal pain, diarrhea, and constipation among participants (χ2 =15.795, p=0.002). A total of 87 positive samples were sequenced and compared with Basic Local Alignment Search Tool, and five subtypes (ST1, ST3, ST4, ST6, and ST7) were identified, of which ST3 was the dominant subtype (63.22%, 55/87), followed by ST7 (17.24%, 15/87) and ST1 (16.09%, 14/87). This is the first study that analyzed the prevalence and subtype distribution of B. hominis in southern and northern Henan Province, thus providing new insights into the epidemiology of B. hominis.

Analysis of Ozone Pollution Characteristics and Transport Paths in Xi’an City

Xi’an is a key city for air-pollution prevention and control in China, and its near-ground ozone (O3) pollution has become a key issue whose resolution is urgent. The spatial and temporal variations in ozone pollution and their relationship with meteorological factors, transport pathways, and potential source distribution in Xi’an City were investigated in this study using the backward trajectory clustering analysis, potential source contribution function (PSCF), and concentration weight trajectory (CWT) methods coupled with the hourly ozone-mass concentration observations and meteorological data from 2014 to 2020. The results indicate that Xi’an City has suffered from increasingly severe ozone pollution in recent years. Overall, the annual average mass concentration of O3_8h_max presented an upward trend, exhibiting a 34.94% increase from 2014 to 2020. A seasonal variation peak occurred in summer, and the monthly variations featured an inverted “V” shape. Furthermore, the diurnal variation was significantly affected by the near-surface atmospheric photochemical process, showing a discernible single-peak and single-valley distribution with a peak between 15:00 and 17:00. The O3 concentration demonstrated a significant linear positive correlation with surface temperature and sunshine duration. When the relative humidity was 30~50%, the temperature was above 30 °C, and the wind speed was ≤4 m/s, high-concentration O3 pollution was liable to occur. The transmission mainly relied on short-distance airflow during periods of heavy O3-polluted weather. In this context, in addition to local O3 generation, high O3 concentrations were also affected by the large NOx and VOC emissions from heavy industries in neighboring cities in Shaanxi Province, southwest Shanxi Province, and northern Henan Province. Therefore, joint prevention and control measures on the O3 pollution in Xi’an City must be adopted throughout the Fenwei Plain area with the aim of strictly controlling the emissions from regional pollution sources.

Holocene landscape evolution in northern Henan Province and its implications for archaeological surveys

AbstractThe heartlands of many of the world's civilizations are situated within alluvial plains, where thick alluvial sediments obscure much of the archaeological record. However, the use of alluvial geoarchaeology remains patchy, particularly in the world's largest alluvial basins. We present results from our geoarchaeological survey at Neihuang County, Henan Province, China, as an example for alluvial geoarchaeological research in the North China Plain and to develop a generalized framework for landscape evolution in the area during the Holocene. We reconstruct the alluvial history of the area around Neihuang County by synthesizing stratigraphic data from seven outcrops into distinct depositional units. Our findings suggest that much of the archaeological record in the North China Plain is buried by meters of sediment or eroded away by the ancient channels of the Yellow River and other tributary streams. Therefore, the presence of buried archaeological sites and river scour in recorded outcrops suggests that the nonsystematic archaeological surveys that are commonly used to interpret cultural changes are not accurate reflections of archaeological site distributions. From the results of this case study, we recommend that archaeologists and paleoclimatologists should exercise more caution when using settlement distribution data gathered through nonsystematic pedestrian surveys to make inferences about ancient processes of cultural change or social dynamics in the North China Plain.

