Happiness Under Haze: A Study on the Moderating Effect of Air Quality and Mental Well-being in New Delhi
Bibliographic record
Abstract
In New Delhi, where air quality index (AQI) is often hazardous due to vehicular traffic, industrialization, crop residue burning, and seasonal changes, has not only aggravated respiratory, cardiac, and neurological medical conditions but has also taken a toll on mental well-being such that stress, anxiety, and depression symptoms have in-creased while cognitive functioning and overall satisfaction in life have decreased.This research seeks to investigate how different levels of AQI, aver-age PM2.5 and PM10 concentration of particles for different periods, affect the various components of happiness such as emotional mental health, wellbeing, life satisfaction, and social well-being of the people in New Delhi, pollution sensitivity being the moderating factor to examine the extent to which the degradation of air quality makes others distressed especially those who are more prone to pollution.A quantitative, crosssectional survey methodology with stratified random sampling of New Delhi residents is applied in this study to describe how AQI, PM2.5, and PM10 concentrations are related to happiness by demographic attributes, with pollution sensitivity as a moderator variable, descriptive statistics, multiple regression analysis, Pearson correlation analysis, and SEM were used to measure the indirect and direct effects of air quality on happiness.The findings indicate moderate happiness levels, with significant negative correlations (p < 0.01) between happiness indicators and air pollution measures, such as AQI's strong negative correlation with Mental Health (-0.55) and Physical Health Perception (-0.57), underscoring pollution's impact on well-being.Regression analysis further supports this, showing significant negative effects of AQI on Self-Reported Happiness (β = -0.34,p = 0.001) and Mental Health (β = -0.38,p = 0.004), while moderation analysis confirms that higher pollution sensitivity amplifies the adverse effects, as shown by AQI's interaction with Self-Reported Happiness (B = -0.15,t = -3.75,p < 0.001).
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".