Assessing the Air Pollution Tolerance Index of Urban Plantation: A Case Study Conducted along High-Traffic Roadways
Bibliographic record
Abstract
Road transport and traffic congestion significantly contribute to dust pollution, which negatively impacts the growth of roadside plants in urban areas. This study aims to quantify the air pollution tolerance index (APTI) and analyze the impacts of dust deposition on different plant species and trees planted along a busy urban roadside in Lahore, Pakistan by considering seasonal variations. The APTI of each species is determined based on inputs of various biochemical parameters (leaf extract pH, ascorbic acid content, relative water content, and total chlorophyll levels), including dust deposition. In this study, laboratory analysis techniques are employed to assess these factors in selected plant species such as Mangifera indica, Saraca asoca, Cassia fistula, and Syzygium cumini. A statistical analysis is conducted to understand the pairwise correlation between various parameters and the APTI at significant and non-significant levels. Additionally, uncertainties in the inputs and APTI are addressed through a probabilistic analysis using the Monte Carlo simulation method. This study unveils seasonal variations in key parameters among selected plant species. Almost all biochemical parameters exhibit higher averages during the rainy season, followed by the summer and winter. Conversely, dust deposition on plants follows an inverse trend, with values ranging from 0.19 to 4.8 g/cm2, peaking during winter, notably in Mangifera indica. APTI values, ranging from 9.39 to 14.75, indicate varying sensitivity levels across species, from sensitive (Syzygium cumini) to intermediate tolerance (Mangifera indica). Interestingly, plants display increased tolerance during regular traffic hours, reflecting a 0.9 to 5% difference between the APTI at peak and regular traffic hours. Moreover, a significant negative correlation (−0.86 at p < 0.05 level) between APTI values and dust deposition suggests a heightened sensitivity to pollutants during the winter. These insights into the relationship between dust pollution and plant susceptibility will help decision makers in the selection of resilient plants for urban areas and improve air quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".