Quantitative estimation of the PM2.5 removal capacity and influencing factors of urban green infrastructure

Analysis of the impact of multiple green space patterns and key meteorological factors on particulate matter pollution: a case study in the Zhengzhou metropolitan area

Atmospheric particulate matter (PM) is a primary pollutant affecting urban air quality, posing increasing threats to public health and ecological environments. While urban green spaces and meteorological conditions individually influence PM pollution, the mechanisms by which meteorological indicators mediate the relationship between green space patterns and PM concentrations remain unclear. We used daily PM concentration data in the Zhengzhou Metropolitan Area (ZMA) in 2021, combined with high-resolution satellite imagery and climate monitoring data. By employing Generalized Linear Models (GLMs) and Partial Least Squares Structural Equation Modeling (PLS-SEM), we investigated the effects of green spaces and meteorological conditions on PM, highlighting the significant mediating role of key meteorological indicators in the process by which green spaces mitigate PM pollution. Results indicated that PM2.5 concentrations were more sensitive to green space patterns and meteorological conditions at 1-6 km scales compared to PM10. Significant scale-dependent differences were observed in the coupling between PM concentrations and green spaces. PLS-SEM revealed that key meteorological indicators, particularly wind speed and humidity, significantly mediated the impact of green spaces on PM pollution, with mediation effects peaking at the 4 km scale. The percentage of largest green space patches had the most pronounced mediated effect on PM2.5 and PM10 through climate factors. Conclusively, to maximize ecological benefits, it is essential to consider wind speed and humidity around green spaces. The findings emphasize the importance of optimizing green space patterns at multiple scales and incorporating local microclimate considerations in future PM pollution management within the ZMA.

Estimating the seasonal and spatial variation of urban vegetation's PM2.5 removal capacity

Fine particulate matter (PM2.5) is one of the most severe factors contributing to urban air pollution, posing significant risks to human health and environmental quality. Urban vegetation, acting as a natural method for pollution mitigation, can effectively reduce harmful air particle concentrations through processes like adsorption and deposition. While much research has quantified urban vegetation's role in PM2.5 removal, the spatial variability and seasonal fluctuations of this process in urban environments remain poorly understood. Furthermore, few studies have quantitatively explored the environmental factors that influence this capability. Using Shanghai as a case study, this research estimates the PM2.5 reduction by urban vegetation in 2022, integrating the i-Tree Eco model with Local Climate Zones (LCZs) classification. The results indicate that vegetation plays a significant role in PM2.5 removal, with a total annual removal of 835 tons and an average removal rate of 0.51 g⋅m-2⋅year-1 per unit leaf area. The maximum annual air quality improvement reached 21.7%, with an average of 4.09%. The removal flux exhibited a clear "double peak" pattern throughout the year, with peaks occurring in late spring and late summer. Significant spatial variations in PM2.5 removal capacity were observed across different LCZs, ranked as follows: Dense Trees > Open Lowrise > Large Lowrise > Bush/Shrub > Scattered Trees > Others. Notably, Open Lowrise areas demonstrated considerable potential in both removal flux and total removal. The 38-42 mm evapotranspiration range was found to be the most effective for PM2.5 removal. However, when evapotranspiration exceeded 50 mm, removal efficiency showed a clear diminishing marginal effect, closely linked to the regulation of leaf stomatal opening and closing. The findings of this study underscore the importance of vegetation in improving air quality and provide valuable insights for urban planning and environmental policy.

Maternal green space exposure and congenital heart defects: A population-based study

BACKGROUND

Beneficial effects of maternal green space exposure on preterm birth and low birth weight have been documented, but few studies have investigated its protective effect on fetal congenital heart defects (CHDs). Our study aimed to investigate the association between maternal green space exposure and CHDs, and quantify the potential benefits of reducing the risk of fetal CHDs by achieving the target of green space coverage.

METHODS

The study included 4160 births with CHDs and 567,483 births without birth defects born from 2014 to 2019 in 14 cities in Liaoning Province, China. Maternal green space exposure, including periconception period (3 months before conception to 3 months into pregnancy), preconception period (3 months before conception), and the first trimester (3 months into pregnancy), was assessed using satellite-based normalized difference vegetation index (NDVI). Logistic regression models were used to estimate the associations between maternal green space exposure and the risk of fetal CHDs.

