Factors influencing the concentration of negative air ions during the year in forests and urban green spaces of the Dapeng Peninsula in Shenzhen, China

Wang, Yafei; Ni, Zhuobiao; Wu, Di; Fan, Cheng; Lu, Jiaqi; Bei-cheng, Xia

doi:10.1007/s11676-019-01047-z

Cited by 14 publications

(18 citation statements)

References 28 publications

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“…This result may be because Site 1 is adjacent to main urban roads, and Site 3 is close to highways in the district fringe. This finding is consistent with that of Wang et al [45], who showed that urban arterial roads affect the NAI concentration. The highest NAI concentration is at Point 4 in Site 2 (Figure 9), reflecting that the NAI concentration near the river water body is higher than that in other urban environments that are not near water bodies [45].…”

Section: Nai Concentration Analysissupporting

confidence: 93%

“…Some studies have found a significantly negative correlation between the air temperature and the NAI concentration [42], whereas some studies show contradictory results [20,43,44]. Other studies such as Wang et al [45] have presented a curvilinear relationship between the air temperature and NAI concentration in the equation. A similar controversy is observed in the correlation of seasonal and diurnal variations with NAIs.…”

Section: Studies Of Nais In the Urban Residential Scale Are Neededmentioning

confidence: 98%

“…As for the influence of morphology on the NAI concentration, the effects of the anthropogenic urban expansion on the concentration of NAIs have been studied in regional and city scale forests or local scale parks. Wang et al [45] found that local morphology such as vegetated area, water bodies, traffics, and residential regions can influence NAI concentration. However, although previous studies have investigated the correlation amongst air pollutants, meteorological conditions, and urban forms at a macroscale for a city or a regional district, research on NAIs at a microscale for urbanized local neighbourhoods is still lacking.…”

Section: Studies Of Nais In the Urban Residential Scale Are Neededmentioning

confidence: 99%

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Air Quality and Key Variables in High-Density Housing

Jia¹,

Liu²,

Ng³

2021

Sustainability

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The high-rise and high-density housing development in nearby industry relocations is a general urban sprawl phenomenon in fast-growing cities in Southern China. Aside from the low price, the improved air quality in the suburban area is always a reason for home buyers, but the consistent monitoring of air quality and knowledge about how to plan housing estates are lacking. This paper investigates the relationship between the housing morphology and the air quality in three housing estates in Shenzhen. This research utilizes on-site monitoring equipment to examine negative air ions (NAIs) and fine particulate matter (PM2.5) and the Computational Fluid Dynamics (CFD) simulation to examine the air flow. This study reveals the effect of the urban form on the concentration of NAIs and PM2.5 in spatial variation. A correlation study between the configuration variables of the urban form and the CFD air flow pattern helps to identify the key variables influencing the air quality. This study concludes that in housing estates with good air quality of surroundings, the building density has no remarkable effect. However, the footprint of buildings, the layout of podiums, the roughness length of the building, the distance between buildings, the open space aspect ratio and the mean building height may have a remarkable impact on the air flow and quality. These findings may encourage high-density housing development and provide planning guidance for the configuration of housing forms in Southern China and subtropical climate regions around the world.

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Section: Nai Concentration Analysissupporting

confidence: 93%

Section: Studies Of Nais In the Urban Residential Scale Are Neededmentioning

confidence: 98%

Section: Studies Of Nais In the Urban Residential Scale Are Neededmentioning

confidence: 99%

See 1 more Smart Citation

Air Quality and Key Variables in High-Density Housing

Jia¹,

Liu²,

Ng³

2021

Sustainability

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“…From the eld data, Spearman's correlation analysis showed that forest community type was negatively correlated with the environmental factors except for RH and NAI, and there were signi cant correlations between forest community type and WS, T, UV, PM1.0, PM10, TSP, and airborne bacterial concentration (p 0.05). Many related studies have reported that there were a positive correlation between forest and RH or NAI (Wang et al 2020a;Wang et al 2020b), and a negative correlation between forest and T, UV or airborne particulate matter (Wang et al 2019). Whereas there were no signi cant correlations between forest community types and RH, NAI, or PM2.5 in this study (p > 0.05).…”

Section: Correlations Between Airborne Bacterial Concentration and In Uencing Factorscontrasting

confidence: 70%

Assessment of Seasonal and Vegetational Variations in the Airborne Bacterial Concentration of the Urban Forest Park: A Case Study in Hunan, China

