Trends and oscillation characteristics of hourly PM2.5 levels in arid environment using wavelet coherence and lagged correlation

Understanding the spatiotemporal dynamics of PM2.5 in arid urban environments is critical for effective air quality management and public health protection. This study investigates the diurnal variability, long-term trends, and climatic interactions of hourly PM2.5 levels in Kuwait City from 2017 to 2024 using wavelet coherence, lagged correlation, and non-parametric trend analyses. Results show significant diurnal variation in atmospheric loading, with PM2.5 concentrations peaking during summer evenings at 7 PM (63.3 µg/m3 in July), greatly reducing total solar irradiance (TSI) available for energy conversion. Monthly data indicate higher atmospheric particulate levels during May, July, and August, coinciding with peak solar energy demand periods. Trend analysis using Theil–Sen slopes indicates an overall decline in PM2.5 levels (− 4.1671 µg/m3/month in September), suggesting improved conditions for solar energy collection. Cross Wavelet Transform analysis uncovered persistent relationships between PM2.5 and meteorological factors influencing solar panel performance, including temperature, humidity, and wind patterns at 100–300-day scales. Multivariate regression identified wind speed at 50 m (− 2.05, p < 0.001), rainfall (− 0.24, p < 0.001), and TSI (− 0.004, p < 0.001) as significant predictors of atmospheric clarity for renewable energy systems. Lagged correlations confirmed delayed but strong influences of meteorological variables, peaking at lags of 11–30 h. The study provides evidence of the climatic regulation of PM2.5 in arid urban settings and underscores the need for integrated, weather-informed mitigation strategies to improve air quality and reduce health risks in desert cities.