Animal-and mineral-based medicines in Gansu-Ningxia-inner Mongolia region, P.R. China: a cross-cultural ethnobiological assessment.

Introduction: Traditional animal- and mineral-based medicines are widely used in the Gansu-Ningxia-Inner Mongolia junction zone, a region with diverse ethnic groups and cultures. This study aims to document, conserve, and explore the potential of these medicines for further research and sustainable development of ethnic medicine. Methods: We interviewed 56 informants from different ethnic backgrounds and analyzed their responses quantitatively. Additionally, a comparative analysis with adjacent regions was conducted, providing invaluable contextual insights. Results: The study unveiled a diverse array of traditional medicines in the Gansu-Ningxia-Inner Mongolia junction zone. A total of 47 animal-based medicines were identified, ranging from insects and scorpios to distinctive animal organs. Of notable significance was Moschus, emerging as a pivotal traditional Chinese medicine resource. In parallel, 12 mineral-based medicines were cataloged, procured both locally and from "pharmacies". Female informants, frequently local herbal practitioners, demonstrated broader knowledge of medicines. The analysis of 13 villages revealed varying perceptions of medicine importance, underscoring the wealth of traditional knowledge. Specific medicines, such as Feng-Mi and Xie-Zi, were widely used and valued in local healthcare practices for their cultural and medicinal benefits. Conclusion: This study provides a comprehensive overview of traditional animal- and mineral-based medicines in the Gansu-Ningxia-Inner Mongolia junction zone. It highlights the need for preserving and applying these practices in a sustainable manner. It also lays a solid foundation for future research on ethnic medicine, which can contribute to the holistic wellbeing of local communities.

[Spatio-temporal Variations in PM2.5and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration].

To coordinate the contradiction between economic development and environmental pollution and achieve the sustainable development of the economy and society, the spatio-temporal variations in PM2.5 were analyzed based on PM2.5 concentration and meteorological data of the Yangtze River Delta (YRD) urban agglomeration. Wavelet transform coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC) were used to analyze the multi-scale coupling oscillation between PM2.5 and meteorological factors in the time-frequency domain. The results showed that:① the concentration of PM2.5 in the YRD decreased from northwest to southeast, and the spatial range with high PM2.5 concentration decreased annually. The spatial distribution characteristics of the seasonal average PM2.5 concentration were similar to those of the annual average PM2.5 concentration. PM2.5 concentration exhibited the seasonal variation characteristics of high in winter, low in summer, and transitioning between spring and autumn. ② PM2.5 concentration decreased from 2015 to 2021, and the compliance rate increased. The difference in annual average PM2.5 concentration was decreased with dynamic convergence characteristics. The convergence of PM2.5 concentration in summer was greater than that in winter. During the whole study period, the daily average PM2.5 concentration showed a "U" distribution, and the proportion of days with excellent and good PM2.5 levels were 49.72% and 41.45%, respectively. ③ The wavelet coherence between PM2.5 and meteorological factors was different in different time-frequency domains. The main factors affecting PM2.5 were different in different time-frequency scales. At all time-frequency scales, WTC and PWC showed that wind speed and temperature were the best explanatory variables of PM2.5 variation, respectively. ④ The larger the time-frequency scale, the stronger the interaction of multi-factor combinations to explain PM2.5 variations. The synergistic effect of temperature and wind speed could better explain the variation in PM2.5. These results can provide reference for air pollution control in the YRD.

Characterisation and Risk Assessment of Metal Contaminants in the Dust Fall in the Vicinity of a Construction Waste Dump in Beijing.

In this study, a large construction waste dump in Beijing, China, was used as the study area. Nineteen effective atmospheric dust samples were collected. The mass fractions of 14 metal elements (Ca, Fe, Al, Mg, Mn, Zn, Cr, Cu, V, Pb, Ni, As, Co, and Cd) were determined for the samples using ICP-MS. The pollutants and the potential ecological risk levels of 10 different heavy metals were evaluated using the enrichment factor, geo-accumulation index, and a potential ecological risk assessment method. The results showed that the Ca, Fe, Al, and Mg contents in the dust fall were considerably high and accounted for 98.81% of the total mass of the analysed metals. Cd and Zn were the main metal contaminants in the dust fall in the vicinity of the construction waste dump, followed by Cu and Mn. The Cd, Zn, Cu, and Mn contents in the construction waste had a significant impact on atmospheric pollution within 250 m of the dump. Moreover, Cd had the largest contribution to the comprehensive ecological risk posed by the heavy metals in the dust fall and was determined to be the primary ecological risk factor in the atmospheric environment in the vicinity of the construction waste dump.

[Pollution Characteristics and Health Risk Assessment of Heavy Metals in PM2.5 Collected in Baoding].

