Wavelet coherence analysis of PM2.5 variability in response to meteorological changes in South Asian cities

Assessment of trends and variability of atmospheric particles can help set the target for pollution reduction, robust air-pollution and climate change policy decision-making, and clean air. This study investigated the recent trends and variability of Particulate Matter 2.5 (PM_2.5) concentrations led by meteorological factors in fifteen major South Asian cities. It comprehensively assessed the influence of climatic factors through multiple statistical and time-frequency models. Trends were identified using the Mann-Kendal Test and innovative trend analysis methods. This study found the most critical air quality condition in Lahore (105.43 μg/m³), safe condition in Ho Chi Minh City (25.97 μg/m³), and most of the cities experience unhealthy to very unhealthy air conditions during winter. PM2.5 levels exhibited ascending trends in Dhaka, Jakarta, Lahore, Karachi, and Islamabad, while descending trends in Delhi, Mumbai, Hyderabad, Kolkata, Chennai, Kathmandu, Rangoon, Hanoi, Vientiane, and Ho Chi Minh City. Statistical models such as linear regression, Pearson's correlation, and Spearman rank correlation were applied to determine the influence of temperature, rainfall, humidity, and windspeed on PM_2.5 concentrations. Despite the differences in climate conditions, PM_2.5 concentrations were most sensitive to humidity, followed by temperature, rainfall, and windspeed across most cities. Most cities showed a negative relationship between PM_2.5 and meteorological factors. Bi-wavelet coherence (WC) analyses were performed to investigate how the frequency components of PM_2.5 and meteorological factors relate to each other over time. This WC analysis showed a high correlation with phase and antiphase correlations and demonstrated that all meteorological factors have influenced PM2.5 concentrations over time.