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Comparing Different Methodologies to Quantify Particulate Matter Accumulation on Plant Leaves
Urban air pollution poses a significant threat to human health, with metropolitan areas particularly affected due to high emissions from human activities. Particulate matter (PMx) is among the most harmful pollutants to human health, being composed of a complex mixture of substances related to severe pulmonary conditions. Urban green spaces play a vital role in mitigating air pollution by capturing PMx, and it is essential to select plant species with a high capacity for PMx accumulation to effectively enhance air quality. This study aimed to evaluate and compare the accuracy of two PMx quantification methods—light microscopy and filtration—which demonstrated a high correlation (R2 = 0.72), suggesting that both methods are reliable for assessing PMx accumulation on leaves. Light microscopy allowed for the visualization of PMx deposition, revealing the species warranting further analysis using the filtration method. Among the species analyzed, Euonymus japonicus, Ligustrum lucidum, Alnus glutinosa, Rubus ulmifolius, and Laurus nobilis demonstrated the highest total PMx accumulation, exceeding 50 µg cm−2, making them particularly valuable for air pollution mitigation. This study examined the correlation between leaf traits such as specific leaf area (SLA), leaf area (LA), leaf dissection index (LDI), and leaf roundness and PMx accumulation across the 30 different plant species. A multiple linear regression analysis indicated that these leaf traits significantly influenced PMx accumulation, with SLA and LA showing negative correlations and leaf roundness exhibiting a positive correlation with PMx deposition. In conclusion, this study highlights the importance of selecting plant species with specific leaf traits for effective air quality improvement in urban environments particularly in highly polluted areas, to enhance air quality and public health.
Comparing Different Methodologies to Quantify Particulate Matter Accumulation on Plant Leaves
Urban air pollution poses a significant threat to human health, with metropolitan areas particularly affected due to high emissions from human activities. Particulate matter (PMx) is among the most harmful pollutants to human health, being composed of a complex mixture of substances related to severe pulmonary conditions. Urban green spaces play a vital role in mitigating air pollution by capturing PMx, and it is essential to select plant species with a high capacity for PMx accumulation to effectively enhance air quality. This study aimed to evaluate and compare the accuracy of two PMx quantification methods—light microscopy and filtration—which demonstrated a high correlation (R2 = 0.72), suggesting that both methods are reliable for assessing PMx accumulation on leaves. Light microscopy allowed for the visualization of PMx deposition, revealing the species warranting further analysis using the filtration method. Among the species analyzed, Euonymus japonicus, Ligustrum lucidum, Alnus glutinosa, Rubus ulmifolius, and Laurus nobilis demonstrated the highest total PMx accumulation, exceeding 50 µg cm−2, making them particularly valuable for air pollution mitigation. This study examined the correlation between leaf traits such as specific leaf area (SLA), leaf area (LA), leaf dissection index (LDI), and leaf roundness and PMx accumulation across the 30 different plant species. A multiple linear regression analysis indicated that these leaf traits significantly influenced PMx accumulation, with SLA and LA showing negative correlations and leaf roundness exhibiting a positive correlation with PMx deposition. In conclusion, this study highlights the importance of selecting plant species with specific leaf traits for effective air quality improvement in urban environments particularly in highly polluted areas, to enhance air quality and public health.
Comparing Different Methodologies to Quantify Particulate Matter Accumulation on Plant Leaves
Barbara Baesso Moura (author) / Francesco Zammarchi (author) / Yasutomo Hoshika (author) / Federico Martinelli (author) / Elena Paoletti (author) / Francesco Ferrini (author)
2024
Article (Journal)
Electronic Resource
Unknown
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