Liao

et al. 2021

Preprint

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It is important to investigate bacterial air quality in urban forest parks because tree bacteriostasis practices are being increasingly advocated as measures to improve the air quality and public-health in urban green spaces around the world. The aim of the present study was to quantitatively investigate airborne bacterial concentration levels based on field measurements in every season in five selected forest communities and the uncovered space in an urban forest park, as well as the effects of several factors on the culturability of airborne bacteria. Results suggested that the airborne bacterial levels of all forest communities reached the clean air quality standard with regard to the airborne bacteria content, with the highest bacteria concentrations in the uncovered space (1658 ± 1298 CFU/m3), and the lowest in the mixed community (907 ± 567 CFU/m3). The temporal distribution analysis showed that the most airborne bacteria were concentrated in summer, as well as in the morning and afternoon. The bacteriostatic rates of the mixed community were significantly different with seasonal variation (p < 0.05). Spearman’s correlations revealed that the bacterial concentration was significantly positively correlated with the season, wind speed (WS), temperature (T), ultraviolet light (UV), negative air ion (NAI) and total suspended particles (TSP) (p＜0.05), but significantly negatively correlated with the forest community type (p < 0.05). WS, UV, NAI, TSP and PM2.5 showed significant effect on airborne bacterial concentration in a multiple linear regression model (p < 0.05). Overall, the selection of tree species plays a key role in shaping the forest structure and improving air quality, and the urban forest highlights key priorities for future efforts towards a cleaner, healthier, and more diverse regional forest environment.

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“…It was discovered in this research that there was a prominent negative correlation mainly between NAI concentration and LST in the Daxing’anling region; this might be because the photosynthesis in growing seasons was hindered by the ultra-high environmental temperature, resulting in influencing the rate of photoelectric effect of green plants when generating NAI [ 48 ]. NAI had a positive correlation with NDVI, suggesting that the NAI concentration level could also be affected by the growth vigor of plants [ 49 ]. The regression equation describing the relationship between NAI and LST took the form of y = -35.515x 1 + 11206.813.…”

Section: Discussionmentioning

confidence: 99%

An inversion model for estimating the negative air ion concentration using MODIS images of the Daxing’anling region

Cui¹,

Zhao²,

Zhang³

et al. 2020

PLoS ONE

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The negative air ion (NAI) concentration is an essential indicator of air quality and atmospheric pollution. The NAI concentration can be used to monitor air quality on a regional scale and is commonly determined using field measurements. However, obtaining these measurements is time-consuming. In this paper, the relationship between remotely sensed surface parameters (such as land surface temperature, normalized difference vegetation index (NDVI), and leaf area index) obtained from MODIS data products and the measured NAI concentration using a stepwise regression method was analyzed to estimate the spatial distribution of the NAI concentration and verify the precision. The results indicated that the NAI concentration had a negative correlation with temperature, leaf area index (LAI), and gross primary production while it exhibited a positive correlation with the NDVI. The relationship between land surface temperature and the NAI concentration in the Daxing’anling region is expressed by the regression equation of y = -35.51x1 + 11206.813 (R2 = 0.6123). Additionally, the NAI concentration in northwest regions with high forest coverage was higher than that in southeast regions with low forest coverage, suggesting that forests influence the air quality and reduce the impact of environmental pollution. The proposed inversion model is suitable for evaluating the air quality in Daxing’anling and provides a reference for air quality evaluation in other areas. In the future, we will expand the quantity and distribution range of sampling points, conduct continuous observations of NAI concentrations and environmental parameters in the research areas with different land-use types, and further improve the accuracy of inversion results to analyze the spatiotemporal dynamic changes in NAI concentration and explore the possibility of expanding the application areas of NAI monitoring.

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Factors influencing the concentration of negative air ions during the year in forests and urban green spaces of the Dapeng Peninsula in Shenzhen, China

Cited by 14 publications

References 28 publications

Air Quality and Key Variables in High-Density Housing

Air Quality and Key Variables in High-Density Housing

Assessment of Seasonal and Vegetational Variations in the Airborne Bacterial Concentration of the Urban Forest Park: A Case Study in Hunan, China

An inversion model for estimating the negative air ion concentration using MODIS images of the Daxing’anling region

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