Effects of meteorological parameters including confounding effects of seasonality and lag effects on air pollution levels are investigated and quantified for Kathmandu valley, Nepal using daily local temporal data of 2017-2020 available through air pollution monitoring by Department of Environment and US Embassy, Kathmandu, Nepal, and meteorology monitored by Department of Hydrology and Meteorology, Kathmandu, Nepal. Data modeling are performed through Exponential, Box-Cox transformed and Gamma Generalized Linear Models since air pollution levels are found non-normal with the presence of non-constant error variance in linear modeling. Results depict high proportions of observed air pollution variations (79%-85%) explained by the fitted models with around 5% reduction in PM 10 and PM 1 levels per 1 0 C increase in average temperature and significant increase in surface O 3 level (0.177 Box-Cox transformed value) per 1 0 C increase in average temperature. Similarly, around 0.7% and 2% decrease in PM 1 and PM 10 per 1% increase in relative humidity, 0.032 decrease in transformed value of PM 2.5 per 1 mm increase in rainfall, and 7.3% decrease in PM 10 per 1 m/s increase in wind speed are also detected. Other effects are also quantified in terms of Box-Cox transformed values for statistically significant effects due to relative humidity and wind. In conclusion, meteorological conditions are found significant contributing factors in determining air pollution levels as demonstrated by statistical modeling. On the long run, atmospheric conditions can play vital roles in air pollution situation shifts mainly due to climate change characterized by changes in meteorological values.