To reveal the pollution characteristics and the health risks of the trace heavy metals in the atmospheric particles in Baoding, Hebei province, PM2.5 samples were collected using a middle volume sampler, and the mass concentrations of V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb in the samples were determined by microwave digestion-inductively coupled plasma-mass spectrometry (ICP-MS). The results showed that the PM2.5 concentration in Baoding ranged from 16.84-476.2 µg·m-3. During sampling, 65 samples were above the second-level standard of the Ambient Air Quality Standards (GB 3095-2012) by 54.2%. The most heavy metal elements showed higher levels in nighttime than during the daytime, except for except for Ni, Mn, and Co. Obvious seasonal variation was found with the trend of winter > autumn > spring > summer. The enrichment factors for Cu, Zn, Pb, and Cd were more than 1.5, indicating that those metals mainly came from anthropogenic emissions, such as traffic sources. Health risk assessment results indicated that the non-carcinogenic risk of heavy metals in PM2.5 in Baoding was small, and the carcinogenic risk resulting form As, Cr, Cd, and Co was greater for adults than for children.

[Variation in Pollutant Concentrations and Correlation Analysis with the Vegetation Index in Beijing-Tianjin-Hebei].

Based on 2017 hourly air quality monitoring data, NDVI 16 d synthetic data, and socio-economic data, the air pollution characteristics of Beijing-Tianjin-Hebei were systematically analyzed, and its variation, normalized vegetation index, and the relationship between the index (NDVI) and its impact on socio-economic factors, were analyzed by linear regression analysis and a geographically weighted regression model. The conclusions are as follows:①The overall air pollution in the Beijing-Tianjin-Hebei region is characterized by high-level pollution over the southern plain areas and low-level pollution over the northern mountainous areas. The air pollution increases from north to south, and shows significant spatial heterogeneity. ②From the perspective of seasonal changes, the overall order winter > autumn > spring > summer is observed, and atmospheric pollution in the Beijing-Tianjin-Hebei region shows significant temporal heterogeneity. ③The concentrations of pollutants such as SO2, NO2, CO, PM2.5, and PM10 all have a negative correlation with the NDVI value. Assuming that natural conditions such as climate and topography are relatively consistent, the lower the NDVI value, the more obvious the interference of human activities, the more concentrated the industrial economy layout, and the greater the pollution emissions, the more significant the negative impact on air quality. ④The NDVI reflects the land use, population distribution, and industrial layout to a certain extent, and these factors directly or indirectly determine the level of air pollution emissions and thus indicate the pollution distribution characteristics of the region. ⑤The results of the GWR model calculation show that the higher the level of economic development, the better the correlation between the NDVI and socioeconomic factors, PM2.5, and other pollutant concentrations. The distribution of the NDVI can generally reflect the level of social and economic development. The distribution of the NDVI also correlates to the distribution of PM2.5 to a certain extent.

Pollution Level and Health Risk Assessment of PM2.5-Bound Metals in Baoding City Before and After the Heating Period.

In order to assess the pollution levels and health risks of PM2.5-bound metals in Baoding City before and after the heating period, samples were collected in 2016 at Hebei University from September 25th to November 14th during the non-heating period, and November 15th to December 26th during the heating period, respectively. ICP-MS was applied to analyze seven heavy metals (Cr, Zn, Cu, Pb, Ni, Cd and Fe). The statistical analysis, enrichment factor (EF), pollution load index method, and Risk Assessment Method proposed by U.S. EPA were used to evaluate the non-carcinogenic risks of six of these heavy metals (Cr, Zn, Cu, Pb, Ni and Cd) and carcinogenic risks of three of these heavy metals (Cr, Ni and Cd). The results showed three main results. First, the average daily PM2.5 concentrations of the national air monitoring stations was 155.66 µg·m-3 which was 2.08 times as high as that of the second level criterion in China (75 µg·m-3) during the observation period. Compared with the non-heating period, all heavy metals concentrations increased during heating period. The growth rates of Pb and Ni were the highest and the lowest, which were 88.03 and 5.11 percent, respectively. Second, the results of enrichment factor indicated that the EF values of all heavy metals were higher during the heating period in comparison with during the non-heating period, but the degree of enrichment of all heavy metals remained unchanged. Not only those, Cr and Ni were minimally enriched and were affected by both human and natural factors, Pb, Cu and Zn were significantly enriched and were mainly affected by human factors, the enrichment of Cd was much higher than that of the other heavy metals, exhibiting extremely high enrichment, mainly due to human factors during the whole sampling period. The results of the pollution load index indicated that the proportions of the number of highly and very highly polluted PM2.5-bound metals were the highest during the heating period, while the proportion of moderately polluted PM2.5-bound metals was the highest during the non-heating period. The combined pollution degree of heavy metals was more serious during the heating period. Third, according to the health risk assessment model, we concluded that the non-carcinogenic and carcinogenic risks caused by inhalation exposure were the highest and by dermal exposure were the lowest for all kinds of people. The overall non-carcinogenic risk of heavy metals via inhalation and subsequent ingestion exposure caused significant harm to children during the non-heating and the heating periods, and the risk values were 2.64, 4.47, 1.20 and 1.47, respectively. Pb and Cr exhibited the biggest contributions to the non-carcinogenic risk. All the above non-carcinogenic risks exceeded the standard limits suggested by EPA (HI or HQ < 1). The carcinogenic risk via inhalation exposure to children, adult men and women were 2.10 × 10-4, 1.80 × 10-4, and 1.03 × 10-4 during the non-heating period, respectively, and 2.52 × 10-4, 2.16 × 10-4 and 1.23 × 10-4 during the heating period, respectively. All the above carcinogenic risks exceeded the threshold ranges (10-6~10-4), and Cr posed a carcinogenic risk to all people.