Labechiid stromatoporoids from the Middle Ordovician Machiakou Formation of North China and their implications for the early development of stromatoporoids

The Darriwilian stromatoporoid assemblage of North China represents a critical stage in early stromatoporoid development across the peri-Gondwanan regions, reaching the highest generic diversity than elsewhere as part of the Great Ordovician Biodiversification Event. We describe four labechiid stromatoporoid species, Labechia variabilis Yabe & Sugiyama, 1930, Labechiella mingshankouensis (Ozaki, 1938), Thamnobeatricea suxianensis (Dong, 1982), and Sinodictyon columnare Yabe & Sugiyama, 1930 from the Middle Ordovician Machiakou Formation in the Xiaonanhai section near Anyang City in northern Henan Province of China. The conodont Plectodina onychodonta occurs in association with these labechiids, implying a middle to late Darriwilian age. This assemblage extends the stratigraphic range of early stromatoporoids in North China to as early as the middle Darriwilian, thus slightly older than the previously known late Darriwilian earliest records. Mutual encrustations of Labechia variabilis and Labechiella mingshankouensis in the present assemblage constructed a microbioherm, together with unidentified tubular organisms, Ortonella-like calcimicrobes, a tubeworm-like organism, and borings. The assemblages of the Darriwilian labechiid stromatoporoids vary from place to place palaeogeographically in the shallow epicontinental marine environment of North China, i.e., in a patchy manner. The significant diversification of Darriwilian labechiid stromatoporoids in North China was likely promoted by a combination of environmental and ecological factors, which include: (1) the occurrence of a very shallow epicontinental marine environment, (2) a possible increase in the carbonate saturation state in seawater, (3) favourable seawater temperature for stromatoporoids during the Middle Ordovician and (4) the scarcity of bryozoans and corals as ecological competitors in North China.

Using time series vector features for annual cultivated land mapping: A trial in northern Henan, China.

Annual monitoring of the spatial distribution of cultivated land is important for maintaining the ecological environment, achieving a status quo of land resource management, and guaranteeing agricultural production. With the gradual development of remote sensing technology, it has become a common practice to obtain cultivated land boundary information on a large scale with the help of satellite Earth observation images. Traditional land use classification methods are affected by multiple types of land cover, which leads to a decrease in the accuracy of cultivated land mapping. In contrast, although the current advanced methods (such as deep learning) can obtain more accurate cultivated land mapping results than traditional methods, such methods often require the use of a massive amount of training samples, large computing power, and highly complex model tuning processes, increasing the cost of mapping and requiring the involvement of more professionals. This has hindered the promotion of related methods in mapping institutions. This paper proposes a method based on time series vector features (MTVF), which uses vector thinking to establish the features. The advantage of this method is that the introduction of vector features enlarges the differences between the different land cover types, which overcomes the loss of mapping accuracy caused by the influences of the spectra of different ground objects and ensures the calculation efficiency. Moreover, the MTVF uses a traditional method (random forest) as the classification core, which makes the MTVF less demanding than advanced methods in terms of the number of training samples. Sentinel-2 satellite images were used to carry out cultivated land mapping for 2020 in northern Henan Province, China. The results show that the MTVF has the potential to accurately identify cultivated land. Furthermore, the overall accuracy, producer accuracy, and user accuracy of the overall study area and four sub-study areas were all greater than 90%. In addition, the cultivated land mapping accuracy of the MTVF is significantly better than that of the maximum likelihood, support vector machine, and artificial neural network methods.

Characteristics of PM2.5 Pollution in Beijing During the Historical Period of the 2022 Olympic Winter Games

Based on the dataset derived from January to March between 2015 and 2021 in Beijing, the PM2.5 pollution characteristics and its potential source regions during the historical period of the Beijing 2022 Olympic Winter Games and Paralympic Winter Games were investigated. From 2015 to 2018, both the number of severely polluted days (daily average ρ(PM2.5)>75 μg·m-3) and the average PM2.5 concentrations during severe pollution episodes decreased significantly in the period of January to March. While, neither variable has changed obviously since 2018. On average, severely polluted days occurred 23 times in each year between 2018 and 2021 during the period of January to March, and the average of ρ(PM2.5) was approximately 120.0 μg·m-3 during such polluted days. From January to March in 2015-2021, the severely polluted event with more than 5 consecutive polluted days occurred 2-3 times in each year, and the severest one lasted 8 d. During the historical period of the Beijing 2022 Olympic Winter Games, severely polluted days took place 2-9 d every year. The large quantities of fireworks during the Spring Festival maybe one of important primary sources of the PM2.5. The number of severely polluted days during the historical period of the Paralympic Winter Games ranged from 1 to 5 d, except for 2021 with 9 d owing to the frequent stagnant weather condition. The PM2.5 chemical composition was dominated by secondary species on severely polluted days during the historical period of the Beijing 2022 Olympic Winter Games and Paralympic Winter Games. Nitrate accounted for 46% of the measurable chemical components of PM2.5 during severe pollution events in 2020, which was remarkably higher than that during clean days in the same year (11%). The mass fraction of SO42- ranged from 12% to 19% in 2018-2020, indicating that the contribution of sulfate was much less, but cannot be ignored. The main potential source regions of PM2.5 in Beijing during the period concerned in this study were central and western Inner Mongolia, Hebei Province, Tianjin City, Shanxi Province, Shaanxi Province, central and western Shandong Province, and northern Henan Province.

