Assessment of Air Pollution Tolerance and Particulate Matter Accumulation of 11 Woody Plant Species

Effects of meteorological factors on the retention of particulate matter in lawn grass blades

Plant leaves can reduce the concentration of atmospheric particulate matter (PM) by absorbing it in the air, and this mitigates the deleterious human health effects of PM. However, the ability of plant leaves to retain dust is limited and varies continually due to various meteorological factors such as rainfall, extreme wind speed, and PM10 concentrations. Here, we measured the ability of seven types of turfgrass with leaves similar in macromorphology but varying in micromorphology to retain dust particles of different sizes; we also analyzed the effects of various meteorological factors, such as rainfall, maximum wind speed, and PM10 concentration, on the ability of leaves to retain particles of different sizes. There were significant differences in the ability of the seven types of turfgrass to retain particles of different sizes; the dust retention capacity of Zoysia sinensis was the strongest(2.04 g·m-2), and that of Festuca elata was the weakest(1.39 g·m-2). The elution rates of PM>10 after rainfall of 3 mm and 4 mm were significantly higher than those of PM2.5-10 and PM2.5; the elution rates of PM>10, PM2.5-10, and PM2.5 increased as the amount of rainfall increased. When the amount of dust on leaves is low, wind promotes increases in leaf PM retention. When the blade retains a certain amount of dust, the maximum wind speed is greater than 9.1 m·s-1, which leads to a decrease in the dust retention of lawn grass blades. The concentrations of PM10 and PM2.5 were positively correlated with the retention of particles of different particle sizes. Therefore, evaluations of the dust retention ability of plant leaves require consideration of the effects of local rainfall, maximum wind speed, PM10 concentration, and other factors.

Assessment of air pollution tolerance potential of selected dicot tree species for urban forestry

Air pollution is one of the killers of our age especially for the urban areas. Urban forestry which involves planting more trees has been considered as one of the prominent strategies to mitigate air pollution. Identification of trees tolerant to air pollution is important for plantation drives being organized across the country. The present study aimed to compare the air pollution tolerance potential of 46 tree species growing in Guru Nanak Dev University (GNDU) campus, Amritsar, using two indices, viz., Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API). APTI is based on four biochemical parameters, viz., relative water content, leaf extract pH, total chlorophyll, and ascorbic acid contents of leaf samples, whereas API takes into consideration morphological and socioeconomic values of plant species along with their APTI. Based on APTI values calculated for 46 tree species, only 2 tree species, viz., Psidium guajava (46.26) and Cassia fistula (41.83), were found to be tolerant to air pollution, while 25 species showed intermediate tolerance. API scores revealed one tree species, namely, P. guajava, as an excellent performer, 8 species as very good performers, and 28 species as moderate to good performers against air pollution. In conclusion, tree species like Alstonia scholaris, C. fistula, Ficus tsjakela, Grevillea robusta, Kigelia africana, Mangifera indica, Melia azedarach, P. guajava, Pongamia pinnata, Pterospermum acerifolium, Putranjiva roxburghii, Syzygium cumini, Terminalia arjuna, and Toona ciliata can be considered as most desirable for plantations in areas around GNDU campus.

Evaluation of plant species for air pollution tolerance and phytoremediation potential in proximity to a coal thermal power station: implications for smart green cities

In metropolitan areas, air pollution poses a significant threat, and it is crucial to carefully select plant species that can tolerate such conditions. This requires a scientific approach based on systematic evaluation before recommending them to executive bodies. This study aimed to determine the air pollution tolerance index (APTI), dust retention capacity, and phytoremediation ability of 10 plant species growing in and around a lignite-based coal thermal power station. The results showed that Ficus benghalensis L. had the highest APTI, followed by Mimusops elengi L., Ficus religiosa L., Azadirachta indica A. Juss., and Annona reticulata L. F. benghalensis also showed the highest pH of leaf extract, relative water content, total chlorophyll, and ascorbic acid content, as well as the highest dust capturing capacity. Among the ten plant species, F. benghalensis, M. elengi, F. religiosa, A. indica and F. racemosa were identified as a tolerant group that can be used for particulate matter suppression and heavy metal stabilization in and around thermal power plants. These findings can inform the selection of plants for effective green infrastructure in smart green cities, promoting the health and well-being of urban populations. This research is relevant to urban planners, policymakers, and environmentalists interested in sustainable urban development and air pollution mitigation.

