Estimation of particulate matter (PM_2.5, PM₁₀) concentration and its variation over urban sites in Bangladesh

Satellite-retrieved aerosol optical depth essentially provides an economical option for regular monitoring of particulate matter (PM) concentration; however, the constrains and challenges come in terms of estimation accuracy. In the present study, we estimated PM_2.5 and PM₁₀ (PM of aerodynamic diameter lesser than 2.5, 10 µm, respectively) for 11 sites in Bangladesh using different methods. Univariate model showed destitute performance (R² < 0.1), whereas integrating MODIS-AOD with surface meteorology, multivariate models enhanced accuracy (R² > 0.6); meanwhile, radial kernel-based ‘eps’-type support vector regression model outperformed rest (R² > 0.8). Furthermore, we investigated variations in ground concentration of PM_2.5, PM₁₀ during 2013–2018 and found annual mean concentration of 76.34 ± 34.12 µg m⁻³ and 136.25 ± 68.94 µg m⁻³, respectively. Predominant anthropogenic contribution to elevated pollution is well remarked by PM_2.5/PM₁₀ ratio, highest during January (0.65 ± 0.06) and lowest during July (0.48 ± 0.11). Grievous pollution found in Narayanganj (PM_2.5: 100.35 ± 56.76 µg m⁻³, PM₁₀: 200.25 ± 91.79 µg m⁻³) and slightest in Sylhet (PM_2.5: 56.13 ± 26.99 µg m⁻³, PM₁₀: 103.94 ± 49.37 µg m⁻³). Intra-annual pattern asserts winter as sternly befouled and least pollution during monsoon, which may indicate significant influence of meteorology on PM pollution. We found that PM divulged negative correlation with air temperature (PM_2.5: −0.78, PM₁₀: −0.73), relative humidity (PM_2.5: −0.66, PM₁₀: −0.73) and rainfall (PM_2.5: −0.59, PM₁₀: −0.61). This study showed outrageous situation of PM pollution in urban areas in Bangladesh and proposed modest pathway for regular monitoring of PM that will help to combat pollution.