Dust particles in the atmosphere play an important role in air pollution, climate change, and biogeochemical cycles. Some of the dominant sources of dust in mid-latitude regions are in Asia. An intense dust storm engulfed Northern China at the beginning of May 2017, and PM10 mass concentrations of 1500–2000 μg m−3 were measured near the dust source region. We combined numerical simulations, air quality monitoring data, and satellite retrievals to investigate dust emission and transport during this event. We found that the event was closely related to cold front activity, characterized by increased wind speed, which increased dust emission. We improved the dust scheme using a local dust size distribution to better simulate the dust emission flux. We found that accurate parametrization of the dust size distribution was important to effectively simulate both dust emission and ambient particle concentration. We showed that using a local dust size distribution substantially improved the accuracy of the simulation, allowing both the spatial distribution of pollution caused by the dust storm and temporal variability in the pollution to be captured.
Severe air pollution in China has caused significant tourism transformation for pursuing fresh air in microclimate tourism markets. Contemporary practices simply measure the air freshness of destinations and scenic spots using a single index, i.e., primarily negative oxygen ions (O2−). This index cannot comprehensively reveal scenic spots’ air freshness degree and determine the dynamic interactions between air freshness and scenic spots’ tourism development, thus inducing an illusion of air freshness for the target scenic spots. Meanwhile, the current fresh air index primarily ignores connections with the microclimate index of scenic spots and cannot provide a multidimensional index for scenic spots to take advantage of both air and microclimate resources for diverse tourism products and service production. Therefore, this study proposes a multidimensional index, the fresh air–natural microclimate comfort index (FAI-NMCI), connecting the fresh air index with the natural microclimate comfort index of scenic spots together from transdisciplinary and multidisciplinary perspectives. This study utilizes FAI-NMCI to measure four scenic spots of Fujian Province, and reveals in-depth results of scenic spots’ air freshness and natural microclimate comfort degree together. The results demonstrate that the four scenic spots in Fujian province of China had different levels of air freshness degree and natural microclimate comfort degree in 2018. The natural scenic spots were mostly distributed in Healing Fresh, Very Fresh, and Super Fresh levels of FAI with the most comfortable and comfortable levels of NMCI. The cultural scenic spots were mostly distributed in Relatively Fresh and Healing Fresh levels of FAI with the most comfortable and comfortable levels of NMCI. Meanwhile, the FAI-NMCI of natural and cultural scenic spots also had significant differences within 24 Jieqi, which will promote dynamic and creative utilization of those resources in microclimate tourism development.
In recent years, as civilization and human society have progressed, the potential and innovative capacity of various sectors of forest therapy have increasingly been recognized. However, the landscape of forest therapy is characterized by significant disparities in its distribution and uneven development patterns. Therefore, a comprehensive analysis of the factors influencing the distribution of forest therapy bases is crucial for optimizing the organization and allocation of resources within this industry, thereby promoting the growth of the forest therapy bases. This research delves into the spatial arrangement of forest therapy bases within Fujian Province, southern China. This study employs the nearest neighbor index, geographic concentration index, kernel density index, scale index, spatial autocorrelation analysis, and redundancy analysis to identify the primary factors influencing the geographical distribution of the bases. The study reveals three key findings about the spatial distribution of forest therapy bases in Fujian Province: (1) Centers are predominantly located in Nanping and Sanming, with a development pattern moving eastward and southward from Jianning and Taining in Sanming. (2) An imbalance is evident in the distribution, where areas with higher center concentrations exhibit a stronger spatial autocorrelation, characterized by high-density clusters. (3) Economic and environmental variables substantially affect center placement. At the municipal level, GDP, number of tourists, and forest coverage are significant. Conversely, at the district or county level, determinants include forest coverage, number of primary and secondary school students, forest land area, and GDP. Thus, it is suggested that the selection of bases for future forest therapy and the development of related industries should take into account local economic, environmental, and social factors. It aims to offer a scientific basis for planning forest therapy, potentially spreading its benefits to more areas.