RESULTS

A 0.1-unit increase in maternal green space exposure during the periconception was significantly associated with 5% declines in the risk of CHDs (OR: 0.95, 95% CI: 0.92, 0.98). Exposure-response association suggested that continuous improvements in maternal green space exposure during the periconception had a greater protective impact on the risk of total CHDs, while attenuated benefits were identified in the area where NDVI is around 0.23. Assuming causality, 5.06% (95% CI: 1.72%, 8.28%) of the annual rate of CHDs could be avoided by increasing NDVI exposure to 0.23 during periconception period in the areas where NDVI is below 0.23. In addition, rural residents, and mothers who have parity twice or more, were more prone to the protective effect of green space exposure.

CONCLUSIONS

Our study provides evidence that maternal green space exposure is a protective factor against the risk of fetal CHDs. The findings suggest that prioritizing green space in public policy can be an effective health-promoting measure.

Evaluating drivers of PM2.5 air pollution at urban scales using interpretable machine learning

Reducing urban fine particulate matter (PM2.5) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM2.5 will enable the development of targeted strategies to reduce PM2.5 levels. This study introduces a machine-learning model that combines CatBoost and the Tree-Structured Parzen Estimator (TPE) to analyze PM2.5 concentration across 297 cities between 2000 and 2021. SHapley Additive exPlanations (SHAP) were employed to identify the primary factors influencing urban PM2.5 concentrations. The study revealed that the proposed model has high accuracy in predicting urban PM2.5 concentrations, achieving a coefficient of determination (R2) score of 96.44%. Socioeconomic and industrial activity are key drivers of PM2.5 concentrations. This study not only quantifies the primary factors exacerbating or alleviating pollution for each city or province during the 2000-2021 period but also evaluates the influence of operational factors such as technological and public financial expenditures. In 2000, the main contributors to pollution in four heavily polluted cities included substantial nitrogen oxide emissions, inadequate technology investments, and excessive population density and liquefied gas consumption. Due to the rapid reduction in nitrogen oxide emissions, pollution levels in these cities have improved substantially. In the future, the most effective strategies for pollution reduction in these cities will focus on controlling population density and slowing down mining development. The proposed framework serves as a robust evaluation tool and can propose tailored strategies to control PM2.5 concentrations effectively in each city.

Combined Catalytic Conversion of NOx and VOCs: Present Status and Prospects

This article presents a comprehensive examination of the combined catalytic conversion technology for nitrogen oxides (NOx) and volatile organic compounds (VOCs), which are the primary factors contributing to the formation of photochemical smog, ozone, and PM2.5. These pollutants present a significant threat to air quality and human health. The article examines the reaction mechanism and interaction between photocatalytic technology and NH3-SCR catalytic oxidation technology, highlighting the limitations of the existing techniques, including catalyst deactivation, selectivity issues, regeneration methods, and the environmental impacts of catalysts. Furthermore, the article anticipates prospective avenues for research, underscoring the necessity for the development of bifunctional catalysts capable of concurrently transforming NOx and VOCs across a broad temperature spectrum. The review encompasses a multitude of integrated catalytic techniques, including selective catalytic reduction (SCR), photocatalytic oxidation, low-temperature plasma catalytic technology, and biological purification technology. The article highlights the necessity for further research into catalyst design principles, structure-activity relationships, and performance evaluations in real industrial environments. This research is required to develop more efficient, economical, and environmentally friendly waste gas treatment technologies. The article concludes by outlining the importance of collaborative management strategies for VOC and NOx emissions and the potential of combined catalytic conversion technology in achieving these goals.

Seasonal variation of particle number concentration in a busy urban street with exposure assessment and deposition in human respiratory tract

Ultrafine particles (UFP) associated with air quality and health impacts are a major concern in growing urban regions. Concentrations of UFP (particles of size between 10 and 100 nm) and accumulation mode (Nacc) (particles of size >100 and up to 1000 nm), are analyzed over a highly polluted megacity, Delhi, in conjunction with vehicular flow density, during peak (morning, and evening) and non-peak hours. UFP contributes ≥60% to total particle concentration during autumn and monsoon. UFP concentrations are about 50,000 particles per cm3 in winter which reduces to about 25,000 particles during monsoon. Nacc are about 20,000 (winter) and 10,000 (monsoon) particles per cm3. UFP concentration and Nacc during peak hours are at least twice higher than those obtained in non-peak hours, confirming the dominant influence of emissions from vehicular exhaust in the study region. Seasonal analysis of UFP size distribution reveals that direct emissions dominate the particle concentrations during winter and autumn, whereas new particle formation mechanism contributes the highest in spring and summer. Assessment of inhalable particle number concentration and particle deposition in the human respiratory tract using Multiple Path Particle Dosimetry (MPPD) model, performed for the first time, shows that the order in which these particles deposit in the human respiratory tract is alveoli > bronchiole > bronchus. The deposition ranges between 10 and 18 million nanoparticles during different hours of the day, whereas the estimated inhalable particle concentration (IPN) varies between 0.5 and 1 billion. Results on the IPN during activities classified from light (walking), medium, heavy, very heavy to severe (long-distance running) provide insights into health effects on vulnerable populations. These quantitative results obtained over a megacity on hourly and seasonal variations of nanoparticles along with IPN and deposition rates for different activities are important, and are invaluable inputs for developing mitigation policies aimed to improve air quality and public health, both of which are major concerns in South Asia.

