Solar-induced chlorophyll fluorescence intensity has a significant correlation with negative air ion release in forest canopy

Shi, Guang-Yao; Huang, Hui; Sang, Yu-Qiang; Cai, Lu-Lu; Zhang, Jinsong; Cheng, Xiangfen; Meng, Ping; Sun, Shuang; Li, Jianxia; Qiao, Yong-Sheng

doi:10.1016/j.atmosenv.2021.118873

Cited by 6 publications

(9 citation statements)

References 51 publications

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“…The negative air ion concentrations of the Songshan National Nature Reserve forest stands in the different seasons ranged from high to low: summer > autumn > spring, which is consistent with the findings of Wu et al (2006), Pawar et al (2010) and Li et al (2022). This is consistent with a high photoel, ectric effect occurring in the summer Shi et al, 2022) when environmental conditions are more suitable for plant growth than spring and autumn. However, other scholars have reported winter > spring > autumn > summer (Cong and Sun, 2010), which may related to the special weather conditions of the four seasons in Dalian.…”

Section: Discussionsupporting

confidence: 89%

Temporal Variation Characteristics of Negative Air ion Concentration and Air Quality Evaluation in Songshan National Nature Reserve, Beijing

Peiwen,

Changqing,

Yining

et al. 2023

Journal of Resources and Ecology

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Section: Discussionsupporting

confidence: 89%

Temporal Variation Characteristics of Negative Air ion Concentration and Air Quality Evaluation in Songshan National Nature Reserve, Beijing

Peiwen,

Changqing,

Yining

et al. 2023

Journal of Resources and Ecology

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“…The spatial distribution of NAIs was successfully depicted with an R 2 of 0.82 and 0.88 in the model training and verification (Figure 5). Our results once again proved the advantages of RF algorithms in explaining NAI with complex causes [19,24].…”

Section: Spatio-temporal Modeling Of Naissupporting

confidence: 74%

“…The significance of NAIs was also presented by site-based studies where forest canopy SIF accurately captured the dynamic changes in NAI concentrations [10,16,24]. These results once again proved the opinion that plant photosynthesis plays a vital role in regulating NAI concentrations [15,20].…”

Section: Spatial-temporal Determinations Of Naismentioning

confidence: 53%

“…In this study, the Random Forest (RF) machine-learning model was adopted to generate an NAI model based on site-based NAIs and observation multi-parameters. Furthermore, with the development of space technology, satellite-based models have been recorded as a possible way to model specific variables at a larger spatial scale [7,8,23,24]. Thus, the spatial information was also considered to realize the NAI inversion in this study.…”

Section: Introductionmentioning

confidence: 99%

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Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products

et al. 2023

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Negative air ions (NAIs), which are known as the “air vitamin”, have been widely used as a measure of air cleanness. Field observation provides an alternative way to record site-level NAIs. However, these observations fail to capture the regional distribution of NAIs due to the limited number of sites. In this study, satellite-based bio-geophysical parameters from the climate, topography, air quality, vegetation, and anthropogenic intensity were used to estimate the daily NAIs with the Random Forest model (RF). In situ NAI observations over Zhejiang Province, China were incorporated into the model. Daily NAIs were averaged to capture the spatio-temporal distribution. The results showed that (1) the RF algorithm performed better than traditional regression analysis and the common BP neural network to generate regional NAIs at a spatial scale of 500 m over the larger scale, with an RMSE of 258.62, R2 of 0.878 for model training, and R2 of 0.732 for model testing; (2) in the variable importance measures (VIM) analysis, 87.96% of the NAI variance was caused by the elevation, aspect, slope, surface temperature, solar-induced chlorophyll fluorescence (SIF), relative humidity (RH), and the concentration of carbon monoxide (CO), while path analysis indicated that SIF was one of the most important factors affecting NAI concentration across the whole region; (3) NAI concentrations in 87.16% of the region were classified above grade III (>500 ions cm−3), which was able to meet the needs of human health maintenance; (4) the highest NAI concentration was distributed over the southwest of the Zhejiang Province, where forest land dominates. The lowest NAI concentration was mostly found in the northeast regions, where urban areas are well-developed; and (5) among different land types, the NAI concentrations were ranked as forest land > water bodies > barren > grassland > croplands > urban and built-up. Among different seasons, summer and winter have the highest and lowest NAIs, respectively. Our study provided a substantial reference for ecosystem services assessment in Zhejiang Province.

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“…Understanding fluctuations in NAI concentrations in these areas not only establishes a scientific basis for evaluating air quality but also provides theoretical support for travel planning [4,6,7]. This study not only enhances our understanding of the correlation between NAI concentrations and forest therapy but also provides valuable insights for refining the design of forest therapy interventions, thereby enriching the overall experience for individuals [7][8][9][10][11][12][13]. Consequently, acquiring a thorough understanding of the temporal trends in NAI concentrations establishes a theoretical foundation for connecting NAI concentrations with health, travel planning, and forest therapy.…”

Section: Introductionmentioning

confidence: 99%

Temporal Dynamics of Negative Air Ion Concentrations in Nanjing Tulou Scenic Area

Li,

Chen

et al. 2024

Atmosphere

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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.

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Solar-induced chlorophyll fluorescence intensity has a significant correlation with negative air ion release in forest canopy

Cited by 6 publications

References 51 publications

Temporal Variation Characteristics of Negative Air ion Concentration and Air Quality Evaluation in Songshan National Nature Reserve, Beijing

Temporal Variation Characteristics of Negative Air ion Concentration and Air Quality Evaluation in Songshan National Nature Reserve, Beijing

Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products

Temporal Dynamics of Negative Air Ion Concentrations in Nanjing Tulou Scenic Area

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