[Enrichment Levels and Comprehensive Pollution Assessment of Dust Heavy Metals in Winter in Beijing].

Dust reflects the natural subsidence of particles and is thus generally accepted as an important environmental indicator. Dust heavy metals find their way into soil or water via atmospheric deposition, potentially damaging plants, aquatic organisms, as well as human beings. In order to study the concentration levels, enrichment degrees, and comprehensive pollution characteristics of the dust heavy metals Cd, Cr, Pb, Cu, Zn, Ni, Co, V, Bi, and Mo in winter in Beijing, 49 groups of dust samples were collected from different locations within the city from November 2013 to March 2014. Heavy metal content (mass percentage) was then measured using Elan DRC Ⅱ type inductively-coupled plasma mass spectrometry (ICP-MS). Results showed that the average content of Cd, Bi, and Mo was less than 10 mg·kg-1, that of Co, Ni, and V was between 10 and 100 mg·kg-1, while that of Pb, Cr, Cu, and Zn was more than 100 mg·kg-1. The amount of Cd, Zn, and Cu in dust samples was generally higher than their secondary standard values in the soil environment. The secondary standard excess rates of Cd, Zn, and Cu were 100%, 97%, and 93.9% in urban environments, and 100%, 100%, and 81.2% in suburban environments, respectively. Results also indicated slight enrichment of Bi, Cu, Ni, and Pb, mainly derived from crustal or soil sources, while Cd, Cr, Mo, and Zn, exhibited mild enrichment, caused by a combination of natural and artificial sources. The paper proposes a "dust heavy metal comprehensive pollution index" model, based on the traditional single pollution assessment method, so as to explore comprehensive pollution characteristics of dust heavy metals in Beijing. Results for the dust heavy metal comprehensive pollution index in Beijing City were in the order of Cd > Zn > Cu > Pb > Cr > Ni > V > Co. Cd, Zn, Cu, and Pb were identified as key pollution factors, with Cd and Zn as primary factors. Results for the dust heavy metal comprehensive pollution index in Beijing suburbs was Cd > Zn > Cu > Pb > Cr > Ni > Co > V. Cd, Zn, Cu, Pb, and Cr were key pollution factors, with Cd and Zn as primary factors.

Pollution Characteristics and Health Risk Assessment of Airborne Heavy Metals Collected from Beijing Bus Stations.

Airborne dust, which contains high levels of toxic metals, is recognized as one of the most harmful environment component. The purpose of this study was to evaluate heavy metals pollution in dustfall from bus stations in Beijing, and to perform a risk assessment analysis for adult passengers. The concentrations of Cd, Co, Cr, Cu, Mo, Ni, Pb, V and Zn were determined by inductively coupled plasma mass spectroscopy (ICP-MS). The spatial distribution, pollution level and potential health risk of heavy metals were analyzed by Geographic Information System (GIS) mapping technology, geo-accumulation index and health risk assessment model, respectively. The results indicate that dust samples have elevated metal concentrations, especially for Cd, Cu, Pb and Zn. The nine metals can be divided into two categories in terms of spatial distribution and pollution level. Cd, Cr, Cu, Mo, Pb and Zn reach contaminated level and have similar spatial patterns with hotspots distributed within the Fifth Ring Road. While the hot spot areas of Co and V are always out of the Fifth Ring Road. Health risk assessment shows that both carcinogenic and non-carcinogenic risks of selected metals were within the safe range.

A damage assessment model of oil spill accident combining historical data and satellite remote sensing information: a case study in Penglai 19-3 oil spill accident of China.

Oil spills are one of the major sources of marine pollution; it is important to conduct comprehensive assessment of losses that occur as a result of these events. Traditional methods are required to assess the three parts of losses including cleanup, socioeconomic losses, and environmental costs. It is relatively slow because assessment is complex and time consuming. A relatively quick method was developed to improve the efficiency of assessment, and then applied to the Penglai 19-3 accident. This paper uses an SAR image to calculate the oil spill area through Neural Network Classification, and uses historical oil-spill data to build the relationship between loss and other factors including sea-surface wind speed, and distance to the coast. A multiple regression equation was used to assess oil spill damage as a function of the independent variables. Results of this study can be used for regulating and quickly dealing with oil spill assessment.