Assessment of the Environmental Impacts of COVID-19 in Urban Areas—A Case Study of Iran
Bibliographic record
Abstract
The Severe Acute Respiratory Syndrome-Coronavirus Outbreak 2019 (COVID-19) has caused worldwide concern and has affected all aspects of human life. The study objective is to assess and evaluate the direct and indirect positive and negative environmental effects of COVID-19 in urban areas. Collected data for Iran as a case study is presented, comprehensively completing the dynamic effect of COVID-19 on the environment. The analysis results indicate that despite the temporarily positive effects of coronavirus on the environment, such as improvement in air quality (15% - 20% reduction of NO2 in Tehran), environmental noise reduction, cleaner beach and coastal areas due to implementing lockdowns, there are negative short- and long-term effects such as excessive water consumption (10% - 40% increase in Iranian cities), reduce in waste recycling and significant increase in both residential and medical solid waste generation (10% - 77% increase in medical waste generation and 10% - 50% increase residential waste generation in Iranian cities), which leads to pollution or/and degradation of the environment (air, water and land). Moreover, with the global economic relaunching relaunch in most countries in the coming months, it could result in adverse effects such as increase in the greenhouse gas emissions. Assessment of environmental impacts, type and scale, could help for better planning and mitigation of the future pandemics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".