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ORIGINAL RESEARCH ARTICLE
Air quality and meteorological drivers in Kathmandu Valley, Nepal: A 2021–2023 observational study
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1 Department of Management Science, Faculty of Statistics, Nepal Commerce Campus, Tribhuvan University, Kirtipur, Bagmati,
Nepal
2 Department of Science and Technology, Faculty of Statistics, Mahendra Bindeshwori Multiple Campus, Tribhuvan University, Rajbiraj, Saptari,
Nepal
3 Department of Civil Engineering, Faculty of Civil Engineering, Government Engineering College, Samastipur, Bihar,
India
EER, 025490086 https://doi.org/10.36922/EER025490086
Received: 2 December 2025 | Revised: 23 April 2026 | Accepted: 24 April 2026 | Published online: 15 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract
Air pollution in Kathmandu Valley threatens public health. Using 2021–2023 data from two urban monitoring stations (Ratna Park and Kirtipur), we analyzed associations between particulate matter (PM2.5, PM10, and total suspended particles [TSP]), temperature, and precipitation using descriptive statistics, correlation analysis, regression analysis, analysis of variance, and cluster analysis. At Ratna Park, PM2.5 concentrations were highest in winter (December–February), with a monthly mean peak of 95.8 μg/m3 in January, driven by temperature inversions and stagnant conditions. Seasonal mean PM2.5 was highest during the pre-monsoon season (60.6 μg/m3), followed by winter (52.5 μg/m3), with no statistically significant difference (p = 0.34). PM10 and TSP peaked in the pre-monsoon season (March–May) due to resuspension of dust from dry, bare surfaces and increased convective activity. PM2.5 showed strong negative correlations with minimum temperature (r = −0.57) and precipitation (r = −0.60). Meteorological factors significantly affect pollutant concentrations; however, unmeasured emission sources are likely to play an important role as well. PM10 and TSP drove poor Air Quality Index (AQI) levels during the pre-monsoon period, while PM2.5 was the primary contributor to winter AQI. Multiple linear regression at Ratna Park explained 68% of the variance in PM2.5, while ridge regression confirmed the negative influence of minimum temperature and precipitation, with a stable coefficient despite multicollinearity. The study provides evidence-based insights to support air quality enhancement, public health planning, and policy-making for Kathmandu Valley and similar topographically characterized urban regions. These findings have a direct impact on the health of over 2.5 million valley residents, mostly during winter and the pre-monsoon period.
Keywords
Air quality
Precipitation
Particulate matter
Seasonal variation
Ridge regression
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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