Assessment of air pollution tolerance index (APTI) and anticipated performance index (API) of selected roadside plant species for the green belt development at Ratnagiri City in the Konkan region of Maharashtra, India

Small towns are becoming hotspots of pollution due to industrial, urbanisation, and domestic activities. Air pollution affects human health and it is also responsible for physiological changes in plants. Green belt development programmes are cost-effective for the minimisation of air pollution. In the present study, to calculate air pollution tolerance index (APTI) and anticipated performance index (API), samples of 25 plant species were collected from each area i.e. the industrial (I), urban (U), and rural (R) areas and analysed for different parameters. Amongst all three areas, APTI of Artocarpus heterophyllus (46.74), Calotropis gigantea (43.63), and Bauhinia racemose (42.11) have shown the highest values and these plants can act as an inhibitor of air pollution. Also, the APTI of Ocimum tenuiflorum has found to be the lowest (12.05, 11.32, 12.86) as compared to other plant species amongst the three areas. Statistical analysis reveals that values of R² are consistent in case of total chlorophyll (TC) and ascorbic acid (AA). API index showed the efficiency of Calotropis gigantea (excellent), Artocarpus heterophyllus (very good), and Mangifera indica (very good) for the green belt development around the selected areas. It is recommended to plant above-mentioned plant species along the roadside by considering their air pollution tolerance ability and medicinal as well as economic importance. Furthermore, it is suggested to plant species of Artocarpus heterophyllus (jackfruit) and Mangifera indica (Alphonso mango) which will generate income source for the local government bodies (Ratnagiri Municipal Council), as the fruits and wood of these plants can be exported and sold.

Plant-Based Solutions for Non-Productive Sites Useful in the Management of Dry Land

The article presents an overview of research conducted in recent years, i.e., from 2004 until now. The study has been prompted by the threat of drought over large land areas which, as a result of current climate change, may lead to desertification in dry and hot regions of the world. For the same reason, large areas of farmland are affected by drought stress. At the same time, rising air temperatures result in a significant intensification of evaporation and a gradual increase in soil salinity. This applies in particular to acres of farmland, forested areas, and green areas of cities, as well as degraded land or brownfields. As the crop stability is threatened, the food base of the world's population is at risk and, additionally, in areas of industrial districts, people's health is in decline. Due to these multistress conditions for plant growth, we propose a review of the current literature which addresses the possibility of counteracting these unfavorable phenomena through the appropriate selection of plant species and, when only applicable, also through specific agroecological treatments. A selection of herbaceous and woody plants useful for cultivation on saline marginal lands was proposed.

Capture of fire smoke particles by leaves of Cunninghamia lanceolata and Schima superba, and importance of leaf characteristics

Emission of particulate matter (PM) during forest fires is a major source of air pollution and hence purification of atmospheric pollution has gained increasing importance. Trees can absorb polluting gases and fine particles by their leaves from the atmosphere and act as a sustainable air purification filter. However, the capture efficiency varies among tree species; thus exploring the ability of forest trees to capture smoke PM released during forest fires provides a basis for assessing net emissions from forest fires and the impact of smoke on forest ecosystems. In this study, the main afforestation tree species, Cunninghamia lanceolata (Lamb.) Hook, and a fire-resistant tree species, Schima superba Gardn.et Champ, in southern China were exposed to different smoke concentrations by simulating forest fire. The amount of PM per unit leaf area, absorption of nutrient element, leaf surface characteristics and antioxidant enzyme activities were determined. The main findings were: (1) The total quantity of PM captured by unit leaf area (μg·cm^-2) of C. lanceolata was 28.25 ± 1.12, 30.52 ± 3.43 and 33.14 ± 3.00 in low, intermediate and high smoke concentrations, respectively. The corresponding values for S. superba was 5.96 ± 0.56, 10.09 ± 1.13 and 12.27 ± 0.39, respectively. (2) Both species had weak absorption capacity for inorganic ions in the PM. (3) The purification of smoke PM by leaves was mainly related to leaf surface roughness, where it was higher for C. lanceolata than S. superba leaves. (4) Smoke treatment positively affected the contents of chlorophyll and soluble protein as well as increased antioxidant enzyme activities. In conclusion, the findings highlight the importance of leaf structural characteristics in capturing smoke particles and C. lanceolata is better suited for purification of atmospheric smoke particles following forest fire than S. superba.