The airflow trajectory model based on Lagrange method (HYSPLITv4.9) was applied to diagnose weather and climate. By simulating three dimension trajectory of the target area and using the airflow trajectory pattern based on Lagrange method, time-dependent curve of air mass and height dependent curve of specific humidity of the same air were drawn and the effect of moisture transport on Southwest China was analyzed concretely.
Negative air ions (NAIs) are crucial for assessing the impact of forests on wellbeing and enhancing the physical and mental health of individuals. They serve as pivotal indicators for assessing air quality. Comprehensive research into the distribution patterns of NAI concentrations, especially the correlation between NAI concentrations and meteorological elements in tourist environments, necessitates the accumulation of additional long-term monitoring data. In this paper, long-term on-site monitoring of NAI concentrations, air temperature, relative humidity, and other factors was conducted in real time over 24 h, from April 2020 to May 2022, to explore the temporal dynamic patterns of NAIs and their influencing factors. The results showed that (1) the daily dynamics of NAI concentrations followed a U-shaped curve. The peak concentrations usually occurred in the early morning (4:30–8:00) and evening (19:10–22:00), and the lowest concentrations usually occurred at noon (12:50–14:45). (2) At the monthly scale, NAI concentrations were relatively high in February, August, and September and low in January, June, and December. At the seasonal scale, NAI concentrations were significantly higher in winter than in other seasons, with higher concentrations occurring in the summer and autumn. (3) Relative humidity, air temperature, and air quality index (AQI) were the primary factors that influenced NAI concentrations. Relative humidity showed a significant positive correlation with NAI concentrations, while air temperature and AQI both exhibited a significant negative correlation with NAI concentrations. Higher air quality corresponds to higher NAI concentrations. Our research provides new insights into NAI temporal dynamics patterns and their driving factors, and it will aid in scheduling outdoor recreation and forest health activities.
The concentration of negative air ions (NAIs) is an important indicator of air quality. Here, we analyzed the distribution patterns of negative air ion (NAI) concentrations at different time scales using statistical methods; then described the contribution of meteorological factors of the different season to the concentration of NAIs using correlation analysis and regression analysis; and finally made the outlook for the trends of NAI concentrations in the prospective using the auto regressive integrated moving average (ARIMA) models. The dataset of NAI concentrations and meteorological factors measured at the fixed stations in the Mountain Wuyi National Park were obtained from the Fujian Provincial Meteorological Bureau. The study showed that NAI concentrations were correlated with relative humidity spanning all seasons. Water was an important factor affecting the distribution of NAI concentrations in different time series. Compared with other ARIMA models, the outlook value of the ARIMA (0,1, 1) model was closer to the original data and the errors were smaller. This article provided a unique perspective on the study of the distribution of negative air oxygen ions over time series.
Precipitation (PRE) is an essential factor that affects the negative air ions (NAIs) concentrations. However, the mechanism of NAIs concentrations and their influencing factors on rainy and non-rainy days remains unclear. Here, we used hourly data of NAIs concentrations and meteorological data in 2019 to analyze the distribution of NAIs concentrations and its influencing factors on rainy and non-rainy days in the Wuyi Mountain National Park (WMNP) of China, which was listed as a World Cultural and Natural Heritage Site in 1999. The results indicated that the NAIs concentrations on rainy days were significantly higher than on non-rainy days. However, the NAIs concentrations on rainy days were slightly higher than on the first and second days after rainy days. Then, the NAIs concentrations were significantly reduced on the third day and after that. Thus, rainy days lead to a 2-day lag in the smooth reduction of NAIs on non-rainy days after rainy days. NAIs concentrations were significantly correlated with the relative humidity (RHU) on both rainy and non-rainy days. By analyzing the meteorological factors on NAIs for ranking the feature importance scores on rainy and non-rainy days, PRE was ranked first on rainy days, and sea level pressure (PRS_Sea) and temperature (TEM) were ranked first and second on non-rainy days, respectively. Based on the univariate linear regression model (ULRM), NAIs concentrations responded strongly (higher absolute slope values) to RHU on rainy days and to pressure (PRS), visibility (VIS), water vapor pressure (VAP), TEM, and ground surface temperature (GST) on non-rainy days. The results highlight the importance of PRE in the lag time of NAIs concentrations on rainy and non-rainy days.
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