Experimental analysis of PM2.5 reduction characteristics between Korean red pine (Pinus densiflora) and sawtooth oak (Quercus acutissima) saplings under different densities and arrangement structures

As global air pollution, particularly fine particulate matter (PM2.5), has become a major environmental problem, various PM2.5 mitigation technologies including green infrastructure have received significant attention. However, owing to spatial constraints on urban greening, there is a lack of management plans for urban forests to efficiently mitigate PM2.5. In this study, we assessed the PM2.5 reduction capabilities of Pinus densiflora (Korean red pine) and Quercus acutissima (sawtooth oak) by measuring the changes of PM2.5 concentrations using an experimental chamber system. In addition, the PM2.5 reduction efficiency in 90 min (PMRE90) and the amount of PM2.5 reduction per leaf area (PMRLA) were compared based on arrangement structures and density levels. The results showed that the PM2.5 reduction by plants was significantly greater than that of the control experiment without any plants, and an additional reduction effect of approximately 1.38 times was induced by a 1.5 m s-1 air flow. The PMRE90 of Korean red pine was the highest at medium density. In contrast, the PMRE90 of sawtooth oak was the highest at high density. The PMRLA of both species was highest at low densities. The different responses of the species to total reduction were well explained by total leaf area (TLA). The PMRE90 of both species was positively correlated with TLA. The PMRLA of sawtooth oak was approximately 2.3 times greater than that of Korean red pine. However, there were no significant differences in both PMRE90 and PMRLA between the arrangement structures. Our findings reveal the potential mechanisms of vegetation in reducing PM2.5 according to arrangement structure and density. This highlights the importance of efficiently using urban green spaces with spatial constraints on PM2.5 mitigation in the future.

Uptake and distribution of the inorganic components NH4+ and NO3- in PM2.5 by two Chinese conifers

Plants can effectively purify PM2.5 in the air, thereby improving air quality. Understanding the mechanisms of the uptake and distribution of PM2.5 in plants is crucial for enhancing their ecological benefits. In this study, the uptake and distribution of the water-soluble inorganic compounds ammonium (NH4+) and nitrate (NO3-) ions in PM2.5 by the two native Chinese conifers Manchurian red pine (Pinus tabuliformis) and Bunge's pine (P. bungeana) were investigated using a one-time aerosol treatment method combined with 15N tracing. The results showed the following: (1) Plants can efficiently uptake NH4+ (0.08-0.21 μg/g) and NO3- (0.03-0.68 μg/g) from PM2.5. Manchurian red pine uptakes these compounds more effectively with increases of 2.01-fold for NH4+ and 1.02-fold for NO3- compared with Bunge's pine. (2) The aboveground organs of the plants uptake and distribute more 15N than the belowground organs. The branches had the highest unit mass uptake (0.08-1.60 μg/g) and rate of distribution (16.91-53.60 %) for NH4+, while the leaves had the highest unit mass uptake (0.15-1.18 μg/g) and rate of distribution (50.78-84.88 %) for NO3-. (3) The ability of the aboveground organs to uptake 15N is influenced by the concentration of PM2.5, which showed an overall increase with increasing concentrations with some fluctuations in specific organs. However, the belowground organs were not affected by the concentration of PM2.5. (4) A larger specific leaf area, root-shoot ratio, branch biomass ratio, coarse root biomass ratio, and lower trunk biomass ratio favors the uptake of NH4+ from PM2.5, whereas these traits had a minimal influence on the uptake of NO3-. Manchurian red pine uptaked significantly more NH4+ compared with Bunge's pine, which benefited from the traits described above. These findings further revealed the mechanism of PM2.5 uptake by plants and its relationship with PM2.5 concentration and plant traits, and provided a scientific basis for how to effectively utilize plants to reduce PM2.5 pollution and purify the environment in areas with different pollution concentrations.