Assessing the environmental impact of air pollution on crops by monitoring air pollution tolerance index (APTI) and anticipated performance index (API)

Air pollutants adversely affect the physiological, biochemical parameters, and productivity of the crops, but scarce and meager reports are available to know the certain impact of air pollution on crops. The aim of the present study was to assess environmental impact of air pollutants on biochemical parameters of the crops by monitoring two important indicators, i.e., Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API). These two indicators provide the sensitivity and the tolerance level of the crops towards the air pollutants. Seven different crops were selected in four different locations in the vicinity of a thermal power plant. The results depicted the maximum aerial particulate matter deposition on crop canopy (ADCC) in barley (Hordeum vulgare 2.15 mg/cm2) and wheat (Triticum aestivum 2.21 mg/cm2). The maximum APTI value was found in berseem (Trifolium alexandrinum, 9.45 and 11.44) during the first and second year of study, respectively. Results indicated that all crops were sensitive to air pollution in the selected area, but berseem (Trifolium alexandrinum) was less sensitive in comparison to other crops. API value showed that wheat (Triticum aestivum) and rice (Oryza sativa) were best-suited crops in the selected study area as compared to other crops. It has been found in the study that the API and APTI are two important indicators for the selection of crops in the severe air polluting area.

Analyzing Air Pollutant Reduction Possibilities in the City of Zagreb

This paper aims to present possible areas to plant different vegetation types near traffic jams to reduce air pollution in the capital of Croatia, the city of Zagreb. Based on main traffic road and random forest machine learning using WorldView-2 European cities data, potential areas are established. It is seen that, based on a 10 m buffer, there is a possible planting area of more than 220,000 square meters, and based on 15 m buffer, there is a possible planting area of more than 410,000 square meters. The proposed plants are Viburnum lucidum, Photinia x fraseri, Euonymus japonicus, Tilia cordata, Aesculus hippocastanum, Pinus sp., Taxus baccata, Populus alba, Quercus robur, Betula pendula, which are characteristic for urban areas in Croatia. The planting of proposed trees may result in an increase of 3–5% in the total trees in the city of Zagreb. Although similar research has been published, this paper presents novelty findings from combined machine learning methods for defining green urban areas. Additionally, this paper presents original results for this region.

Screening of Plant Species Response and Performance for Green Belt Development: Implications for Semi-Urban Ecosystem Restoration

Screened plant species with potential for green belt development can act as eco-sustainable tools for restoring the polluted ecosystem. Eight plant species from two study locations in Ado-Odo, Ota, Ogun State, Nigeria, were examined to identify their air pollution response and performance by deploying two air pollution indices, namely air pollution tolerance index (APTI) and anticipated performance index (API). APTI results identified all screened plants as sensitive species suitable as bio-indicators of air pollution, with Ficus auriculata (2.42) common to the non-industrial location being the most sensitive. API scores categorized Ficus auriculata (56.25%) as a moderate performer, while Syzygium malaccense (75%) and Mangifera indica (75%) were identified as very good performers, suitable for green belt development. The relationship between each biochemical parameter with APTI was investigated using regression analysis and two-way analysis of variance. The model result showed a significant relationship between each biochemical parameter with APTI, and relative water content had the highest influence on APTI (R2 = 0.99436). Both indices (APTI and API) are suitable for screening and recommending native plant species for cultivation in the polluted environment, thus promoting ecological restoration. Hence, Syzygium malaccense, Mangifera indica and Ficus auriculata, respectively, were recommended for green belts design. Further intensive screening to identify tolerant species and best to excellent performer’s trees suitable for restoring the ecosystem is advised.