Analysis of PM2.5 Transmission Characteristics in Main Cities of Jinzhong Basin in Winter

In this study, we analyzed the hourly concentration data of PM10 and PM2.5 in major cities in Jinzhong basin from 2017 to 2019. The main distribution characteristics of aerosols in Jinzhong and Taiyuan were determined, and PM2.5 hourly concentration data and HYSPLIT in Jinzhong basin in winter were discussed. The results showed that the overall level of particulate matter concentration in Taiyuan was higher than that in Jinzhong, and the monthly and seasonal variation characteristics were similar. All showed high concentrations in winter and low concentrations in summer, and the highest concentration value appeared in January. The aerosol pollution caused by the static and stable weather in Jinzhong was more common than that caused by the sand and dust weather in Taiyuan. The distribution of particulate matter showed the characteristics of more intermediate values in Jinzhong and more high and fewer low values in Taiyuan, and winter was the highest incidence season of PM2.5 pollution in Jinzhong basin. PM2.5 transmission passageways in the main cities of Jinzhong basin in winter could be divided into four categories:class 1 was transmitted along the transverse valley of Taihang Mountain, and class 2 was the southeast transmission channel. Class 1 and class 2 were the short-range transmission passageways; air masses carried more moisture, and PM2.5 transmitted along such passageways allowed moisture to be absorbed more easily, increasing levels and aggravating local pollution. Class 3 was the northwest passageway, corresponding to the most serious pollution period of PM2.5 in Jinzhong basin before the arrival of cold air, which also corresponded to the dust transmission passageway. Class 4 was the Fenwei Plain passageway, corresponding to high-concentration PM2.5 pollution. Areas with dense pollution tracks (more than 100 pollution tracks) and areas with slow air flow movement (RTA pollution track end points greater than 50) easily became potential source areas of target cities (PSCF contribution greater than 0.7). The main potential source areas of PM2.5 in winter in Jinzhong (PSCF contributing more than 0.7) were mainly distributed in Linfen, Jincheng, and other places in Shanxi province, as well as in the north of Henan province, the south of Hebei province, and central and south Shaanxi province. The distribution range of main potential source areas of PM2.5 in Taiyuan in winter was wider than that in Jinzhong, including the south of Lvliang, Yangquan, Linfen, and Yuncheng and the south of Jinzhong in Shanxi, as well as most areas in southern Shaanxi, northern Henan province, and southern Hebei province. In addition, the PSCF distribution of high-value centers above 0.9 was wider than that of Jinzhong. When pollution occurs in cities that PSCF contributed more than 0.9, special attention should be paid to the influence of mutual transmission between them and cities in Jinzhong basin. Jinzhong and Taiyuan showed different distribution characteristics corresponding to the surface wind direction when light and higher pollution occur, when the wind direction near the ground in Jinzhong was E, the frequency of light and higher pollution was 8.1%; it was the highest in all wind directions. When the wind direction near the ground in Taiyuan was SSW, the frequency of light to higher polluted weather was the highest in all wind directions (5.1%). In the case of calm wind, the frequency of light to higher pollution in Taiyuan (3.4%) was higher than that in Jinzhong (0.5%).

Identifying Spatiotemporal Heterogeneity of PM2.5 Concentrations and the Key Influencing Factors in the Middle and Lower Reaches of the Yellow River

Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding-tillering, overwintering, reviving, jointing-heading, and flowering-maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries-Matusita (J-M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing-heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing-heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing-heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation.