The Reducing Effect of Green Spaces with Different Vegetation Structure on Atmospheric Particulate Matter Concentration in BaoJi City, China

Current methods for evaluating people's exposure to green space: A scoping review

People's exposure to green space is a critical link between urban green space and urban residents' health. Since green space may affect human health through multiple pathways regarding diverse human health outcomes, the measurement of people's exposure to green space must be tailored to concrete study contexts and research questions. In this scoping review, we systematically categorized the available green space representations and metrics in the last two decades that can be used to derive people's exposure to green space regarding different research topics. A three-phase systematic review was conducted after a generalized search of relevant research articles from the three most-used publication databases, namely Scopus, the Web of Science, and PubMed. We identified 260 research articles that particularly discuss green space representations and metrics. We further developed a multi-pathway framework to articulate the complicated context issues in green space studies. We categorized the most relevant green space representations and metrics into five groups, including green space indices, the delineation, inventory, and usage of green space, the spatiotemporal evolution of green space, the attributes and components of green space, and the green space landscape and fragmentation. Finally, we discussed the inter-conversion between different green space representations and metrics, the "mobility-turn" in green space studies and how it may affect the derivation of people's exposure to green space, and other potential methodological issues in measuring people's exposure to green space. Our scoping review provides the most comprehensive framework and categories for deriving people's exposure to green space to date, which may strongly support a broad range of studies that concern green space's health effects.

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollution disaster risk. This research has integrated principal component analysis (PCA), a genetic algorithm (GA) and a backpropagation (BP) neural network to evaluate the regional disaster risk. Hazard risk, hazard-laden environment sensitivity, hazard-bearing body vulnerability and disaster resilience were used to measure the degree of disaster risk. The main findings were: (1) the air pollution disaster risk index of Liaoning Province, Beijing, Shanghai and Guangdong Province increased year by year from 2010 to 2019; (2) the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of each regional air pollution disaster risk index in 2019, as predicted by the PCA-GA-BP neural network, were 0.607, 0.317 and 20.3%, respectively; (3) the predicted results were more accurate than those using a PCA-BP neural network, GA-BP neural network, traditional BP neural network, support vector regression (SVR) or extreme gradient boosting (XGBoost), which verified that machine learning could be used as a method of air pollution disaster risk assessment to a considerable extent.

PM2.5 Pollutant Concentrations in Greenspaces of Nanjing Are High but Can Be Lowered with Environmental Planning

Small-scale greenspaces in high-density central urban districts serve as important outdoor activity spaces for the surrounding residents, especially the elderly. This study selects six small-scale, popular greenspaces with distinct characteristics that are jointly situated along the same main urban artery in a high-density central urban district. Field investigations and questionnaires are conducted and combined with statistical analyses, to explore the spatial-temporal distribution and influencing factors of PM2.5 concentrations in these greenspaces. The study finds that the air quality conditions in the sites are non-ideal, and this has potential negative impacts on the health of the elderly visitors. Moreover, the difference values of PM2.5 concentrations' spatial-temporal distributions are significantly affected by vehicle-related emissions, which have significant temporal characteristics. PM2.5 concentration is strongly correlated with percentage of green coverage (R = 0.82, p < 0.05), degree of airflow (R = -0.83, p < 0.05), humidity and comfort level (R = 0.54, p < 0.01 and R = -0.40, p < 0.01 respectively). Meanwhile, the sites' "sky view factor" is strongly correlated with degree of airflow (R = 0.82, p < 0.05), and the comfort level plays an indirect role in the process of PM2.5 affecting crowd activities. Based on this analysis, an optimal set of index ranges for greenspace elements which are correlated with the best reduction in PM2.5 concentrations is derived. As such, this research reveals the technical methods to best reduce their concentrations and provides a basis and reference for improving the quality of small-scale greenspaces in high-density urban districts for the benefit of healthy aging.

Spatial Association of Urban Form and Particulate Matter

Increasingly detrimental effects of fine particulate matter (PM) have been observed in Northeast Asia owing to its rapid economic development. Previous studies have found that dust, combustion, and chemical reactions are the major sources of PM; nevertheless, the spatial configuration of land use and land cover, which is of most interest to planners and landscape architects, also influences the PM levels. Here, we attempted to unveil the relationship between PM and different types of land use cover (i.e., developed, agricultural, woody, grass, and barren lands) in 122 municipalities of Korea. Landscape ecology metrics were applied to measure the spatial configuration of land use pattern and spatial lag models by taking into account the transboundary nature of air pollution, allowing us to conclude the following regarding PM levels: (1) the size of land cover type matters, but their spatial configuration also determines the variations in PM levels; (2) the contiguity and proximity of landcover patches are important; (3) the patterns of grasslands (e.g., simple, compact, and cluster (with large patches) patterns) and woodlands (e.g., complex, contiguous, and cluster (with large patches) patterns) considered desirable for minimizing PM are dissimilar in terms of contiguity.

Where trees cannot grow - Particulate matter accumulation by urban meadows

It has already been proven that trees and shrubs, can efficiently remove particulate matter (PM) from air. However, almost nothing is known about PM accumulation by herbaceous plants (grasses and forbs) found in urban meadows. Meadows, unlike trees and shrubs, can be located close to roads, one of the main sources of PM in cites. The aim of this study was to investigate the tolerance to urban condition and PM accumulation in the immediate roads vicinity of selected plants species in urban meadows. PM accumulation of annual and perennial meadows was compared with that of lawns. Results were interpreted in the context of species composition, biomass production, soil conditions and ambient PM concentrations. Of the species grown in annual meadows, the highest PM accumulation was found in Achillea millefolium L., Chenopodium album L. and Echium vulgare L., while Centaurea scabiosa L., Echium vulgare L. and Convolvulus arvensis L. accumulated the largest amounts of PM in perennial meadows. PM deposition on plants was positively correlated with a feathery leaf shape. For species in the annual meadows, a positive correlation was also found between PM accumulation and the wax content on plants. The presence of hairs on leaves, leaf size and plant growth pattern had no effect on PM deposition on plants. PM accumulation in one square metre of urban meadow was on average greater than that of lawn, regardless of meadow species' composition, age and location. The greatest accumulation of PM was found in a perennial meadow with low biodiversity but the greatest biomass. It would appear that the biomass produced by meadows and canopy structure has a crucial impact on the amount of PM accumulated by meadow plants. The results obtained indicate that meadows could be an important element of nature-based solutions for mitigating air pollution in urbanised areas.

Measuring and Quantifying Impacts of Environmental Parameters on Airborne Particulate Matter in Under-Viaducts Spaces in Wuhan, China

Particulate pollution caused by urban traffic emissions has become a significant public hazard. Many urban roads of under-viaduct spaces (UVSs) have become concentrated areas of particulate pollution. This study aims to explore the effects of landscape parameters on particulate matter in UVSs in Wuhan, China. We selected 14 types of UVS sections and nine potential environmental parameters to monitor four types of particulate matter (PM_1.0, PM_2.5, PM₁₀, and TSP). Finally, linear regression analysis was employed to quantify the relative contributions of environmental parameters to the reduction in the concentration of the four types of particulate matter in the summer and winter. The results showed that particulate matter concentrations exhibit spatial and seasonal differences in UVSs. A single landscape parameter was correlated with particulate matter concentration, while compound environmental parameters had significant effects on the particulate matter concentration in UVSs. Meteorological factors and greening structures had a dominant impact on the particulate matter concentrations in summer and winter, respectively. Therefore, adjusting and optimizing the environmental parameters could reduce particulate pollution in UVSs and could have practical significance for the planning and design of UVSs.

Removal ability of different underlying surfaces to near-surface particulate matter

Atmospheric particulate matter is a wide-ranging environmental pollutant that can cause serious harm and poses a serious threat to public health. In this study, the near-surface particulate matter removal ability was quantitatively analyzed and compared for different land types under different pollution levels. The results showed that the concentrations of particulate matter 10 μm or less in diameter (PM10) and 2.5 μm or less in diameter (PM2.5) were higher in the morning and lower in the afternoon and that the seasonal variation was autumn > winter > spring > summer at a forest site. The diurnal concentration of particulate matter at a wetland site decreased continuously, with a seasonal variation of winter > autumn > spring > summer. The annual variation in the particulate matter concentration was higher in 2017 than in 2016 at both the forest and wetland sites. Forests remove particulate matter via plant leaves and root absorption, and wetlands rely on the enhancement of the relative air humidity to promote the absorption and accumulation of particles. For different air pollution levels, the deposition flux of PM2.5 increased with the pollution gradient. For the same air quality pollution level, the deposition flux of PM2.5 at the forest site was approximately 1.29 times higher than that at the wetland site. Data concerning PM10 in forests and wetlands are lacking. The results show that the deposition effect of the forest on particulate matter was better than that of the wetland.

Liaison between exposure to sub-micrometric particulate matter and allergic response in children from a petrochemical industry city

The study examines the association between exposure to sub-micrometric Particulate Matter (PM₁) and allergic response in a group of sensitive young children (age: 2-10 years) from Ploiesti city, Romania. The city is the only urban agglomeration in Europe surrounded by four oil refineries. A panel study was conducted by collecting medical information from children with respiratory illnesses and atopy (n = 135). Hot Spot Analysis revealed the areas of the city that are susceptible to high levels of PM₁. We found a close interaction between exposure to PM₁ outdoor concentrations and various physiological changes and clinical symptoms in children including triggering of allergic reactions, rhinitis, alteration of lung function, upper and lower respiratory tract symptoms, and bronchial asthma. During the 2-year study period, the incidence of hospitalizations was 40.7%. Strong correlations (p < 0.001) were observed between the PM₁ exposure and hospitalizations, and exposure and Immunoglobulin E (IgE). PM₁ exposure was also correlated with eosinophils (p < 0.05). Another positive correlation was observed between hospitalizations and IgE levels (p < 0.05). The mean results of tested indicators were as follows: wheezing (5.3, 95% CI (1.4-1.8); Coeff. of var. (CV) = 30%), IgE (382, 95% CI (349-445); CV = 102%), and EO% (5.3, 95% CI (3.3-4.2); CV = 69.5%). We can conclude that exposure to PM₁ influenced the frequency of wheezing episodes, increased hospitalizations, and the levels of allergic blood indicators in children, especially in infants and pre-schoolers. CAPSULE: Exposure to sub-micrometric particles (PM₁) influences the frequency of wheezing episodes, hospitalizations, and the levels of allergic blood indicators in children, especially in infants and pre-schoolers.

The blocking effect of atmospheric particles by forest and wetland at different air quality grades in Beijing China

To understand the effect of forests and wetlands on air quality, the PM₁₀ and PM_2.5 concentration and meteorological data were collected in the forest and wetland in the Beijing Olympic Forest Park in China from May 2106 to May 2017. The blocking rates of forest and wetland to PMs were calculated under different air quality grades which were divided into six levels base on a technical regulation. And we have got three main conclusions. (1) The diurnal variations of PMs were different in the forest and wetland. It showed a first decrease and then an increase in the forest; the lowest value (PM₁₀ = 40.00 µg/m³, PM_2.5= 5.37 µg/m³) was at approximately 12:00. In the wetlands, the lowest values were recorded at 16:00 (PM₁₀ = 39.63 µg/m³ and PM_2.5 = 15.89 µg/m³). (2) Another result showed that the blocking in the forest were significantly higher than that at the wetlands (P < .05), and the blocking effects were much better under lower air quality grades. The blocking rate of PM₁₀ and PM_2.5 was the highest when the air quality is excellent in the forest. When it comes to wetland, the highest blocking rate of PM₁₀ appears at good air quality, and the highest of PM_2.5 was at serious polluted. (3) In addition, there was negative correlation between PM concentrations and temperature, whereas the correlation between PM concentrations and relative humidity is positive. However, the correlation between blocking and meteorological parameters is weak.

An Effect of Urban Forest on Urban Thermal Environment in Seoul, South Korea, Based on Landsat Imagery Analysis

The urban heat island effect has posed negative impacts on urban areas with increased cooling energy demand followed by an altered thermal environment. While unusually high temperature in urban areas has been often attributed to complex urban settings, the function of urban forests has been considered as an effective heat mitigation strategy. To investigate the cooling effect of urban forests and their influence range, this study examined the spatiotemporal changes in land surface temperature (LST) of urban forests and surrounding areas by using Landsat imageries. LST, the size of the urban forest, its vegetation cover, and Normalized Difference Vegetation Index (NDVI) were investigated for 34 urban forests and their surrounding areas at a series of buffer areas in Seoul, South Korea. The mean LST of urban forests was lower than that of the overall city, and the threshold distance from urban forests for cooling effect was estimated to be roughly up to 300 m. The group of large-sized urban forests showed significantly lower mean LST than that of small-sized urban forests. The group of urban forests with higher NDVI showed lower mean LST than that of urban forests with lower mean NDVI in a consistent manner. A negative linear relationship was found between the LST and size of urban forest (r = −0.36 to −0.58), size of vegetation cover (r = −0.39 to −0.61), and NDVI (r = −0.42 to −0.93). Temporal changes in NDVI were examined separately on a specific site, Seoul Forest, that has experienced urban forest dynamics. LST of the site decreased as NDVI improved by a land-use change from a barren racetrack to a city park. It was considered that NDVI could be a reliable factor for estimating the cooling effect of urban forest compared to the size of the urban forest and/or vegetation cover.

Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina

In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5 μm) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built. Different techniques have been applied for the construction of the confidence intervals, and routes around the park and through the middle of it have been used to build the confidence intervals and classify this urban park in accordance with categories established by the Quito air quality index. These intervals have been based on the following estimators: the mean and standard deviation, median and median absolute deviation, median and semi interquartile range, a-trimmed mean and Winsorized standard error of order a, location and scale estimators based on the Andrew's wave, biweight location and scale estimators, and estimators based on the bootstrap-t method. The results of the classification of the park and its surrounding streets showed that, in terms of air pollution by PM2.5, the park is not at caution levels. The results of the classification of the routes that were followed through the park and its surrounding streets showed that, in terms of air pollution by PM2.5, these routes are at either desirable, acceptable or caution levels. Therefore, this urban park is actually removing or attenuating unwanted PM2.5 concentration measurements.

The Effect of Vegetation Enhancement on Particulate Pollution Reduction: CFD Simulations in an Urban Park

Vegetation in parks is regarded as a valuable way to reduce particulate pollution in urban environments but there is little quantitative information on its effectiveness. The aim of this study was to conduct on-site measurements and computational fluid dynamic (CFD) simulations to determine the aerodynamic and deposition effects of vegetation enhancement on particulate matter (PM) dispersions in an urban park in Xi’an, China. Initially, the airflow characteristics and deposition effects of vegetation were predicted and compared with measured air velocities and particulate pollution data to validate the numerical modeling. Then, associated coverage ratios and supplementary green areas (tree coverage ratio, crown volume coverage (CVC), and roof greening) were added to numerical simulations. After a series of numerical simulations and comparisons, results indicated that: (1) Numerical models with simplified vegetation method could reproduce the distribution of particulate matter concentrations in the real park environment; (2) with a tree coverage ratio >37.8% (or CVC > 1.8 m3/m2), the pedestrian-level PM2.5 could meet the World Health Organization’s air quality guidelines (IT-1) standards in the park; (3) roof greening on leeward buildings produced greater PM removal effects compared with windward buildings; and (4) the most economical and reasonable tree coverage ratio and CVC to reduce atmospheric PM in urban parks should be 30% and 1.8 m3/m2, respectively. These results are useful guidelines for urban planners towards a sustainable design of vegetation in urban parks.

Difference of Airborne Particulate Matter Concentration in Urban Space with Different Green Coverage Rates in Baoji, China

With the acceleration of urbanization and industrialization, the problem of airborne particulate pollution has become more and more serious. Green areas in urban spaces with different green coverage rates in Baoji City were selected to quantitatively compare the effects and differences of month, time, temperature, humidity, wind velocity, vegetation structure, and area of site on PM2.5 and PM10 concentration. The results showed that increasing the urban green coverage rate will help to improve the green area's reduction of airborne particulate matter concentration and the selected factors affecting the green area's reduction ability were discrepant in urban spaces with different green coverage rates. With the decrease of the green coverage rate, the purification effect of green area itself on air particles was weakened, and other factors, such as meteorological conditions and human activities, became the dominant influencing factors. Vegetation structure only had significant effects on the concentration of PM2.5 and PM10 in green areas of urban space with a green coverage rate greater than 75%. The concentration of PM2.5 and PM10 were lowest in the partly closed green area of one-layered coniferous trees and the closed green area of one-layered mixed trees. The research shows that green areas in urban spaces with different green coverage rates have different reduction effects on the concentration of airborne particles, which provides a theoretical basis and reference for the optimization of green area structures and to improve air quality effectively in the future.